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1
Ma, Zishun. "Topics in financial market risk modelling." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1675.
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The growth of the financial risk management industry has been motivated by the increased volatility of financial markets combined with the rapid innovation of derivatives. Since the 1970s, several financial crises have occurred globally with devastating consequences for financial and non-financial institutions and for the real economy. The most recent US subprime crisis led to enormous losses for financial and non-financial institutions and to a recession in many countries including the US and UK. A common lesson from these crises is that advanced financial risk management systems are required. Financial risk management is a continuous process of identifying, modeling, forecasting and monitoring risk exposures arising from financial investments. The Value at Risk (VaR) methodology has served as one of the most important tools used in this process. This quantitative tool, which was first invented by JPMorgan in its Risk-Metrics system in 1995, has undergone a considerable revolution and development during the last 15 years. It has now become one of the most prominent tools employed by financial institutions, regulators, asset managers and nonfinancial corporations for risk measurement. My PhD research undertakes a comprehensive and practical study of market risk modeling in modern finance using the VaR methodology. Two newly developed risk models are proposed in this research, which are derived by integrating volatility modeling and the quantile regression technique. Compared to the existing risk models, these two new models place more emphasis on dynamic risk adjustment. The empirical results on both real and simulated data shows that under certain circumstances, the risk prediction generated from these models is more accurate and efficient in capturing time varying risk evolution than traditional risk measures. Academically, the aim of this research is to make some improvements and extensions of the existing market risk modeling techniques. In practice, the purpose of this research is to support risk managers developing a dynamic market risk measurement system, which will function well for different market states and asset categories. The system can be used by financial institutions and non-financial institutions for either passive risk measurement or active risk control.
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Gyamfi, Michael. "Modelling The Financial Market Using Copula." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron149601408369316.
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Omar, Mahmoud Abdulsalam Taib. "Stochastic modelling in financial markets : case study of the Nigerian Stock Market." Thesis, Sheffield Hallam University, 2012. http://shura.shu.ac.uk/16847/.
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This research uses suitable stochastic models typically encountered in empirical and quantitative financial economics to analyse stock market data from the Nigerian Stock Market (NSM), in light of a) possible changes in the policy environments as result of the 2004 financial reforms by the then Governor of Central Bank of Nigeria, b) effects or otherwise of the 2008-09 global financial crises on the Nigerian financial system, and c) more technical issues underpinning performance of financial markets for example market efficiency, anomalies, bubbles, volatilities and their implications for investment decisions, stock market development and financial policy. There are substantial differences in the operation and characteristics of developed, emerging and pre-emerging (African) financial markets in terms of the above mentioned issues. Sometimes as part of general discussion of results we comment on the extent to which the characteristics of the NSM differ from known results in developed markets. A wide range of financial econometric methods and models including multivariate regression, Goodness of fit tests, Runs, Autocorrelation Function, Variance Ratio, Autoregressive tests, and discrete log logistic and GARCH-type models are applied. Both the All Share index and return data for 2000 to 2010 are used in this study. The time series data are divided into two periods namely pre-reforms (2000-2004) and postreforms (2005-2010). This study provides both investors and researchers in emerging African markets with a clear understanding of key financial characteristics of the NSM. Some useful results were obtained. Key characteristics of the NSM analysed in terms of market index prices and returns reveal evidence of market inefficiency and volatility. The data do not provide evidence of bubbles and anomalies in the NSM. This study, according to the author’s best knowledge, is possibly the most comprehensive combined study of crucial issues affecting the NSM including volatility, anomalies, bubbles and market efficiency. However, some other issues are excluded from the study because of the limitations of the data for example valuations and predictability, which are more suitably studied within specific companies and market sectors.
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Tran, Quoc-Tran. "Some contributions to financial market modelling with transaction costs." Thesis, Paris 9, 2014. http://www.theses.fr/2014PA090036/document.
