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1
Zhuang, Ziyi. "The Portfolio Optimization Project." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/285.
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This project has three parts. The first part is to use the efficient frontier and find the tangency portfolio to form our optimal portfolio. We built our portfolio using the Interactive Brokers software and rebalanced every week for 4 holding periods to see the relationship between our projected returns and actual market returns. In the second part we considered the Capital Asset Pricing Model (CAPM) and ran linear regressions on the stocks we chose in the first part of the project. This process is based on our idea of finding the systematic risk in each stock to improve our stock choosing ability. In the last part we introduce the concept of factor models and add more factors into our original CAPM model. Via a back-testing method, we test the reasonability of our factors and give advice to further improve our portfolio optimization project.
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Sharma, Amita. "Optimal portfolio selection contemplating risk propensity of investors in stock markets." Thesis, IIT Delhi, 2016. http://localhost:8080/xmlui/handle/12345678/7098.
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Demarco, Raffaella Michaela. "Optimal model points in term life insurance." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18236/.
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This thesis is focused on the problem of seeking an optimal set of the model points selection when dealing with a portfolio of term insurance policies and a LIBOR Market Model that determines the dynamics of the forward rates. Specifically, the study is associated to the problem of minimizing a specific risk functional which measures the average discrepancy between two portfolios: the given portfolio of policies and the model points, a small group of representative contracts which substitute the first one, without misrepresenting its inherent risk structure. This optimization process is aimed to reducing the computation difficulties of the valuation of the performance of any portfolios of policies, projections to be made daily by life insurance companies.
In particular, in the present thesis, after a brief reference to some basic concepts in the interest rate field, there are described the LIBOR Market Model and the risk functional in a Banach space. The portfolio representation problem is also examined, because it allows to define the dynamics of those portfolios within a certain class that best represents the inherent risk structure of a given financial exposure.
Finally, it is analyzed the particular case of the term life insurance.
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Gabih, Abdelali, and Ralf Wunderlich. "Optimal portfolios with bounded shortfall risks." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200401202.
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This paper considers dynamic optimal portfolio strategies of utility maximizing
investors in the presence of risk constraints. In particular, we investigate the optimization problem with an additional constraint modeling bounded shortfall risk
measured by Value at Risk or Expected Loss. Using the Black-Scholes model of a
complete financial market and applying martingale methods we give analytic expressions for the optimal terminal wealth and the optimal portfolio strategies and
present some numerical results.
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Starck, Markus O. "Delegated investing and optimal risk budgets /." Hamburg : Kovač, 2008. http://www.verlagdrkovac.de/978-3-8300-3612-8.htm.
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Angoshtari, Bahman. "Stochastic modeling and methods for portfolio management in cointegrated markets." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:1ae9236c-4bf0-4d9b-a694-f08e1b8713c0.
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In this thesis we study the utility maximization problem for assets whose prices are cointegrated, which arises from the investment practice of convergence trading and its special forms, pairs trading and spread trading. The major theme in the first two chapters of the thesis, is to investigate the assumption of market-neutrality of the optimal convergence trading strategies, which is a ubiquitous assumption taken by practitioners and academics alike. This assumption lacks a theoretical justification and, to the best of our knowledge, the only relevant study is Liu and Timmermann (2013) which implies that the optimal convergence strategies are, in general, not market-neutral. We start by considering a minimalistic pairs-trading scenario with two cointegrated stocks and solve the Merton investment problem with power and logarithmic utilities. We pay special attention to when/if the stochastic control problem is well-posed, which is overlooked in the study done by Liu and Timmermann (2013). In particular, we show that the problem is ill-posed if and only if the agent’s risk-aversion is less than a constant which is an explicit function of the market parameters. This condition, in turn, yields the necessary and sufficient condition for well-posedness of the Merton problem for all possible values of agent’s risk-aversion. The resulting well-posedness condition is surprisingly strict and, in particular, is equivalent to assuming the optimal investment strategy in the stocks to be market-neutral. Furthermore, it is shown that the well-posedness condition is equivalent to applying Novikov’s condition to the market-price of risk, which is a ubiquitous sufficient condition for imposing absence of arbitrage. To the best of our knowledge, these are the only theoretical results for supporting the assumption of market-neutrality of convergence trading strategies. We then generalise the results to the more realistic setting of multiple cointegrated assets, assuming risk factors that effects the asset returns, and general utility functions for investor’s preference. In the process of generalising the bivariate results, we also obtained some well-posedness conditions for matrix Riccati differential equations which are, to the best of our knowledge, new. In the last chapter, we set up and justify a Merton problem that is related to spread-trading with two futures assets and assuming proportional transaction costs. The model possesses three characteristics whose combination makes it different from the existing literature on proportional transaction costs: 1) finite time horizon, 2) Multiple risky assets 3) stochastic opportunity set. We introduce the HJB equation and provide rigorous arguments showing that the corresponding value function is the viscosity solution of the HJB equation. We end the chapter by devising a numerical scheme, based on the penalty method of Forsyth and Vetzal (2002), to approximate the viscosity solution of the HJB equation.
7
David, Delphine. "Contrôle optimal stochastique avec retard, asymétrie d'information, et applications en finance et en économie." La Rochelle, 2008. http://www.theses.fr/2008LAROS249.
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Nous considérons dans la première partie de cette thèse des problèmes de contrôle optimal lorsque l'état est dirigée par une équation différentielle stochastique à retard et à sauts. Nous montrons que les théorèmes classiques de contrôle tels que l'équation d'Hamilton-Jacobi-Bellman ou le principe du maximum s'étendent à notre modèle et qu'ils permettent l'obtention de résultats explicites. Nous étudions également l'existence et l'unicité d'une solution de viscosité pour l'équation d'Hamilton-Jacobi-Bellman. La deuxième partie est composée de deux travaux indépendants ayant pour thème l'étude du comportement optimal des agents pour des modèles économique et financier bien spécifiques. Le premier travail résout le problème de consommation et investissement d'un initié par le biais des intégrales forward, le deuxième analyse un modèle à générations imbriquées en temps continu. La troisième partie de cette thèse est dédiée au calcul de sensibilité d'options et plus particulièrement au calcul de l'indicateur nommé Theta. Nous donnons une expression explicite de cet indicateur pour les options européennes et digitales et présentons des simulations numériques<br>We consider, in the first part, optimal control problems when the state is driven by a stochastic differential equation with delay and jumps. We show that standard optimal control theorems as the Hamilton-Jacobi-Bellman equation or the maximum principle can be extended in our setting and that they give explicit solutions. We also study existence and uniqueness of viscosity solution for the Hamilton-Jacobi-Bellman equation. The second part of this thesis is composed of two independent works related by the study of the optimal behavior of agents in specific economic and financial frameworks. The first work solves a consumption and investment problem for an insider by means of forward integrals, the second one analyses an overlapping generations model in continuous time. The third part of this thesis is devoted to sensitivity analysis. More precisely we compute the indicator named Theta. We obtain an explicit form of this indicator for european and digital options and give some numerical simulations
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Bjurgert, Johan, and Marcus Edstrand. "Forecasting the Equity Premium and Optimal Portfolios." Thesis, Linköping University, Department of Mathematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11795.
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<p>The expected equity premium is an important parameter in many financial models, especially within portfolio optimization. A good forecast of the future equity premium is therefore of great interest. In this thesis we seek to forecast the equity premium, use it in portfolio optimization and then give evidence on how sensitive the results are to estimation errors and how the impact of these can be minimized.</p><p>Linear prediction models are commonly used by practitioners to forecast the expected equity premium, this with mixed results. To only choose the model that performs the best in-sample for forecasting, does not take model uncertainty into account. Our approach is to still use linear prediction models, but also taking model uncertainty into consideration by applying Bayesian model averaging. The predictions are used in the optimization of a portfolio with risky assets to investigate how sensitive portfolio optimization is to estimation errors in the mean vector and covariance matrix. This is performed by using a Monte Carlo based heuristic called portfolio resampling.</p><p>The results show that the predictive ability of linear models is not substantially improved by taking model uncertainty into consideration. This could mean that the main problem with linear models is not model uncertainty, but rather too low predictive ability. However, we find that our approach gives better forecasts than just using the historical average as an estimate. Furthermore, we find some predictive ability in the the GDP, the short term spread and the volatility for the five years to come. Portfolio resampling proves to be useful when the input parameters in a portfolio optimization problem is suffering from vast uncertainty. </p>
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Li, Zejing [Verfasser], and N. [Akademischer Betreuer] Bäuerle. "Optimal Portfolios in Wishart Models and Effects of Discrete Rebalancing on Portfolio Distribution and Strategy Selection / Zejing Li. Betreuer: N. Bäuerle." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1033351482/34.
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廖智生 and Chi-sang Liu. "A study of optimal investment strategy for insurance portfolio." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31227636.
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Vacek, Vladislav. "Stochastické metody v řízení portfolia." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-73894.
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From the beginning of 20th century many studies proved randomness in price evolution of investment instruments. Therefore models respecting this randomness must be used in portfolio management. This thesis' aim is to provide basic theory regarding some of the stochastic methods and show their practical use in real situations.
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Deng, Hui, and 鄧惠. "Mean-variance optimal portfolio selection with a value-at-risk constraint." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B41897213.
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Ramarimbahoaka, Dimbinirina. "Growth optimal portfolios and real world pricing." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/2209.
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Thesis (MSc (Mathematics))--Stellenbosch University, 2008.<br>In the Benchmark Approach to Finance, it has been shown that by taking the
Growth Optimal Portfolio as numéraire, a candidate for a pricing derivatives
formula under the real world probability can be given. This result allows
us to price in an incomplete financial market model. The result comes from
two different approaches. In the first approach we use the supermartingale
property of portfolios in units of the benchmark portfolio which leads to the
fact that an equivalent measure is not needed. In the second approach the
numéraire property of the Growth Optimal Portfolio is used. The numéraire
portfolio defines an equivalent martingale measure and by change of measure
using the Radon-Nikodým derivative, a real world pricing formula is derived
which is the same as the one given by the first approach stated above.
14
Gabih, Abdelali, Matthias Richter, and Ralf Wunderlich. "Dynamic optimal portfolios benchmarking the stock market." Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200501244.
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The paper investigates dynamic optimal portfolio strategies of utility maximizing portfolio managers in the presence of risk constraints. Especially we consider
the risk, that the terminal wealth of the portfolio falls short of a certain benchmark level which is proportional to the stock price. This risk is measured by the
Expected Utility Loss. We generalize the findings our previous papers to this case.
Using the Black-Scholes model of a complete financial market and applying martingale methods, analytic expressions for the optimal terminal wealth and the optimal
portfolio strategies are given. Numerical examples illustrate the analytic results.
15
Moustaid, Elhabib. "Optimal Project Portfolio Execution : Computer Implementation of Models and Simulation Framework." Thesis, KTH, Optimeringslära och systemteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140505.
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This thesis work presents both mathematical models and a simulation approach to get more insight to the R&D Project Portfolio Execution problem. It gives special care to finding the optimal number of projects to run simultaneously in a portfolio in order to get the maximum monetary gain, and give the factors that affect the most this number. This report tries as well to give the best simulation of resources behaviour inside an R&D department, and takes a stage-gate model for the projects. The proposed mathematical model is a Non-Linear Mixed Integer Program that is hard to solve. A simplification lead to a less complicated Mixed Integer Program that is easier to solve. But in order to have an insight of the whole complexity of the problem, a simulation platform has been implemented. Thanks to its low computation cost, it allowed to have a big number of simulations and draw some conclusions about the initial question. The simulation platform also allows to see the influence of different factors on the number of projects that should be executed in parallel in R &D departments, which was hard to do using the mathematical models.<br>Detta examensarbete presenterar både matematiska modeller och en simuleringsplattform för att få mer insikt i R & D Project Portfolio Execution problem. Målet är att beräkna det optimala antalet projekt att köra samtidigt i en portfölj och hitta de faktorer som har störst påverkan på detta. Rapporten försöker också att ge den bästa simuleringen av resursersbeteende i R & D avdelningar. Den föreslagna matematiska modellen är ett icke-linjärt Mixed Integer Program som är svårt att lösa. En simplifiering leder till en mindre komplicerad Mixed Integer Program som är lättare att lösa, men för att få en inblick i hela problemets komplexitet har en simuleringsplattform implementerats. Tack vare dess låga beräkningskostnad, är det möjligt att köra ett stort antal simuleringar och dra vissa slutsatser om den inledande frågan. Användandet av en simuleringsplattform gör det också möjligt att se påverkan av olika faktorer på antalet projekt som ska köras parallellt i R & D -avdelningar, som hade varit svårt att göra med matematisk modellering.
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Liu, Binbin, and 刘彬彬. "Some topics in risk theory and optimal capital allocation problems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48199291.
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In recent years, the Markov Regime-Switching model and the class of Archimedean
copulas have been widely applied to a variety of finance-related fields. The
Markov Regime-Switching model can reflect the reality that the underlying economy
is changing over time. Archimedean copulas are one of the most popular
classes of copulas because they have closed form expressions and have great flexibility in modeling different kinds of dependencies.
In the thesis, we first consider a discrete-time risk process based on the compound
binomial model with regime-switching. Some general recursive formulas
of the expected penalty function have been obtained. The orderings of ruin probabilities are investigated. In particular, we show that if there exists a stochastic
dominance relationship between random claims at different regimes, then we can
order ruin probabilities under different initial regimes.
Regarding capital allocation problems, which are important areas in finance
and risk management, this thesis studies the problems of optimal allocation of
policy limits and deductibles when the dependence structure among risks is modeled
by an Archimedean copula. By employing the concept of arrangement
increasing and stochastic dominance, useful qualitative results of the optimal
allocations are obtained.
Then we turn our attention to a new family of risk measures satisfying a set
of proposed axioms, which includes the class of distortion risk measures with
concave distortion functions. By minimizing the new risk measures, we consider
the optimal allocation of policy limits and deductibles problems based on the
assumption that for each risk there exists an indicator random variable which
determines whether the risk occurs or not. Several sufficient conditions to order
the optimal allocations are obtained using tools in stochastic dominance theory.<br>published_or_final_version<br>Statistics and Actuarial Science<br>Doctoral<br>Doctor of Philosophy
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Mtemeri, Nyika. "A model of pension portfolios with salary and surplus process." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2931_1364203235.
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<p>Essentially this project report is a discussion of mathematical modelling in pension funds, presenting sections from Cairns, A.J.D., Blake, D., Dowd, K., Stochastic lifestyling: Optimal dynamic asset allocation for defined contribution pension plans, Journal of Economic Dynamics and Control, Volume 30, Issue 2006, Pages 843-877, with added details and background material in order to demonstrate the mathematical methods. In the investigation of the management of the investment portfolio, we only use one risky asset together with a bond and cash as other assets in a <br>continuous time framework. The particular model is very much designed according to the members&rsquo<br>preference and then the funds are invested by the fund manager in the financial market. At the end, we are going to show various simulations of these models. Our methods include stochastic control for utility maximisation among others. The optimisation problem entails the optimal <br>investment portfolio to maximise a certain power utility function. We use MATLAB and MAPLE programming languages to generate results in the form of graphs and tables</p>
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Geidt-Karrenbauer, Ulrike. "Die Optimierung des Kreditportfolios ein Modell zur optimalen Gestaltung des Kreditportfolios mithilfe aktiver Steuerungsinstrumente." Sternenfels Verl. Wiss. & Praxis, 2009. http://d-nb.info/999729942/04.
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Pasos, Jose E. "Mean-variance optimal portfolios for Lévy processes and a singular stochastic control model for capacity expansion." Thesis, London School of Economics and Political Science (University of London), 2018. http://etheses.lse.ac.uk/3771/.
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In the first part of the thesis, the problem of determining the optimal capacity expansion strategy for a firm operating within a random economic environment is studied. The underlying market uncertainty is modelled by means of a general one-dimensional positive diffusion with possible absorption at 0. The objective is to maximise a performance criterion that involves a general running payoff function and associates a cost with each capacity increase up to the first hitting time of 0, at which time the firm defaults. The resulting optimisation problem takes the form of a degenerate twodimensional singular stochastic control problem that is explicitly solved. The general results are further illustrated in the special cases in which market uncertainty is modelled by a Brownian motion with drift, a geometric Brownian motion or a square-root mean-reverting process such as the one in the CIR model. The second part of the thesis presents a study of mean-variance portfolio selection for asset prices modelled by Lévy processes under conic constraints on trading strategies. In this context, the combination of the price processes’ jumps and the trading constraints gives rise to a new qualitative behaviour of the optimal strategies. The existence and the behaviour of the optimal strategies are related to different no-arbitrage conditions that can be directly expressed in terms of the Lévy triplet. This allows for a fairly complete characterisation of mean-variance optimal portfolios under conic constraints.
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Johannesson, Ola, and Hiitti Christofer Johansson. "Optimal Project Portfolio Execution - Using analytical and simulation models with realistic project layouts and resource behavior." Thesis, KTH, Optimeringslära och systemteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-139398.
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This project aims to increase the knowledge in the area of resource allocation in R&D portfolios, a topic interesting to both the industry and academic research. The thesis investigates how to optimally allocate resources to projects in a R&D portfolio, with focus on how many projects that are optimal to run in parallel. A complex mixed-integer nonlinear programming model with a realistic stage-gate project layout and advanced resource behaviour, including project resource learning and other efficiency losses, is developed and also proven unsolvable in realistic time. A simplified model handling learning is proposed and solved using a mixed integer solver for a small portfolio. A simulation framework implementing all complexities is developed, and used to find portfolio parameters affecting the optimal number of projects to run in parallel, using a Monte Carlo method. From the simplified mathematical model optimal allocations from small portfolios are presented, and from the simulation several results from large portfolios using different resource allocation strategies are presented. From these results it is argued that there exists an optimal number of projects to run in a portfolio, and that a portfolio run with either larger or smaller number of running projects produces a lower gain. This number is however found to be highly dependent on the size and specification of the portfolio, and how resources are allocated to projects.<br>Detta masterarbete har som mål att utöka kunskapen kring resursallokering inom portföljer med utvecklingsprojekt, ett område som genererat intresse både inom industrin och inom tidigare akademiska arbeten. Rapporten undersöker hur resurser optimalt ska allokeras till projekt inom utvecklingsportföljer, med huvudsakligt fokus på hur många projekt som är optimalt att köra på samma gång. Ett ickelinjärt mixed-integer optimeringsproblem, som bygger på en realistisk stage-gate projektmodell samt avancerade approximationer av resursers beteende, ställs upp; modellen inkluderar bland annat lärotider vid allokering till nya projekt och andra effektivitetsförluster. Denna modell bevisas olösbar med realistisk beräkningskraft. En förenklad modell som tar hänsyn till lärandeeffekterna tas fram och löses för små portföljer. Ett simuleringsprogram tas också fram vilket inkluderar all komplexitet från den fullständiga modellen, och detta används för att med en Monte Carlo-metod undersöka vilka portföljparametrar som påverkar hur många projekt som optimalt ska köras parallellt. Från den förenklade matematiska modellen tas optimala resursfördelningar fram för små portföljer, och från simuleringarna presenteras data från realistiskt stora portföljer vilka har simulerats med olika resursallokeringsstrategier. Dessa resultat visar på att det finns ett optimalt antal projekt att köra parallellt i en portfölj, och att om en portfölj drivs med antingen färre eller fler projekt parallellt ger det en lägre vinst. Exakt vad detta optimala antal projekt är beror dock mycket på storleken och detaljer i portföljen, och hur resurser allokeras till projekt.