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Cette thèse traite plusieurs problèmes qui se posent pour les marchés financiers avec coûts de transaction et se compose de quatre parties.On commence, dans la première partie, par une étude du problème de couverture approximative d’une option Européenne pour des marchés de volatilité locale avec coûts de transaction proportionnelles.Dans la seconde partie, on considère le problème de l’optimisation de consommation dans le modèle de Kabanov, lorsque les prix sont conduits par un processus de Lévy.Dans la troisième partie, on propose un modèle général incluant le cas de coûts fixes et coûts proportionnels. En introduisant la notion de fonction liquidative, on étudie le problème de sur-réplication d’une option et plusieurs types d’opportunités d’arbitrage.La dernière partie est consacrée à l’étude du problème de maximisation de l’utilité de la richesse terminale d’une portefeuille sous contraintes de risqueThis thesis deals with different problems related to markets with transaction costs and is composed of four parts.In part I, we begin with the study of assymptotic hedging a European option in a local volatility model with bid-ask spread.In part II, we study the optimal consumption problem in a Kabanov model with jumps and with default risk allowed.In part III, we sugest a general market model defined by a liquidation procès. This model is more general than the models with both fixed and proportional transaction costs. We study the problem of super-hedging an option, and the arbitrage theory in this model.In the last part, we study the utility maximization problem under expected risk constraint
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Lamper, David. "Problems in mathematical finance : market modelling and derivative pricing." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270642.
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Yang, Ju-Huei Steffi. "On financial market instability : an analysis using agent-based modelling." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614781.
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Alhnaity, Bashar. "Financial engineering modelling using computational intelligent techniques : financial time series prediction." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/13652.
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Prediction of financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and dynamic nature. In any investment activity, having an accurate prediction system will significantly benefit investors by guiding decision making, especially in trading, asset management and risk management. Thus, the attempts to build such systems have attracted the attention of practitioners in the market and also researchers for many decades. Furthermore, the purpose of this thesis is to investigate and develop a new approach to predicting financial time series with consideration given to their dynamic nature. In this thesis, the prediction procedures will be carried out in three phases. The first phase proposes a new hybrid dynamic model based on Ensemble Empirical Mode Decomposition (EEMD), Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN), Support Vector Regression (SVR) and EEMD-Genetic Algorithm (GA)-Weighted Average (WA) to predict stock index closing price. EEMD in this phase is introduced as a preprocessing step to historical observation for the first time in the literature. The experimental results show that the EEMDD-GA-WA model performance is a notch above the other methods utilised in this phase. The second phase proposes a new hybrid static model based on Wavelet Transform (WT), RNN, Support Vector Machine (SVM), Nave Bayes and WT-GA-WA to predict the exact change of the stock index closing price. In this phase, the experimental results showed that the proposed WT-GA-WA model outperformed the rest of the models utilised in this phase. Moreover, the input data that are fed into the hybrid model in this phase are technical indicators. The third phase in this research introduces a new Hybrid Heuristic-Rules-based System (HHRS) for stock price prediction. This phase intends to combine the output of the hybrid models in phase one and two in order to enhance the final prediction results. Thus,to the best of our knowledge, this study is the only one to have carried out and tested this approach with a real data set. The results show that the HHRS outperformed all suggested models over all the data sets. Thus, this indicates that combining di↵erent techniques with diverse types of information could enhance prediction accuracy.
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Wang, Shixuan. "Four essays on modelling asset returns in the Chinese financial market." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7655/.
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Firstly, we employ a three-state hidden semi-Markov model (HSMM) to explain the time-varying distribution of the Chinese stock market returns. Our results indicate that the time-varying distribution depends on the hidden states, represented by three market conditions, namely the bear, sidewalk, and bull markets. Secondly, we further employ the three-state HSMM to the daily returns of the Chinese stock market and seven developed markets. Through the comparison, three unique characteristics of the Chinese stock market are found, namely “Crazy Bull”, “Frequent and Quick Bear”, and “No Buffer Zone”. Thirdly, we propose a new diffusion process referred to as the ``camel process'' to model the cumulative return of a financial asset. Its steady state probability density function could be unimodal or bimodal, depending on the sign of the market condition parameter. The overreaction correction is realised through the non-linear drift term. Lastly, we take the tools in functional data analysis to understand the term structure of Chinese commodity futures and forecast their log returns at both short and long horizons. The FANOVA has been applied to examine the calendar effect of the term structure. An h-step functional autoregressive model is employed to forecast the log return of the term structure.
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Butler, Matthew R. "Computational intelligence for analysis concerning financial modelling and the adaptive market hypothesis." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/4836/.
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This thesis concerns the field of computational intelligence (CI), an important area of computer science that predominantly endeavours to model complex systems with heuristic algorithms. Heuristic algorithms from CI are generally nature or biologically inspired programs that iteratively learn from experience. More specifically, the research focuses on the overlap between the fields of CI and financial analysis, where the financial markets (in the form of financial time series) provide the complex system of interest. Therefore the objective of the thesis can be summarized as a exercise in improving the abilities of CI algorithms for modelling financial time series. CI has been applied to a whole spectrum of domains where techniques are developed at a more general level and then applied to a particular application area or complex system. The financial markets are somewhat unique. Unlike other complex systems in nature, the financial markets are of our own creation and their evolution is a by product of human nature, where the beliefs and bias of the participants (humans) in the complex system (financial markets) govern how the system behaves. This is in contrast to many complex systems where, for example, the opinions of experts have no effect on the outcome. Thus, the motivation of this work is to quantify meaningful characteristics (behaviour) of the financial markets as a means to improve and understand how heuristic algorithms respond to them. This process of applying more scrutiny to the analysis of the application area, yields an approach to algorithm development that takes into account the unique characteristics of the market. To achieve this goal the thesis is structured into three sections that comprise four contribution chapters. The contribution chapters are labelled: validity, implications and innovations and each is motivated by a separate research question. The validity chapter is based on determining a reasonable characterization of the financial markets. This includes a detailed literature review of the popular competing market theories as well as some new innovative tests. There is not a general consensus as to which theory is correct but from a computational intelligence perspective the adaptive market hypothesis (AMH) is revealed as a reasonable characterization of the financial markets and one that provides quantifiable characteristics to be utilized in following chapters. The implications chapter concerns testing the effect of implications of the AMH, if any, on the robustness of models derived from CI. Specifically three implications are examined, i.e., variable stationarity, variable efficiency and the waxing and waning of investment strategies. The experiments concerned six algorithms from four of the major paradigms in supervised learning. The results from each of the studies demonstrated that the implications of the AMH affect CI derived models. This conclusion reveals that the unique properties of the financial markets should be taken into account when applying CI algorithms for modelling and forecasting. The two chapters concerning innovations explore how CI techniques can be improved based on the results from the validity and implications chapters. The first chapter (chapter 6) concerns the development of a meta learner based on the implication of the waxing and waning of investment strategies, the meta learning algorithm called LATIS (Learning Adaptive Technical Indicator System) is a blend of micro and macro modelling perspectives and allows for online adaptive learning with an interpretable white box framework. The second innovations chapter (chapter 7) concerns the discretization of financial time series data into a finite alphabet. A discretization algorithm is developed, which extends an existing state-of-the-art algorithm to handle the characteristics of financial time series. The proposed algorithm, called alSAX (adaptive local Symbolic Aggregate approXimation), is demonstrated to be superior in terms of its symbolic mappings, in relation to a gold standard, and in the popular time series subsequence analysis task. Additionally, an invalid theoretical assumption of the existing algorithm is revealed. The flaw in the algorithm is discussed and its impact is determined based on the characteristics of the time series and the parameters of the algorithm. From the analysis, the thesis offers viable fixes to compensate for the flaw, where the suitability of the fixes are dependent on the problem domain and objective of the data mining task.
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Do, Thi Tuan Anh. "Modelling cross-market linkages between global markets and China’s A-, B- and H-shares." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2020. https://ro.ecu.edu.au/theses/2344.
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One of the biggest challenges in quantifying joint risk and forming effective policies in financial management and investment strategies is to fully understand the characteristics of market associations in low and high volatility periods. Market interdependence, therefore, is a hot topic that has received interest from academics and industry experts, especially since the Asian Financial Crisis in 1997. China, being the world’s second-largest economy, has been the centre of many studies investigating stock market dependencies. While China has three major share types, namely A-, B- and H-shares, with different market players, market characteristics and operating efficiency, the number of studies on each of these share types remains conservative in comparison to the vast literature on the financial modelling of market interdependencies. Given the need for a more comprehensive understanding of the influence between these share types and other global markets, especially during market turbulences, this thesis examines the cross-market linkages between A-, B- and H-shares in China and several major emerging and advanced markets from 2002 to 2017, which is divided into two non-crisis periods and two crisis periods.