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Meira, Anna Carolina Granja. "Aplicação de modelos de tempo-contínuo para escolha de portfólio ótimo." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/49933.
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A presente dissertação expõe o ambiente em que o Problema de Merton é construído e, baseando-se na bibliografia apresentada, constrói exemplos em softwares cujas especificidades podem colaborar na clareza da resolução. O software Matlab engloba as soluções numéricas, enquanto o software Maple é responsável pela solução de equações diferenciais ordinárias e parciais de forma simbólica. Apresenta-se modificações do Problema de Merton original como exercícios para melhor esclarecer os diferentes parâmetros abordados. Na seção final é apresentada a solução de viscosidade, uma alternativa quando a função valor não apresenta características desejáveis para a análise apresentada.<br>This dissertation explicit the environment which Merton’s problem is built, according to the presented bibliography, exemples are built in softwares whose specificity might help to clarify the solution. The Matlab software embraces numeric solutions, while Maple software is appropriate to solve ordinary and parcial differential equations in symbolic form. Some modifications are presented to Merton’s Problem as exercise to improve understanding on the variations adopted. On final section, viscosity solutions are presented as an alternative solution for when the value function does not possess the desirables properties that allow the analysis on focus.
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Valeyre, Sébastien. "Modélisation fine de la matrice de covariance/corrélation des actions." Thesis, Sorbonne Paris Cité, 2019. https://tel.archives-ouvertes.fr/tel-03180258.
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Une nouvelle méthode a été mise en place pour débruiter la matrice de corrélation des rendements des actions en se basant sur une analyse par composante principale sous contrainte enexploitant les données financières. Des portefeuilles, nommés "Fundamental Maximum variance portfolios", sont construits pour capturer de manière optimale un style de risque défini par un critère financier ("Book", "Capitalization",etc.). Les vecteurs propres sous contraintes de la matrice de corrélation, qui sont des combinaisons linéaires de ces portefeuilles, sont alors étudiés. Grâce à cette méthode, plusieurs faits stylisés de la matrice ont été mis en évidence dont: i) l'augmentation des premières valeurs propres avec l'échelle de temps de 1 minute à plusieurs mois semble suivre la même loi pour toutes les valeurs propres significatives avec deux régimes; ii) une loi _universelle_ semble gouverner la composition de tous les portefeuilles "Maximum variance". Ainsi selon cette loi, les poids optimaux seraient directement proportionnels au classement selon le critère financier étudié; iii) la volatilité de la volatilité des portefeuilles "Maximum Variance_" qui ne sont pas orthogonaux, su_rait à expliquer une grande partie de la diffusion de la matrice de corrélation; iv) l'effet de levier (augmentation de la première valeur propre avec la baisse du marché) n'existe que pour le premier mode et ne se généralise pas aux autres facteurs de risque. L'effet de levier sur les beta, sensibilité des actions avec le "market mode", rend les poids du premier vecteur propre variables<br>A new methodology has been introduced to clean the correlation matrix of single stocks returns based on a constrained principal component analysis using financial data. Portfolios were introduced, namely "Fundamental Maximum Variance Portfolios", to capture in an optimal way the risks defined by financial criteria ("Book", "Capitalization", etc.). The constrained eigenvectors of the correlation matrix, which are the linear combination of these portfolios, are then analyzed. Thanks to this methodology, several stylized patterns of the matrix were identified: i) the increase of the first eigenvalue with a time scale from 1 minute to several months seems to follow the same law for all the significant eigenvalues with 2 regimes; ii) a universal law seems to govern the weights of all the "Maximum variance" portfolios, so according to that law, the optimal weights should be proportional to the ranking based on the financial studied criteria; iii) the volatility of the volatility of the "Maximum Variance" portfolios, which are not orthogonal, could be enough to explain a large part of the diffusion of the correlation matrix; iv) the leverage effect (increase of the first eigenvalue with the decline of the stock market) occurs only for the first mode and cannot be generalized for other factors of risk. The leverage effect on the beta, which is the sensitivity of stocks with the market mode, makes variable theweights of the first eigenvector
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Guéniche, Alain. "Dérivation empirique du portefeuille optimal des investisseurs informés et test du MEDAF conditionnel." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAG017.
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Les modèles d’équilibre à anticipations rationnelles (EAR) ont été considérablement développés ces 40 dernières années. Cependant, encore relativement peu d’avancées ont été réalisées quant à leurs applications empiriques, les signaux privés étant inobservables. Nous proposons une nouvelle méthodologie, fondée théoriquement, pour reconstituer ces signaux et ainsi parfaitement déduire toute l’information. Ce qui nous permet de construire le portefeuille optimal des agents informés et d’explorer ses propriétés à travers trois études. Dans un premier article, nous montrons que les ordres soumis au carnet d’ordres (l’offre) et le prix d’équilibre qui en résulte constituent une statistique suffisante pour l’ensemble d’information agrégé. Nous expliquons comment extraire l’information contenue dans ces deux données, en utilisant les volumes réalisés (connus avec délai) comme proxy pour l’offre, et construire ex post le portefeuille conditionnel à l’information privée. Nous comparons ses performances avec le portefeuille optimal des agents non-informés obtenu ex ante à partir des prix. Dans un second article, nous dérivons le portefeuille optimal des investisseurs informés en explorant une spécification différente du bruit. Constitué dans la première étude par une offre fournie de façon exogène par des noise traders, nous considérons à présent que les investisseurs informés et non-informés échangent entre eux. Ils sont initialement dotés d’une quantité aléatoire d’actifs risqués et échangent rationnellement sur le marché boursier pour se couvrir et spéculer sur leur information. Nous démontrons qu’il est alors nécessaire d’utiliser la partie des volumes relative à de l’information, déterminée à partir d’une mesure de la probabilité d’échanges informés, à la place des volumes totaux. A cause des contraintes et de la complexité de cette mesure, nous trouvons qu’utiliser les volumes totaux constitue le meilleur choix, du moins jusqu’à ce qu’une meilleure mesure soit trouvée. Enfin, dans une troisième étude, nous utilisons le portefeuille des agents informés pour tester le modèle d’évaluation des actifs financiers (MEDAF) conditionnel, à la place d’un indice boursier pondéré selon les capitalisations traditionnellement utilisé comme proxy pour le portefeuille de marché. Nous démontrons que conditionner à l’information privée permet d’estimer le vrai bêta, ainsi que la prime de risque du marché en isolant la prime de risque d’information qu’un indice boursier est incapable de distinguer<br>Rational expectation equilibrium (REE) models were considerably developed over the past 40 years. However, still relatively little has been done on their empirical applications, private signals being unobservable. We propose a new methodology, theoretically premised, to reconstitute these signals and thus perfectly infer all the information. This allows us to build the optimal informed investors’ portfolio and explore its properties through three studies. In the first paper, we show, based on a REE model, that the orders entered into the order book (supply) and the resulting equilibrium price constitute a sufficient statistic for the aggregate information set. We explain how to extract the information contained in these two data, using realized volumes (known with delay) as proxy for the supply, and to construct ex post the portfolio conditional on private information. We compare its performance with the optimal uninformed agents’ portfolio obtained ex ante from prices. In a second paper, we derive the optimal informed investors’ portfolio by investigating a different specification for the noise. Constituted in the first study by a supply exogenously provided by noise traders, we now consider that informed and uninformed investors trade amongst themselves. They are initially endowed with a random quantity of risky assets and have both risk-sharing and informational motives to trade rationally on the stock market. We demonstrate that we must use information-related volumes, determined with a measure of the probability of informed trades, instead of total volumes. Due to the constraints and complexity of this measure, we found that using total volumes constitutes the best choice, at least until a better measure is found. Finally, in a third study, we use the informed agents’ portfolio to test the conditional capital asset pricing model (CAPM), instead of a value-weighted stock index traditionally used as proxy for the market portfolio. We show that conditioning on private information allows estimating the real beta, as well as the market risk premium by isolating the information risk premium that an index is unable to distinguish
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ALMEIDA, Jonatas Araujo de. "Modelo Multicritério para Seleção de Portfólio de Projetos de Sistemas de Informação." Universidade Federal de Pernambuco, 2012. https://repositorio.ufpe.br/handle/123456789/18961.
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Previous issue date: 2012-06-12<br>Este trabalho apresenta uma metodologia para a seleção de portfólios de sistemas de
informação que integra a visão estratégica da organização ao planejamento de SI. O método
multicritério PROMETHEE V possui uma abordagem voltada para seleção de portfólios,
porém possui problemas devido às suas transformações de escala, distorcendo o resultado. Foi
testado então um modelo que utiliza o conceito de portfólios c-ótimos, para eliminar tais
distorções devido a mudanças de escalas. O modelo baseado no PROMETHEE V com
conceitos de portfólios c-ótimos foi aplicado a um problema realístico, sendo realizada
também uma análise de robustez sobre o resultado. Foram verificadas, porém, distorções
oriundas do próprio método PROMETHEE V. Uma análise aprofundada do método mostrou
uma fonte destas distorções, oferecendo uma prova matemática da inadequação do
PROMETHEE V. Um novo modelo foi proposto, como alternativa que utiliza a racionalidade
não-compensatória do PROMETHEE sem apresentar as distorções verificadas no
PROMETHEE V, para encontrar a solução do problema. O novo modelo aplica o método
PROMETHEE II sobre os portfólios ao invés de projetos, para isto foi utilizado um
procedimento de geração de portfólios de fronteira descrito na literatura, realizando sobre este
procedimento uma adaptação que aumenta a sua eficiência. O novo modelo proposto foi
aplicado a problemas simulados, sendo feita uma comparação que mostra que sua
recomendação supera e podendo inclusive dominar a recomendação do modelo com
PROMETHEE V.<br>This work presents a methodology for selection of information system portfolios that
integrates the strategic view of the organization to the IS planning. The multi-criteria method
PROMETHEE V has an aproach that aims the portfolio selection, but it has problems due to
its changes in scale that distorts the result. A model that uses the concept of c-optimo
portfolios has been tested then, in order to eliminate these distortions caused by changes in
scale. The PROMETHEE V with the c-optimo portfolio concept has been used in a realistic
problem, an analysis of robustness has also been done. However, distortions from the
PROMETHEE V method have been verified. A deep analysis of the method has showed a
source of these distortions, offering a mathematical proof of the PROMETHEE V inadequacy.
A new model has been proposed as an alternative that uses the non compensatory rationality
of the PROMETHEE, without presenting the distortions verified on the PROMETHEE V, in
order to find solutions to the problem. The new model applies the PROMETHEE II methodon
the portfolios instead on the projects. With this aim, was used a boundary portfolio generation
procedure, described in literature, changing this procedure to increase its efficiency. The new
model proposed was applied to simulated problems, and in a comparison of results, its
recommendation was better and even dominates the PROMETHEE V recommendation.
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Liu, Jingshu. "Essays on risk and uncertainty in financial decision making: Bayesian inference of multi-factor affine term structure models and dynamic optimal portfolio choices for robust preferences." Thesis, Boston University, 2014. https://hdl.handle.net/2144/11120.
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Thesis (Ph.D.)--Boston University<br>This thesis studies model inference about risk and decision making under model uncertainty in two specific settings. The first part of the thesis develops a Bayesian Markov Chain Monte Carlo (MCMC) estimation method for multi-factor affine term structure models. Affine term structure models are popular because they provide closed-form solutions for the valuation of fixed income securities. Efficient estimation methods for parameters of these models, however, are not readily available. The MCMC algorithms developed provide more accurate estimates, compared with alternative estimation methods. The superior performance of the MCMC algorithms is first documented in a simulation study. Convergence of the algorithm used to sample posterior distributions is documented in numerical experiments. The Bayesian MCMC methodology is then applied to yield data. The in-sample pricing errors obtained are significantly smaller than those of alternative methods. A Bayesian forecast analysis documents the significant superior predictive power of the MCMC approach. Finally, Bayesian model selection criteria are discussed. Incorporating aspects of model uncertainty for the optimal allocation of risk has become an important topic in finance. The second part of the thesis considers an optimal dynamic portfolio choice problem for an ambiguity-averse investor. It introduces new preferences that allow the separation of risk and ambiguity aversion. The novel representation is based on generalized divergence measures that capture richer forms of model uncertainty than traditional relative entropy measures. The novel preferences are shown to have a homothetic stochastic differential utility representation. Based on this representation, optimal portfolio policies are derived using numerical schemes for forward-backward stochastic differential equations. The optimal portfolio policy is shown to contain new hedging motives induced by the investor's attitude toward model uncertainty. Ambiguity concerns introduce additional horizon effects, boost effective risk aversion, and overall reduce optimal investment in risky assets. These findings have important implications for the design of optimal portfolios in the presence of model uncertainty.
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El, Khalloufi Hamza. "Liquidité de Marché : de l'interaction avec la politique monétaire à l'impact sur l'allocation optimale de portefeuille." Thesis, Paris 1, 2020. http://www.theses.fr/2020PA01E039.
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L'objet de ce travail est de comprendre, d'une part, les interactions entre la liquidité du marché des actions et la politique monétaire et, d'autre part, d'étudier l'impact de la liquidité de marché sur l'allocation optimale du portefeuille. Dans le premier chapitre, nous examinons les interrelations entre la liquidité du marché des actions et la politique monétaire. Nos résultats montrent que cette dernière n'a pas d'impact sur la liquidité du marché pendant toute la période. Cette dernière influence significativement l'incertitude de la politique monétaire. En outre, nous avons constaté que la politique monétaire exerce un effet asymétrique sur la liquidité du marché. Dans le deuxième chapitre, nous étudions l'impact de la liquidité de marché sur l'allocation de portefeuille. L'investisseur cherche à maximiser dynamiquement son espérance d'utilité sous la contrainte du rendement instantané du portefeuille. Nous déterminons l'allocation et la consommation optimales du portefeuille. Les résultats empiriques montrent que la liquidité du marché affecte significativement l'allocation et la consommation optimales. Dans le dernier chapitre, nous étudions comment la présence simultanément d'actifs liquides et imparfaitement liquides peut influencer l'allocation optimale du portefeuille. Ainsi, nous utilisons la méthode d'absence d'opportunités d'arbitrage par les martingales pour résoudre le programme d'optimisation dynamique. Nous obtenons une solution analytique pour les demandes. L'investisseur sous-investira ou surinvestira dans les deux actifs, par rapport au modèle de Merton, en fonction de son aversion au risque et du niveau de la liquidité du marché<br>The purpose of this work is to understand the interactions between equity market liquidity and monetary policy on the one band, and to study the impact of market liquidity on optimal portfolio allocation on the other. In the first chapter, we examine the interactions between equity market liquidity and monetary policy. Our results show that the latter has no impact on market liquidity throughout the period. The latter significantly influences monetary policy uncertainty. Furthermore, we find that monetary policy has an asymmetric effect on market liquidity. ln the second chapter, we study the impact of market liquidity on portfolio allocation. The investor seeks to dynamically maximize bis expected utility under the constraint of the portfolio's instantaneous return. We determine the optimal allocation and consumption of the portfolio. The empirical results show that market liquidity significantly affects optimal allocation and consumption. ln the last chapter, we study how the simultaneous presence of liquid and imperfectly liquid assets can influence optimal portfolio allocation. Thus, we use the martingale method under the no arbitrage opportunities approach to solve the dynamic optimization program. We obtain an analytical solution for the demands. The investor will underinvest or overinvest in both assets, compared to the Merton model, depending on his risk aversion and the level of market liquidity
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Huang, Chien-Hsun, and 黃建勳. "Using Backward-type Portfolio Selection Methods to Construct Optimal Portfolio Evaluated Index and Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/74797563727817301130.
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碩士<br>國立清華大學<br>工業工程與工程管理學系<br>92<br>Portfolio selection methods are developed in many fields. Many techniques and mathematical models are used to settle related problems based on mean-variance model developed in the stock markets. Many researches focus on evaluating items and formulate portfolio from good items and the methods belong to forward-type. On the contrary, this study aims to use “backward-type” portfolio selection method.
In the perspective of backward-type selection, this thesis identifies the portfolio attributes into three categories such as independent, interrelated and synergistic portfolio attributes. Other than the mean-variance model considers the risk as the selected criteria. The thesis used the performance (i.e. future return) what the investor emphasized as the target. By the statistic of partial R squares from stepwise-regression method toward performance, the investors’ attitude (i.e. relative importance) of each attribute is obtained periodically and the evaluation index is constructed. Based on the index, the study then constructed multi-criteria mixed-integer quadratic programming model and quadratic programming by different definition of synergistic attributes to obtain invested position of stocks in the portfolio. Finally, This study will have illustrations in Taiwan Stock market and find that the backward-type selection methods, company profitability and synergistic attribute including in the model will have good performance.
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Wei, Yung-Pin, and 魏永賓. "A Study on the Optimal Loan Portfolio Model in Financial Institution." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/59bf6a.
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碩士<br>銘傳大學<br>管理科學研究所在職專班<br>92<br>This thesis attempts to establish and to utilize a simple mathematic model to investigate the relationship between the ratios of corporate and personal loans and the probability of financial institutions successfully retrieve their credit-outstanding back. By means of analyzing the weights of different loans position, which financial institutions achieve the optimal expected rate of return, we can find out the optimal allocated ratios of bank assets. Assuming there are only two results in financial institutes when loans claims occur, completely or fail to discharge the responsibilities of liabilities, accompanying with the factors such as the ratios of corporate loans, individual loan and guarantee ratio, I explore the impact of those parameters which influence the net revenue. Accordingly, I also try to find out the relationship between the probabilities of retrieving and the ratios of assets allocation, thus to propose some references for loan decision-making in financial institutions and the methods of evaluation and governance in financial regulatory units.
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CHOU, JUNG-FA, and 周榮發. "Optimal Portfolio Model Construction and Implementation Decision System for Cloud Services." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/fng38w.
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碩士<br>銘傳大學<br>資訊管理學系碩士在職專班<br>104<br>Regarding to international financial markets, interest rates keep declined. To select the appropriate finance tools can be able to achieve the goal of risk-sharing, Besides, portfolio performance evaluation by means of financial indicators as a criterion. Popularization of higher mobile phones, the strength of cloud services is making users can calculate and process big data. In addition, it helps to reduce the cost of hardware and software maintenance.
In this study, we use Android 5.0 as developing environment, Microsoft's NET Web API deliver data, and combine Optimal Portfolio Model Construction, Microsoft Azure website, optimization software Lindo API into mobile phones to provide a cloud service and construct solving mechanism. Users get the optimal portfolio by visual interface and decision mechanism. The system functions of sensitivity analysis are with rich tables and charts show the parameter change and the optimal solution variations. This study will provide the optimal investment decisions for investors in financial markets.