This thesis assesses market integration among 17 markets, including asymmetries and leverage effect in the marginal distributions, volatility spillover and tail dependence. The thesis aims to: 1) investigate the univariate asymmetries and leverage effect in the distributional volatility of each time series and to detect volatility spillover between China and other studied markets; 2) assess the dynamic multivariate dependence between China and other studied markets; 3) evaluate the bivariate dependence structure for each of China’s markets and other studied markets using seven different copula functions; and 4) study the multivariate joint tail dependence structure of all studied markets using vine copulas.
There are various findings from the thesis. Many advanced and emerging markets experienced leverage effect and asymmetries in volatility. China’s markets were much more prone to local shocks than external shocks and in many cases, there is evidence that China’s markets diverged from the global trends especially during the crisis periods. Besides, segmentation between China’s markets and the United States is clearly evident. In addition, regional dependence is stronger than intra-regional dependence. The thesis also found the existence of contagion effect between each of China’s markets and various markets in the sample in the Global Financial Crisis. Finally, heterogeneity was found for A-, B- and H-shares in various aspects, from distributional asymmetries to joint behaviour in both crisis and non-crisis periods.
A novel aspect of this thesis is that it closes the gap in the literature of market linkages for A-, B- and H-shares with other global markets by assessing volatility spillover, time-varying co-movement, and tail dependence among the studied markets. This thesis provides various implications in both theoretical and empirical contexts in many areas including measuring joint risk at the tails, constructing an optimal portfolio, hedging, and managing financial exposures and contagious volatility from other markets. The thesis provides some recommendations and suggestions regarding the policies implemented in China.
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Heinen, Andreas. "Modelling time series counts data in financial microstructure /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3130202.
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Du, Toit Carl. "Modelling market risk with SAS Risk Dimensions : a step by step implementation." Thesis, Link to the online version, 2005. http://hdl.handle.net/10019/1015.
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Singh, Abhay Kumar. "Modelling Extreme Market Risk - A Study of Tail Related Risk Measures." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2011. https://ro.ecu.edu.au/theses/417.
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Market risk modelling is one of the most dynamic domains in finance. Risk is the uncertainty that affects the values of assets in the system in an unknown fashion causing fluctuations in their values and in investment outcomes. Market risk is defined as the losses due to fluctuations in the prices of financial assets which are caused by changing market conditions. Market risk modelling comprises tools and techniques which quantify the risk associated with financial instruments. Risk quantification is necessary to devise strategies such as hedging or diversification against the risk, to avoid severe losses. With the recent financial market events like the Global Financial Crisis, there is a need to evaluate the traditional risk return relationships presented in Asset Pricing models and more sophisticated risk modelling tools like Value at Risk (VaR). Along with Asset Pricing and VaR modelling another important risk issue between financial assets is the asymptotic tail dependence, which plays a vital role in accurate risk measurement in portfolio selection and hedging amongst other considerations. The usual measure of dependence, the Pearson Correlation coefficient works on the assumption of normality in the data distribution and hence is unable to capture the tail dependence between financial assets which is an important characteristic for tail risk modelling. The research presented in this dissertation models the risk quantification techniques of Asset Pricing, VaR modelling and Tail dependence, with the more sophisticated statistical tools of Quantile Regression and Extreme Value Theory (EVT), which are particularly useful in modelling the tail behaviour of the distributions. The research targets four broad objectives to evaluate extreme risk and dependence measures in the Australian stock market which are realised with the robust techniques of Quantile Regression and EVT. The thesis comprises six chapters with chapter-1 introducing the thesis presenting the driving motivations for the research and the four major objectives (which are detailed in individual chapters following chapter-1) along with the contribution of the research and finally chapter-6 presenting the conclusion. The structure of rest of the thesis is also outlined in chapter-1.
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Lespagnol, Vivien. "Information diffusion in financial markets : an agent-based approach to test the fundamental value discovery in different market structures." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM2012/document.