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Mapasa, Mzingisi Peace. "Forecasting exchange rates using an optimal portfolio model with time varying weights." Thesis, 2017. http://hdl.handle.net/10539/23426.
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Masters of Management in Finance and Investments.
Witwatersrand Business School
Faculty of Commerce, Law and Management
Johannesburg<br>This paper presents a mean variance based model of exchange rate determination and forecasting using the return differential of an optimal portfolio composed of money, bond, and stock market returns. We use the simple OLS estimation technique for the estimation and a recursive rolling regression technique to generate the out-of-sample forecasts. We employ an autoregressive technique to estimate the mean returns and time varying variance covariance matrices to generate time varying portfolio return weights. The out-of-sample forecast analysis, using the CW statistic suggests that our Optimized Uncovered Rate of Return Parity model outperforms the naïve random walk model in forecasting one month ahead nominal exchange rates for all the countries in the study. The results also show that the un-optimized model is also able to outperform the naïve random walk in all the countries at one month ahead forecasting horizon. These findings imply that the inclusion of the three market variables in modelling exchange rates improves the forecasting ability of exchange rate models.<br>MT2017
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Lin, Hsiao-chi, and 林曉祺. "Optimal Portfolio Selection with Spectral Risk Measure under AR(1)-Copula Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/2j39e2.
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碩士<br>國立高雄大學<br>統計學研究所<br>101<br>In this article, a portfolio selection problem with spectral risk measure is considered. The dynamics of the returns of each underlying asset is modeled by an autoregressive model of order 1. The tail dependence structure of the underlying asset-return vector is depicted by a copula function. The technique of linear programming is employed to solve the optimal asset allocation. Empirical studies are conducted for investigating the impact of the degree of risk aversion, the level of autocorrelation and the tail dependence for underlying assets on the portfolio selection problem based on the component stocks of the Taiwan 50 Index. Numerical results indicate that less risk aversion investors have higher income during a period of economic prosperity while conservative investments have less losses during a recession. However, these phenomena are unapparent if the tail dependence for underlying assets is large. In addition, a less risk aversion investment strategy receives higher earnings in an economic recovery if underlying returns are negatively autocorrelated.
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Wang, Shih-Bin, and 王士賓. "Model Construction and Implementation of Optimal Portfolio Decision System for Mutual Funds." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/47557526109236768664.
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碩士<br>銘傳大學<br>資訊管理學系碩士班<br>98<br>Many kinds of assets increase in financial market, and different assets have different rewards and risks. Investors pay close attention to the issues about how to invest and how much weight should they set. Due to the burst of real estate bubbles in United States in 2006, FED adopted the strategy of reducing interest to promote the real estates’ market. However, this strategy led to a chain of serious financial crisis and subprime mortgage which swept through the world. Investment market in Taiwan was also facing the same impact.
Under the environment with different investments, we constructed three optimal portfolio-decision mathematical models with the different needs and preferences of investors (1) portfolio-decision of risk minimization model (2) portfolio-decision of return maximization model (3) portfolio-decision of benefit optimization model. Using empirical data to find the optimal portfolio and sensitivity analysis were presented. According to the mathematical models, then we constructed a system for investors. Finally, the results provided investors to establish investment-portfolio and gave different points of view for more accurate strategy-making.
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Cheng, Yi-Wen, and 鄭憶雯. "Optimal Portfolio Model Construction and Implementation Decision System for Broken Lot Investment." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/88478044655793336581.
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碩士<br>銘傳大學<br>資訊管理學系碩士班<br>100<br>Since new investment tools are continuously developed in the financial markets, how to choose the right investment tools and use financial indicators to estimate the performance of portfolio is important for investors. This study is to explore that the investors use financial indicators and then invest under limited funds in order to examine company future and development. This study is also to construct an optimal portfolio mathematical model in stock market and get the maximum return from portfolio under the criteria of selecting investing indicators.
This empirical study uses 50 leading stocks except finance and optoelectronics industry in Taiwan to find out the optimal investment portfolio of broken lot investment and to implement sensitivity analysis.
In this study, we use Microsoft Visual Studio 2008 as development platform and combine C#, optimal portfolio mathematical model for stock investment, optimization software Lindo API, ZedGraph drawing tools and database access in XML to construct broken lot investment decision system. Users get the optimal portfolio and implement the sensitivity analysis by visual interface and decision making mechanism. The system functions of sensitivity analysis with rich tables and charts show the parameter change and the optimal solution variations. This study will provide the optimal investment decisions for investors in financial markets.
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Ho, Chia-Hao, and 何佳豪. "Application of Optimal Dynamic Allocation Model in Portfolio-Evidence on Taiwan-50 Stock Index." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08654151839979810392.
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碩士<br>淡江大學<br>財務金融學系碩士班<br>99<br>This paper use the Mean-Variance Model of Markowitz(1952) to discuss the allocation of the optimal assets. We use the component stocks of Taiwan 50 Index for the underlying of the portfolio. Through the dynamic moving window estimation, we use the weekly return from 2008/9/5 to 2009/8/28 for the first sample data. Estimate the portfolio’s mean, variance and the covariance of the next period of different kinds of model by dynamic method, then using the Mean-Variance Model to derive the optimal weight of the portfolio. We use this weights to forecast the allocation of the optimal assets from 2008/9/4 to 2010/8/27. This paper found that through the estimation of the different performance index, compare to the Mean-Variance Model, DCC-GARCH model is not only more accurate to reflect the market trend but also has better forecast ability and investment performance.
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Shaw-Yih, Wu, and 吳紹逸. "An optimal technology portfolio adoption model considering capacity planning under demand and technology uncertainty." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/659znn.
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碩士<br>國立臺灣科技大學<br>工業管理系<br>104<br>One of the most challenging issues for industry is how to tackle technology adoption and capacity planning simultaneously under uncertainty. In this research, a technology portfolio adoption model considering capacity planning under demand and technological uncertainty is proposed. The model optimizes technology portfolio and simultaneously addresses the optimal capacity planning to maximize the profit in a planning horizon. The problem is modeled by Markov decision process (MDP), of which each action is presented as a desired length of time to retain the currently used technologies, in which the capacity planning problem is modeled by a stochastic mixed linear integer programming (SMLIP) problem. For achieving an efficient solution approach, a parallel sampling-based differential evolution (PSDE) algorithm is employed to solve the SMLIP problem. After that, an optimality backward recursive function is proposed to solve the MDP problem. Further, a parallel computing technique is utilized to relax the computational burden of the MDP model. In the experiment, a sensitive analysis is conducted to investigate effects of the algorithm parameters by using Taguchi method. Furthermore, a performance comparison among PSDE and other popular algorithms is conducted. Finally, we evaluate the impact of different levels of demand variance and risk of investment on the expected profit.
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Chan, Kai-Hsiang, and 詹凱翔. "Applying Mean-Variance Model and Genetic Algorithms to Construct Optimal Weights of Portfolio of Funds." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/53089080199407359615.
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碩士<br>國立成功大學<br>財務金融研究所<br>96<br>This study applies Mean-Variance Model proposed by Markowitz and Genetic Algorithm developed from artificial intelligence to construct optimal weighted simulated fund portfolios. We also compare the performance of simulated fund portfolios with MSCI All Country World Index, S&P 500, and equally weighted fund portfolios. We adopt Franklin Investments as our target funds which are categorized into five regions: Global market, European market, Emerging market, American market and Single country market. The period of this study starts from January 1999 to December 2007.
Markowitz’s Mean-Variance Model is still a famous investment theory for asset allocation until now. Normal distribution is the main assumption of Mean-Variance Model and if the distribution is not normal, then, the optimal solution can not be achieved by using Mean-Variance Model. Genetic Algorithm does not require the assumption of normal distribution. But most of our chosen funds follow normal distribution. The main purpose of this thesis is to investigate whether Genetic Algorithm can perform better than Mean-Variance Model or not in this thesis.
Our results are as follows. First, the performance of Mean-Variance Model and Genetic Algorithms based on maximizing return for a given risk is better than that based on minimizing risk for a given return. Second, Mean-Variance Model and Genetic Algorithms can outperform MSCI All Country World Index, S&P 500, and equally weighted fund portfolios based on Jensen’s measure and Sharpe’s measure under return-maximizing procedure. Third, Genetic Algorithms can only outperform Mean-Variance Model based on Jensen’s measure and Treynor’s measure under risk-minimizing procedure. Finally, the performance of Mean-Variance Model has better persistence than that of Genetic Algorithms in the future period.
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Guo, Yi-Shiuan, and 郭懿萱. "Development of an Optimal Model on the Portfolio and Fund Allocation for Taiwan 50 Index." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/38503236055546866975.
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碩士<br>國立臺中技術學院<br>資訊科技與應用研究所<br>96<br>There are more and more multiplicative channels and targets for the investment in the modern society. The derivative financial commodities, such as stocks, funds, and bonds, are weeding out the old and bringing forth the new constantly. It makes investors have more channels to invest, but it also makes it more difficult to estimate the factors of the risk caused by stock price, interest rate and ratio of risks. If the investors can exactly understand the implying profit and risk of the commodity, they would gain the highest profit.
To solve investors' problems, this paper proposes a method which combined hierarchical clustering and genetic algorithm for selecting the best investment portfolio and capital allocation in Taiwan Stock Market.
We use hierarchical clustering to divide blue chips selected in every sub-period of Taiwan stock market into several groups by their features, and select the target of investment portfolio from each group, and use genetic algorithm to allocate the ratio of invested capital. The empirical results indicate that the profit in this paper we found is significantly better than blue chips in Taiwan stock market.
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JIAN, JING-LUN, and 簡敬倫. "A study on Prediction Model of Dynamic Grey Rough Set and its Application for Optimal Stock Portfolio." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/86134633170047597851.
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碩士<br>嶺東技術學院<br>財務金融研究所<br>93<br>ABSTRACT
The main purpose of our study is to establish a trend filtering system, which combines Rough Set and Grey Theory to form the Trend Grey-Sets (Its abbreviation is TG-Rough Sets). This model is used to let the time-serial, season-serial or regular data have the dynamic trend concepts by Grey prediction, then select the data sets with trend value through rough set screening system. It mainly is applied for a portfolio prediction in the stock market. Our study first predicts each listed company’s attributes of condition and decision-making by Grey Prediction, secondly groups their attributes by K-means grouping tools, then filters and categorizes the groups with the classified capacity of Rough Set for uncertain and non-sufficient information and selects the blue-chip portfolio. And then we evaluate the company shares from the portfolio according to their past EPS and ROE and elect the better ones again. Finally, the selected companies are arranged in order with Grey Relation and determine the weight of each share in the portfolio according to it.
In selecting the attributes for the Rough Sets, we regard investment master Buffet’s rules for company evaluating and investment as foundation for long and medium-term investment target selecting. Under Buffet’s rules, mine shares (the share with false market value by false accounting) may be left out and the sustainable company share that may create a high rate of return for the investors may be picked up.
The implementation results: in Taiwan, the annual rates of return of the investment following this model were 15.11% and 100.69% respectively during the 2000-2001 bear market. During five years (2000-2004), the average annual rate of return was 38.1%; the accumulated rate of return for 20 quarter was 364.49%; the average quarter rate was 18.22%; the accumulated annual average rate of 5-year was 72.49%. In U.S., the annual rates of return of the investment following this model were 11.4% and 10.31% respectively during the 2000-2001 bear market. From 1998 to the third quarter of 2004, the average annual rate of return was 8.77%; the accumulated rate of 27 quarter was 238.88%; the average quarter rate was 8.84%; the accumulated annual average rate of 7-year was 35.38%. The portfolio determined by the model overran the market dramatically.
Key words: Grey Theory, Grey Prediction, K-means, Rough Set.
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Urban, Matěj. "Optimal Investment Portfolio with Respect to the Term Structure of the Risk-Return Tradeoff." Master's thesis, 2011. http://www.nusl.cz/ntk/nusl-298040.
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My thesis will focus on optimal investment decisions, especially those that are planned for longer investment horizon. I will review the literature, showing that changes in investment opportunities can alter the risk-return tradeoff over time and that asset return predictability has an important effect on the variance and correlation structure of returns on bonds, stocks and T bills across investment horizons. The main attention will be given to pension funds, which are institutional investors with relatively long investment horizon. I will find the term structure of risk-return tradeoff in the empirical part of this paper. Later on I will add some variables into the model and investigate whether it can improve the results. Finally the optimal investment strategies will be constructed for various levels of risk tolerance and the results will be compared with strategies of Czech pension funds. I am going to use data from Thomson Reuters Datastream, Wharton Research Data Services and additionally from some other sources.
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Pereira, Diogo Alexandre. "Portfolio optimization of stochastic volatility models through the dynamic programming equations." Master's thesis, 2018. http://hdl.handle.net/10362/58449.
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In this work we study the problem of portfolio optimization in markets with stochastic volatility.The optimization criteria considered consists in the maximization of the utility of terminal wealth.The most usual method to solve this type of problem passes by the solution of an equation with partial derivatives,deterministic and nonlinear, named the Hamilton-Jacobi-Bellman equation (HJB) or the dynamic programming equation. One of the biggest challenges consists in verifying that the solution to the HJB equation coincides with the payoof the optimal portfolio.These results are known as verication theorems.In this sense,we follow the approach by Kraft[13],generalizing the verication theorems for more general utility functions.
The most significant contribution of this work consists in the resolution of the optimal portfolio problem for the 2-hypergeometric stochastic volatility model considering power utilities. Specifically we obtain a Feynman-Kac formula for the solution of the HJ Bequation.Based on this stochastic representation weapply the Monte Carlo method to approximate the solution to the HJB equation,which if it sufifciently regular it coincides with the payoff function of the optimal portfolio.
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CHI, LUNG-YU, and 紀隆裕. "A Study on Prediction Model of Dynamic Generalized Variable Precision Rough Sets and its Application for Optimal Stock Portfolio." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/49262590252205581031.
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碩士<br>嶺東科技大學<br>財務金融研究所<br>94<br>As we know, “Don’t put all the eggs in a basket” is a popular and old proverb in stock market. What it stresses is the importance of portfolio diversification. Along with the development of modern portfolio theories and the advancement of IT, investors can conduct investment analysis by quantity method and construct strategic or dynamic portfolio in their investment practice. Therefore, numerous analysis tools and methods appear continuously with such practice. And from them, it should be some results and experiences were generated. Why not collect and organize those results and experiences in stock market in a whole to develop a set of investment strategic tool? With combining this and an integral financial database accumulated for a long term, we can form an investment strategic testing platform further.
The main purpose of creation and improvement of each model is to let it fit the real world and benefit the public. However, whether a newly built model is appropriate or not? The key factor affecting if a prediction tool is used properly or not is the matching level of data attributes and the prediction tool. Dynamic generalized varied precision rough set model (DGVPRS-Model) combines features of models of generalized rough set, varied precision rough set, neuro fuzzy and grey systems theory, and uses K-means clustering tool and ordering tool are used properly. Although successes of some individual theories for company evaluation have been attained and the feasibility of the model has been proved, we still need to conduct various tests, discussions and improvements for the validity and objectivity of our study results based on a spirit of continuous proving.
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TSENG, KUO-TUNG, and 曾國棟. "A Preliminary Study of Facebook Optimal Advertising Release Portfolio Model -A Case Study of Taiwan Native Hirami Lemon Juice." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/x9h4w7.
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碩士<br>東吳大學<br>企業管理學系<br>104<br>According to the announcement of "Taiwan's top 100 website ranking" at the end of February 2015, Facebook beat Youtube,Yahoo, Google and other well-known virtual community.Facebook become Taiwan's most popular sites, the number of us-ers has reached as many as 15 million people. With the increase of Facebook users advertising serving in the Facebook has become a new trend.Explore Facebook the optimal advertising release model is the main purpose of this study.
This study use"Taiwan native hirami lemon juice" as an example, in the man-ner of action research, trend graphs and correlation analysis ,and use click-through rate links, interaction rate and purchase rate as indicators.By setting different adver-tising release, analysis the benefits of each advertising serving models.
The main results are as follows: First, different marketing portfolio brings dif-ferent adversting release effectiveness; Second, the best advertising serving effect is set to interactive posts, charging mode is set to CPM for the optimal advertising re-lease model setting ; Third, advertising posts content recommendation of famous people brings better advertising effectiveness; Fourth, the purchase rate and click-through rate links have more correlation.In final,this study provides manage-ment practices and suggestions for future studies.
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Lee, Yuan-ting, and 李遠婷. "Performance Evaluation for Fund of Funds based on Mean-Variance Model and Genetic Algorithms to Construct Optimal Weights of Portfolio of Funds." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/56098967173236704641.
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碩士<br>國立成功大學<br>財務金融研究所<br>97<br>ABSTRACT
The study applies the Mean-Variance Model (MV) proposed by Markowitz and the Genetic Algorithms (GA) developed from artificial intelligence to construct optimal-weighted simulated fund portfolios. The main difference between this thesis and past studies is that we do not invest all portfolios; instead, we only choose the past top performance portfolios including three percent, five percent, ten percent, fifteen percent and twenty percent of all portfolio which is more rational, since investors might only want to choose past top percent of portfolio performing better due to expectation of continuing higher performance. To be more objective to evidence top performance portfolios have better performance, the study chooses the worse performance portfolios including three percent, five percent, ten percent, fifteen percent and twenty percent of all portfolio to examine whether there exists performance difference between portfolio invested based on past top and worse performance portfolios. The study also compares the performance of MV and GA with that of MSCI, S&P500 and Equally Weighted portfolio. Since there is the limitation required from the Markowitz model such as normal distribution of target asset, most prior researches demonstrate that the Genetic Algorithms outperforms the Mean-Variance Model. We proceed to examine whether the Genetic Algorithms can perform better than the Mean-Variance Model by examining funds portfolio based on 360 Franklin and 288 Fidelity funds portfolio.
There are several findings. First, the performance of the Mean-Variance Model and the Genetic Algorithms under maximizing return for a given risk is better than that under minimizing risk for a given return. Second, in most situations, the Mean-Variance Model outperforms the Genetic Algorithms. Finally, the result presents that the Mean-Variance Model and the Genetic Algorithms can improve performance persistence by increasing portfolios. Nevertheless, the phenomenon of performance persistence becomes better when the size of portfolio ranges in twenty percent.
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"Optimal portfolio allocation under behavioral framework." 2008. http://library.cuhk.edu.hk/record=b5896840.