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L’objectif des travaux présentés dans cette thèse est d’étudier la diffusion de l’information dans les marchés financiers. Considérant comme établi que les individus sont hétérogènes et à rationalité limitée, nous avons fondé nos travaux sur une catégorie de modèles computationnels dans le but de simuler les actions et les interactions des agents autonomes. Cette catégorie est communément nommée modélisation agent (ABM).Plus concrètement, cette recherche se concentre sur le rôle de l’hétérogénéité des agents dans la diffusion et l’utilisation de l’information. À cet effet, nous avons développé deux structures de marché, qui diffèrent par leur transparence. Dans les chapitres 1 et 2, nous introduisons un marché centralisé, où une partie du carnet d’ordre est accessible (information publique). Dans le chapitre 3, nous développons un marché de gré à gré dans lequel les agents négocient et échangent avec leurs relationsThe piece of work’s aim is to understand information diffusion in financial markets. Starting from the empirical evidences that agents are heterogeneous and bounded rational, we based our investigations on a class of computational models for simulating the actions and interactions of autonomous agents: the agent - based model (ABM). More precisely, this research focuses on the impacts of agents heterogeneity in diffusion and use of information. For this purpose, we developed two market structures, in which the market transparency varies. In the chapters 1 and 2, we introduce a centralised market, where a part of the order-book is available as a public information. In the chapter 3, we build an Over-The-Counter market, where agents bargains with their trading contacts
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Marcus, Elwin. "Simulating market maker behaviour using Deep Reinforcement Learning to understand market microstructure." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240682.
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Market microstructure studies the process of exchanging assets underexplicit trading rules. With algorithmic trading and high-frequencytrading, modern financial markets have seen profound changes in marketmicrostructure in the last 5 to 10 years. As a result, previously establishedmethods in the field of market microstructure becomes oftenfaulty or insufficient. Machine learning and, in particular, reinforcementlearning has become more ubiquitous in both finance and otherfields today with applications in trading and optimal execution. This thesisuses reinforcement learning to understand market microstructureby simulating a stock market based on NASDAQ Nordics and trainingmarket maker agents on this stock market. Simulations are run on both a dealer market and a limit orderbook marketdifferentiating it from previous studies. Using DQN and PPO algorithmson these simulated environments, where stochastic optimal controltheory has been mainly used before. The market maker agents successfullyreproduce stylized facts in historical trade data from each simulation,such as mean reverting prices and absence of linear autocorrelationsin price changes as well as beating random policies employed on thesemarkets with a positive profit & loss of maximum 200%. Other tradingdynamics in real-world markets have also been exhibited via theagents interactions, mainly: bid-ask spread clustering, optimal inventorymanagement, declining spreads and independence of inventory and spreads, indicating that using reinforcement learning with PPO and DQN arerelevant choices when modelling market microstructure.Marknadens mikrostruktur studerar hur utbytet av finansiella tillgångar sker enligt explicita regler. Algoritmisk och högfrekvenshandel har förändrat moderna finansmarknaders strukturer under de senaste 5 till 10 åren. Detta har även påverkat pålitligheten hos tidigare använda metoder från exempelvis ekonometri för att studera marknadens mikrostruktur. Maskininlärning och Reinforcement Learning har blivit mer populära, med många olika användningsområden både inom finans och andra fält. Inom finansfältet har dessa typer av metoder använts främst inom handel och optimal exekvering av ordrar. I denna uppsats kombineras både Reinforcement Learning och marknadens mikrostruktur, för att simulera en aktiemarknad baserad på NASDAQ i Norden. Där tränas market maker - agenter via Reinforcement Learning med målet att förstå marknadens mikrostruktur som uppstår via agenternas interaktioner. I denna uppsats utvärderas och testas agenterna på en dealer – marknad tillsammans med en limit - orderbok. Vilket särskiljer denna studie tillsammans med de två algoritmerna DQN och PPO från tidigare studier. Främst har stokastisk optimering använts för liknande problem i tidigare studier. Agenterna lyckas framgångsrikt med att återskapa egenskaper hos finansiella tidsserier som återgång till medelvärdet och avsaknad av linjär autokorrelation. Agenterna lyckas också med att vinna över slumpmässiga strategier, med maximal vinst på 200%. Slutgiltigen lyckas även agenterna med att visa annan handelsdynamik som förväntas ske på en verklig marknad. Huvudsakligen: kluster av spreads, optimal hantering av aktielager och en minskning av spreads under simuleringarna. Detta visar att Reinforcement Learning med PPO eller DQN är relevanta val vid modellering av marknadens mikrostruktur.
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Dima, Dafni. "European labour market trajectories before and during the 2008 financial crisis : national, regional and individual variation." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31084.