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Kam, Kwok Hung.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.<br>Includes bibliographical references (leaves 100-103).<br>Abstracts in English and Chinese.<br>Abstract Page --- p.11<br>Abstract (Chinese) --- p.12<br>Acknowledgment Page --- p.13<br>Table of Contents --- p.1<br>Table of Figures --- p.1<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Background --- p.1<br>Chapter 1.2 --- Utility and Value Function --- p.5<br>Chapter 1.2.1 --- Expected utility theory --- p.5<br>Chapter 1.2.2 --- Prospect Theory --- p.9<br>Chapter 1.3 --- Mental Accounting --- p.14<br>Chapter 1.3.1 --- Segregation vs Aggregation --- p.17<br>Chapter 2 --- Moving reference point with loss aversion --- p.21<br>Chapter 2.1 --- Model Setup --- p.21<br>Chapter 2.2 --- Simulation Results --- p.27<br>Chapter 3 --- Constant Rebalancing Portfolio with Additive Utility --- p.30<br>Chapter 3.1 --- Model setting --- p.31<br>Chapter 3.1.1 --- Additive Utility Theory (AUT) --- p.33<br>Chapter 3.2 --- Analysis --- p.34<br>Chapter 3.3 --- Results --- p.35<br>Chapter 3.4 --- Summary --- p.38<br>Chapter 4 --- Revision of Gomes´ة Work --- p.40<br>Chapter 4.1 --- Background --- p.40<br>Chapter 4.2 --- Portfolio Allocation with zero surplus wealth --- p.44<br>Chapter 4.3 --- Portfolio Allocation with Negative Surplus --- p.46<br>Chapter 4.4 --- Portfolio Allocation with Positive Surplus --- p.50<br>Chapter 4.5 --- Numerical Results --- p.51<br>Chapter 4.5.1 --- Gomes´ة Work --- p.56<br>Chapter 4.6 --- Summary --- p.57<br>Chapter 5 --- Mental Accounting under Value Function in the Prospect Theory --- p.59<br>Chapter 5.1 --- Cognitive dissonance --- p.59<br>Chapter 5.2 --- Market Setting --- p.60<br>Chapter 5.3 --- Single Mental Account --- p.61<br>Chapter 5.4 --- Two Mental Accounts --- p.63<br>Chapter 5.5 --- Numerical results --- p.67<br>Chapter 5.5.1 --- Pessimistic View --- p.71<br>Chapter 5.6 --- Summary --- p.72<br>Chapter 6 --- Mental Accounting under Friedman-Savage Value Function --- p.74<br>Chapter 6.1 --- Two Assets with Single mental account --- p.76<br>Chapter 6.1.1 --- Different Sharpe ratios --- p.78<br>Chapter 6.1.2 --- Same Sharpe ratio --- p.82<br>Chapter 6.2 --- Two Assets with two mental accounts --- p.85<br>Chapter 6.2.1 --- Segregation or Aggregation --- p.86<br>Chapter 6.2.2 --- Numerical results --- p.90<br>Chapter 6.3 --- Summary --- p.93<br>Chapter 7 --- Conclusion --- p.96<br>Bibliography --- p.100
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"Optimal immunization strategy in multiple period portfolio selection." 2001. http://library.cuhk.edu.hk/record=b5890789.
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Lam Fong.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.<br>Includes bibliographical references (leaves 67-68).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Background --- p.1<br>Chapter 1.1 --- Bond and Yield --- p.1<br>Chapter 1.1.1 --- Bond [8] --- p.1<br>Chapter 1.1.2 --- Yields --- p.3<br>Chapter 1.1.3 --- Qualitative Nature of Price-Yield Curves --- p.5<br>Chapter 1.2 --- "Duration, Convexity and Time Value" --- p.8<br>Chapter 1.2.1 --- Duration --- p.8<br>Chapter 1.2.2 --- Qualitative Properties of Duration --- p.10<br>Chapter 1.2.3 --- Convexity --- p.16<br>Chapter 1.2.4 --- Literatures Review of Duration and Convexity --- p.17<br>Chapter 1.2.5 --- Time Value --- p.20<br>Chapter 2 --- Management of Interest Rate Risk --- p.22<br>Chapter 2.1 --- Laddered Strategy --- p.23<br>Chapter 2.2 --- Dumbbell Strategy --- p.24<br>Chapter 2.3 --- Immunization Strategy --- p.25<br>Chapter 2.4 --- Consideration of Convexity for Managing Interest Rate Risk --- p.26<br>Chapter 2.5 --- Duration Targeting[l2] --- p.28<br>Chapter 2.6 --- Immunizing Default-Free Bond Portfolios with a Duration Vec- tor [2] --- p.29<br>Chapter 2.7 --- The need of Dynamic Global Portfolio Immunization Theorem --- p.32<br>Chapter 3 --- Multi-Period Portfolio Selection --- p.34<br>Chapter 3.1 --- Objective --- p.34<br>Chapter 3.2 --- Dynamic Programming Formulation --- p.35<br>Chapter 3.3 --- Specific Situation --- p.46<br>Chapter 3.4 --- Summary of Implementation Results --- p.59<br>Chapter 4 --- Summary --- p.64<br>Bibliography --- p.67<br>A Matlab Program of the Dynamic Portfolio Selection --- p.69
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"Optimal dynamic portfolio selection under downside risk measure." 2014. http://library.cuhk.edu.hk/record=b6116127.
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传统的风险控制以终端财富的各阶中心矩作为风险度量，而现在越来越多的投资模型转向以不对称的在某个特定临界值的下行风险作为风险度量。在现有的下行风险中，安全第一准则，风险价值，条件风险价值，下偏矩可能是最有活力的代表。在这篇博士论文中，在已有的静态文献之上，我们讨论了以安全第一准则，风险价值，条件风险价值，下偏矩为风险度量的一系列动态投资组合问题。我们的贡献在于两个方面，一个是建立了可以被解析求解的模型，另一个是得到了最优的投资策略。在终端财富上加上一个上界，使得我们克服了一类下行风险投资组合问题的不适定性。引入的上界不仅仅使得我们的下行风险下的投资组合问题能得到显式解，而且也让我们可以控制下行风险投资组合问题的最优投资的冒险性。用分位数法和鞅方法，我们能够得到上述的各种模型的解析解。在一定的市场条件下，我们得到了对应的拉格朗日问题的乘子的存在性和唯一性, 这也是对应的鞅方法中的核心步骤。更进一步，当市场投资组合集是确定性的时候，我们推出解析的最优财富过程和最优投资策略。<br>Instead of controlling "symmetric" risks measured by central moments of terminal wealth, more and more portfolio models have shifted their focus to manage "asymmetric" downside risks that the investment return is below certain threshold. Among the existing downside risk measures, the safety-first principle, the value-at-risk (VaR), the conditional value-at-risk (CVaR) and the lower-partial moments (LPM) are probably the most promising representatives.<br>In this dissertation, we investigate a general class of dynamic mean-downside risk portfolio selection formulations, including the mean-exceeding probability portfolio selection formulation, the dynamic mean-VaR portfolio selection formulation, the dynamic mean-LPM portfolio selection formulation and the dynamic mean-CVaR portfolio selection formulation in continuous-time, while the current literature has only witnessed their static versions. Our contributions are two-fold, in both building up tractable formulations and deriving corresponding optimal policies. By imposing a limit funding level on the terminal wealth, we conquer the ill-posedness exhibited in the class of mean-downside risk portfolio models. The limit funding level not only enables us to solve dynamic mean-downside risk portfolio optimization problems, but also offers a flexibility to tame the aggressiveness of the portfolio policies generated from the mean-downside risk optimization models. Using quantile method and martingale approach, we derive optimal solutions for all the above mentioned mean-downside risk models. More specifically, for a general market setting, we prove the existence and uniqueness of the Lagrangian multiplies, which is a key step in applying the martingale approach, and establish a theoretical foundation for developing efficient numerical solution approaches. Furthermore, for situations where the opportunity set of the market setting is deterministic, we derive analytical portfolio policies.<br>Detailed summary in vernacular field only.<br>Zhou, Ke.<br>Thesis (Ph.D.) Chinese University of Hong Kong, 2014.<br>Includes bibliographical references (leaves i-vi).<br>Abstracts also in Chinese.
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"Multi-period optimal portfolio selection with limited rebalancing opportunities." 2011. http://library.cuhk.edu.hk/record=b5894622.
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Wang, Yang.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.<br>Includes bibliographical references (p. 72-74).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Literature Review and Model Description --- p.1<br>Chapter 1.1 --- Portfolio theory under mean-variance framework --- p.2<br>Chapter 1.2 --- Portfolio theory under utility-maximizing framework --- p.5<br>Chapter 1.3 --- Model Description --- p.11<br>Chapter 2 --- Parameterized optimal rebalancing strategy --- p.14<br>Chapter 2.1 --- An open-loop policy of the T-horizon model --- p.16<br>Chapter 2.2 --- A closed-loop policy of the T-horizon model --- p.24<br>Chapter 2.3 --- Illustrative numerical example --- p.36<br>Chapter 3 --- Non-parameterized optimal rebalancing model --- p.46<br>Chapter 3.1 --- T=2 period problem --- p.47<br>Chapter 3.2 --- T=3 period problem --- p.55<br>Chapter 4 --- s-S type policy --- p.59<br>Chapter 4.1 --- Exponential K-convex function --- p.60<br>Chapter 4.2 --- Revised multiperiod portfolio selection model --- p.62<br>Chapter 5 --- Conclusion and summary of work --- p.70<br>Bibliography --- p.71
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"Shrinkage method for estimating optimal expected return of self-financing portfolio." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075221.
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A new estimator for calculating the optimal expected return of a self-financing portfolio is proposed, by considering the joint impact of the sample mean vector and the sample covariance matrix. A shrinkage covariance matrix is designed to substitute the sample covariance matrix in the optimization procedure, which leads to an estimate of the optimal expected return smaller than the plug-in estimate. The new estimator is also applicable for both p < n and p ≥ n. Simulation studies are conducted for two empirical data sets. The simulation results show that the new estimator is superior to the previous methods.<br>By the seminal work of Markowitz in 1952, modern portfolio theory studies how to maximize the portfolio expected return for a given risk, or minimize the risk for a given expected return. Since these two issues are equivalent, this thesis only focuses on the study of the optimal expected return of a self-financing portfolio for a given risk.<br>Finally, under certain assumptions, we extend our research in the framework of random matrix theory.<br>The mean-variance portfolio optimization procedure requires two crucial inputs: the theoretical mean vector and the theoretical covariance matrix of the portfolio in one period. Since the traditional plug-in method using the sample mean vector and the sample covariance matrix of the historical data incurs substantial estimation errors, this thesis explores how the sample mean vector and the sample covariance matrix behave in the optimization procedure based on the idea of conditional expectation and finds that the effect of the sample mean vector is an additive process while the effect of the sample covariance matrix is a multiplicative process.<br>Liu, Yan.<br>Adviser: Ngai Hang Chan.<br>Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: .<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 2011.<br>Includes bibliographical references (leaves 76-80).<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Abstract also in Chinese.