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Since 2008 Europe has been in crisis, a financial and debt crisis that spread from the U.S. to all European countries. This thesis aims to provide evidence on the consequences of the crisis for individuals’ labour market outcomes across different countries and regions of Europe and to analyse how the recession has differentially affected sub-groups of the European population. Through the analysis of the longitudinal component of the European Union Statistics on Income and Living Conditions (EU-SILC) dataset, the project sheds light on the labour market trajectories of more than 20,000 Europeans across 11 European countries and 41 regions, before and during the 2008 financial crisis (2005-2012). Sequence and cluster analysis are used to investigate the heterogeneity of individual labour market trajectories across countries and time, while multilevel models are used to study regional labour markets during the years in crisis. The concept of transitional labour markets, as well as theories of labour market segmentation, job competition and job mobility, provide the theoretical framework for this research. The empirical findings show that during the financial crisis, labour market trajectories appear more turbulent and fragmented for the already disadvantaged sub-groups, namely women, younger workers and low educated workers. Furthermore, during the Great recession, an increase in unemployment among men confirms the sectoral profile of the crisis, which hit harder the male-dominated sectors of construction and industry. At the same time, a decrease in inactivity among women is consistent with the added worker effect, according to which women in periods of economic hardship are pushed towards labour market activity in order to contribute to the household income. Countries with weak economies and underperforming labour markets prior to the crisis, such as Greece and Italy, unsurprisingly experienced a deep and persistent crisis, while countries with stronger economies and more inclusive labour markets, such as Denmark and Sweden, managed to survive the crisis with less social harm. The institutional context of the countries offering high chances of employment even during the financial crisis, such as the Nordic countries, lies on the flexicurity of their labour markets. Indeed, flexible labour markets with the use of reduced working-time schemes, i.e. part-time forms of employment, contained unemployment during the financial shock. However, we need to be cautious about flexibility without security or partial deregulation of the markets, implemented in southern European countries, because during the crisis such policies led to further labour market segmentation and thus an increase in employment inequalities. Finally, the region of residence matters in employment outcomes, almost as much as the country of residence. In fact, from the regional analysis of individual employment outcomes during the years of the crisis, an uneven distribution of labour is detected even within the national borders. Summing up, the European crisis should be considered as the sum of national and regional crises.
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Watson, Iain David. "An investigation of the use of market and industry data in financial distress modelling : based on data derived from the Unlisted Securities Market and Official List." Thesis, University of Ulster, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339298.
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Coulon, Michael. "Modelling price dynamics through fundamental relationships in electricity and other energy markets." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:ddc11641-920f-461f-85cd-a9e6351d9104.
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Energy markets feature a wide range of unusual price behaviour along with a complicated dependence structure between electricity, natural gas, coal and carbon, as well as other variables. We approach this broad modelling challenge by firstly developing a structural framework to modelling spot electricity prices, through an analysis of the underlying supply and demand factors which drive power prices, and the relationship between them. We propose a stochastic model for fuel prices, power demand and generation capacity availability, as well as a parametric form for the bid stack function which maps these price drivers to the spot electricity price. Based on the intuition of cost-related bids from generators, the model describes mathematically how different fuel prices drive different portions of the bid stack (i.e., the merit order) and hence influence power prices at varying levels of demand. Using actual bid data, we find high correlations between the movements of bids and the corresponding fuel prices (coal and gas). We fit the model to the PJM and New England markets in the US, and assess the performance of the model, in terms of capturing key properties of simulated price trajectories, as well as comparing the model’s forward prices with observed data. We then discuss various mathematical techniques (explicit solutions, approximations, simulations and other numerical techniques) for calibrating to observed fuel and electricity forward curves, as well as for pricing of various single and multi-commodity options. The model reveals that natural gas prices are historically the primary driver of power prices over long horizons in both markets, with shorter term dynamics driven also by fluctuations in demand and reserve margin. However, the framework developed in this thesis is very flexible and able to adapt to different markets or changing conditions, as well as capturing automatically the possibility of changes in the merit order of fuels. In particular, it allows us to begin to understand price movements in the recently-formed carbon emissions markets, which add a new level of complexity to energy price modelling. Thus, the bid stack model can be viewed as more than just an original and elegant new approach to spot electricity prices, but also a convenient and intuitive tool for understanding risks and pricing contracts in the global energy markets, an important, rapidly-growing and fascinating area of research.