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"Theoretical and numerical study on continuous-time mean-variance optimal strategies." 2006. http://library.cuhk.edu.hk/record=b5896528.
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Li Yan.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.<br>Includes bibliographical references (leaves 87-88).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Literature Review --- p.8<br>Chapter 2.1 --- Markowitz´ةs Single-Period Mean-Variance Model --- p.9<br>Chapter 2.2 --- Discrete-Time Mean-Variance Problem --- p.10<br>Chapter 2.2.1 --- Optimal Buy-and-Hold Policy --- p.11<br>Chapter 2.2.2 --- Optimal Rolling Markowitz Policy --- p.12<br>Chapter 2.2.3 --- Multi-Period Mean-Variance Optimal Policy --- p.12<br>Chapter 2.3 --- Continuous-Time Market --- p.13<br>Chapter 2.3.1 --- Optimal Unconstrained Policy --- p.15<br>Chapter 2.3.2 --- Bankruptcy Prohibited Optimal Policy --- p.16<br>Chapter 2.3.3 --- No-Shorting Optimal Policy --- p.17<br>Chapter 2.4 --- Continuously Rebalancing Optimal Policy --- p.18<br>Chapter 3 --- Discretized Continuous-Time Optimal Policies --- p.20<br>Chapter 3.1 --- Problem Setup --- p.21<br>Chapter 3.2 --- Unconstrained Problem --- p.25<br>Chapter 3.3 --- Problem with No-shorting Constraint --- p.31<br>Chapter 3.4 --- Problem with No-Bankruptcy Constraint --- p.34<br>Chapter 3.4.1 --- Quasi No-Bankruptcy Problem --- p.36<br>Chapter 3.5 --- Stability of the Simulation --- p.38<br>Chapter 3.6 --- Concluding Remarks --- p.41<br>Chapter 4 --- Performance of Continuous-Time M-V Optimal Policies --- p.43<br>Chapter 4.1 --- Measures of the Performance by Probabilities --- p.45<br>Chapter 4.2 --- Performance of the Optimal Mean-Variance Portfolio --- p.51<br>Chapter 4.2.1 --- Target-Hitting Probability --- p.51<br>Chapter 4.2.2 --- Cut-Off Probability --- p.53<br>Chapter 4.2.3 --- Target-Hitting-before-Cut-Off Probability --- p.58<br>Chapter 4.3 --- Numerical Evaluations of Probabilities for Discrete-Time Market --- p.63<br>Chapter 4.3.1 --- Simulation on Target-Hitting Probability --- p.64<br>Chapter 4.3.2 --- Simulation on Zero-Hitting Probability --- p.66<br>Chapter 4.3.3 --- Simulation on Target-Hitting-before-Bankruptcy Probability --- p.67<br>Chapter 4.4 --- Policy Comparison --- p.68<br>Chapter 4.4.1 --- Profile of the Probabilities --- p.70<br>Chapter 4.4.2 --- Impact of z on the Probabilities --- p.72<br>Chapter 4.5 --- Concluding Remarks --- p.74<br>Chapter 5 --- Empirical Analysis --- p.75<br>Chapter 5.1 --- Experiment Description and Parameter Estimation --- p.76<br>Chapter 5.1.1 --- Introduction of the Data --- p.76<br>Chapter 5.1.2 --- Experiment Description --- p.77<br>Chapter 5.1.3 --- Parameter Estimation --- p.79<br>Chapter 5.2 --- Empirical Results and Analysis --- p.80<br>Chapter 5.2.1 --- Performance Indicator --- p.80<br>Chapter 5.2.2 --- Experimental Results and Analysis --- p.81<br>Chapter 5.3 --- Concluding Remarks --- p.83<br>Chapter 6 --- Summary --- p.84<br>Bibliography --- p.87
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"Optimal execution strategy under CVaR framework." 2013. http://library.cuhk.edu.hk/record=b5549303.
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交易员通常在处理大单交易时会遇到困难，因为市场没有足够的流动性来消化这些买单或卖单。交易员想要在对市场产生冲击最小的情况下完成加仓或平仓，或者他们想设计一套程序来达成这个目的。<br>由于每次的交易结果都是一个随机变量，为了方便比较，我们可以设置一个比较基准，在本文中我们选用。<br>本文对之前存在的动态一致性风险测度模型的一大改进是引入了动量效应。在短时的股市中动量效应就有明显效应。<br>我们的最优策略是当市场朝我们不利的方向变动时我们加速仓位的增加或减少，而朝我们有利的方向变动时我们减缓我们的动作。我们的最优策略每期都会出请或买入一个预先设定的比例的股票，同时我们会在交易的初期加快我们的买卖处理，而在后期放缓动作。<br>我们的最优策略是时间一致的，并且是一个动态变化的策略。<br>For an equity trader, one problem he faces is to execute large order of stocks for his clients. The trader seeks to optimize his performance for buying and selling stocks. Basically various costs incurred during the trading includes the commission fees, margin loans, bid-ask spread, price impacts, taxes and other occasional costs. But among the all, the price impact takes the largest part.<br>In a sell program, the implementation shortfall is the differience between the value of the trader’s initial equity position and the sum of the cash flow he receives from his trading process. Because of the randomness inherited in the stock price process, the resulting implementation shortfall is a random variable, and we should project the random variable into real number to compare. The measure we choose is the dynamic coherent risk measure.<br>One of the most significant improvements of our model is the inclusion of momentum effect. Momentum is a significant effect when considering stock price dynamics in a daily circle. Another main contribution is the approximation method used in solving our model, which helps reduce much computation burden.<br>Our strategy applies best to the high frequency trading problem due to the nature of our approximation method. The optimal strategy in our framework is to trade more when the current price drift is negative. This is mainly due to the prevention from future possible negative price drifts. Our strategy also shows that, in addition to liquidate a fixed proportion of inventory at each period, the trader has to trade faster at earlier periods.Our optimal strategy derived from dynamic programming is time consistent and is an adapted process.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>He, Mengfei.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2013.<br>Includes bibliographical references (leaves 132-134).<br>Abstracts also in Chinese.<br>Abstract --- p.i<br>Acknowledgement --- p.iv<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Literature Review --- p.10<br>Chapter 2.1 --- Model Comparison --- p.10<br>Chapter 2.1.1 --- Price dynamics --- p.10<br>Chapter 2.1.2 --- Price impacts --- p.11<br>Chapter 2.1.3 --- Inventory constraints --- p.14<br>Chapter 2.1.4 --- Objective functions and risk measures --- p.15<br>Chapter 2.1.5 --- Discrete or continuous framework --- p.17<br>Chapter 2.2 --- Work by Bertsimas and Lo --- p.18<br>Chapter 2.2.1 --- Formulation under Linear Price Impact --- p.21<br>Chapter 2.2.2 --- Formulation under LPT Law --- p.22<br>Chapter 2.2.3 --- Formulation under General Price Impact --- p.26<br>Chapter 2.2.4 --- Portfolio Case --- p.28<br>Chapter 2.3 --- A Series ofWorks by Almgren --- p.29<br>Chapter 2.3.1 --- Adaptive Arrival Price --- p.29<br>Chapter 2.3.2 --- Bayesian Adaptive Trading with a Daily Cycle --- p.32<br>Chapter 2.3.3 --- Mean-Variance Optimal Adaptive Execution --- p.36<br>Chapter 2.4 --- Work by Lin and Pena --- p.42<br>Chapter 2.4.1 --- Multiple Assets --- p.46<br>Chapter 2.5 --- A Series ofWorks by Forsyth --- p.48<br>Chapter 2.5.1 --- A Hamilton-Jacobi-Bellman Approach to Optimal Trade Execution --- p.49<br>Chapter 2.5.2 --- A Mean Quadratic Variation Approach --- p.55<br>Chapter 2.6 --- A Series ofWorks by Schied --- p.58<br>Chapter 2.6.1 --- Optimal Trade Execution in Limit Order BookModels --- p.58<br>Chapter 2.6.2 --- Optimal Trade Execution under Geometric BrownianMotion --- p.66<br>Chapter 2.7 --- Work byMoazeni --- p.69<br>Chapter 3 --- Model Setting --- p.71<br>Chapter 3.1 --- ExecutionModel --- p.71<br>Chapter 3.2 --- Coherent Dynamic RiskMeasures --- p.81<br>Chapter 3.3 --- Optimization Formulation --- p.84<br>Chapter 4 --- Solution Methodologies --- p.89<br>Chapter 4.1 --- BinomialModel --- p.89<br>Chapter 4.2 --- Linear Approximation --- p.92<br>Chapter 4.3 --- Numerical Results --- p.107<br>Chapter 4.4 --- Simulation Results --- p.110<br>Chapter 4.5 --- Efficient Frontier --- p.111<br>Chapter 4.6 --- CVaR Case --- p.113<br>Chapter 5 --- Conclusions and Future Research --- p.119<br>Chapter 5.1 --- Conclusions --- p.119<br>Chapter 5.2 --- Future Research --- p.121<br>Chapter A --- Equation Derivation --- p.124<br>Bibliography --- p.132