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Siddiqui, Muhammad Shahid. "Three Essays on Environmental Economics and on Credit Market Imperfections." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20161.
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This dissertation contains three essays on environmental economics and on credit
market imperfections.
The literature on carbon tax incidence generally finds that carbon taxes have a
regressive impact on the distribution of income. The main reason for that finding
stems from the fact that poor households spend a larger share of their total expenditure
on energy products than the rich households do. This literature, however, has ignored the impact of carbon taxes on income stemming from changes in relative factor prices. Yet, changes in household welfare depend not only on variations in commodity prices, but also on changes in income.
Chapter 1 provides a comprehensive analysis of the distributional impact of carbon taxes on inequality by considering both demand-side and supply-side channels. We use a multi-sector, multi-household general equilibrium model to analyze the distributional impact of carbon taxes on inequality. Using equivalent income as the household welfare metric, we apply the Shapley value and concentration index approaches to decomposing household inequality. Our simulation results suggest that
carbon taxes exert a larger negative impact on the income of the rich than that of the poor, and are thereby progressive. On the other hand, when assessed from the use side alone (i.e., commodity prices alone), our results confirm previous findings, whereas carbon taxes are regressive.
However, due to the stronger incidence of carbon taxes on inequality from the income
side, our results suggest that the carbon tax tends to reduce inequality. These
findings further suggest that the traditional approach of assessing the impact of
carbon taxes on inequality through changes in commodity prices alone may be misleading.
Chapter 2 investigates the economic impacts of creating an emissions bubble between Canada and the US in a context of subglobal participation in efforts to reduce pollution with market based-instruments. One of the advantages of an emissions bubble is that it can be beneficial to countries that differ in their production and consumption patterns. To address the competitiveness issue that arises from the free-rider problem in the area of climate-change mitigation, we consider the imposition of a border tax adjustment (BTA) - a commonly suggested solution in the literature.
We develop a detailed multisector and multi-regional general equilibrium model
to analyze the welfare, aggregate, sectoral and trade impacts of the formation of an
emissions bubble between Canada and the US with and without BTA. Our simulation
results suggest that, in the absence of BTA, the creation of the bubble would make both countries better off through a positive terms-of-trade effect, and more importantly, through a significant reduction in Canada’s marginal abatement cost. The benefits of these positive effects would spill over to the non-participating countries, leading them to increase their trade shares in non-emissions-intensive goods.
Moreover, the simulation results also indicate that a unilateral implementation of a BTA by any one of the two countries is welfare deteriorating in the imposing country and welfare improving in the other. In contrast, a joint implementation of a BTA by the two countries would make Canada better off and the US worse off.
Chapter 3 shows that learning by lending is a potential channel of understanding
the business cycle fluctuation under an imperfect credit market. An endogenous link
among the learning parameter, lending rates, and the size of investment makes it
possible to generate an internal propagation even due to a temporary shock. The main finding of this chapter is the explanation of how ex post non-financial factors such as information losses by individual agents in a credit market may account for a persistence in real indicators such as capital stock and output.
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Saliba, Pamela. "High-frequency trading : statistical analysis, modelling and regulation." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX044.
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Cette thèse est constituée de deux parties liées l’une à l’autre. Dans la première, nous étudions empiriquement le comportement des traders haute fréquence sur les marchés financiers européens. Nous utilisons les résultats obtenus afin de construire dans la seconde partie de nouveaux modèles multi-agents. L’objectif principal de ces modèles est de fournir aux régulateurs et plateformes de négociation des outils innovants leur permettant de mettre en place des règles pertinentes pour la microstructure et de quantifier l’impact des divers participants sur la qualité du marché.Dans la première partie, nous effectuons deux études empiriques sur des données uniques fournies par le régulateur français. Nous avons accès à l’ensemble des ordres et transactions des actifs du CAC 40, à l’échelle de la microseconde, avec par ailleurs les identités des acteurs impliqués. Nous commençons par comparer le comportement des traders haute fréquence à celui des autres intervenants, notamment pendant les périodes de stress, en termes de provision de liquidité et d’activité de négociation. Nous approfondissons ensuite notre analyse en nous focalisant sur les ordres consommant la liquidité. Nous étudions leur impact sur le processus de formation des prix et leur contenu informationnel selon les différentes catégories de flux : traders haute fréquence, participants agissant pour compte client et participants agissant pour compte propre.Dans la seconde partie, nous proposons trois modèles multi-agents. À l’aide d’une approche à la Glosten-Milgrom, nous parvenons avec notre premier modèle à construire l’ensemble du carnet d’ordres (spread et volume disponible à chaque prix) à partir des interactions entre trois types d’agents : un agent informé, un agent non informé et des teneurs de marché. Ce modèle nous permet par ailleurs de développer une méthodologie de prédiction du spread en cas de modification du pas de cotation et de quantifier la valeur de la priorité dans la file d’attente. Afin de se concentrer sur une échelle individuelle, nous proposons une deuxième approche où les dynamiques spécifiques des agents sont modélisées par des processus de type Hawkes non linéaires et dépendants de l’état du carnet d’ordres. Dans ce cadre, nous sommes en mesure de calculer en fonction des flux individuels plusieurs indicateurs pertinents relatifs à la microstructure. Il est notamment possible de classer les teneurs de marché selon leur contribution propre à la volatilité. Enfin, nous introduisons un modèle où les fournisseurs de liquidité optimisent leurs meilleurs prix à l’achat et à la vente en fonction du profit qu’ils peuvent générer et du risque d’inventaire auquel ils sont confrontés. Nous mettons alors en évidence théoriquement et empiriquement une nouvelle relation importante entre inventaire et volatilitéThis thesis is made of two related parts. In the first one, we study the empirical behaviour of high-frequency traders on European financial markets. We use the obtained results to build in the second part new agent-based models for market dynamics. The main purpose of these models is to provide innovative tools for regulators and exchanges allowing them to design suitable rules at the microstructure level and to assess the impact of the various participants on market quality.In the first part, we conduct two empirical studies on unique data sets provided by the French regulator. It covers the trades and orders of the CAC 40 securities, with microseconds accuracy and labelled by the market participants identities. We begin by investigating the behaviour of high-frequency traders compared to the rest of the market, notably during periods of stress, in terms of liquidity provision and trading activity. We work both at the day-to-day scale and at the intra-day level. We then deepen our analysis by focusing on liquidity consuming orders. We give some evidence concerning their impact on the price formation process and their information content according to the different order flow categories: high-frequency traders, agency participants and proprietary participants.In the second part, we propose three different agent-based models. Using a Glosten-Milgrom type approach, the first model enables us to deduce the whole limit order book (bid-ask spread and volume available at each price) from the interactions between three kinds of agents: an informed trader, a noise trader and several market makers. It also allows us to build a spread forecasting methodology in case of a tick size change and to quantify the queue priority value. To work at the individual agent level, we propose a second approach where market participants specific dynamics are modelled by non-linear and state dependent Hawkes type processes. In this setting, we are able to compute several relevant microstructural indicators in terms of the individual flows. It is notably possible to rank market makers according to their own contribution to volatility. Finally, we introduce a model where market makers optimise their best bid and ask according to the profit they can generate from them and the inventory risk they face. We then establish theoretically and empirically a new important relationship between inventory and volatility
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Meenagh, David. "Modelling monetary policy and financial markets." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/55154/.
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Andruszkiewicz, Grzegorz. "Modelling animal spirits in financial markets." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24868.
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The term 'animal spirits' was introduced by Keynes to describe the entrepreneur's often irrational optimism and drive to act as opposed to basing decisions on formal analysis. This PhD thesis provides an analysis, both theoretical and empirical, of this phenomenon in the financial markets from several points of view. In the first chapter we show that the pricing kernel in the economy may be represented in a probabilistic form, as a solution to a stochastic filtering problem. The noise in the associated information process may contain drift term that is impossible to estimate from current market prices of assets. This drift can be associated with 'animal spirits' driving the market. The second chapter is explicitly devoted to 'animal spirits': it introduces a factor based risk-management model for an illiquid project. We show that behavioural factors together with the collateralization mechanism often employed by banks not only increase the risk for the banking system, but also introduce anomalies during high-volatility crisis periods. In the third chapter we apply Hidden Markov Models to estimate animal spirits from historic asset prices. We argue that an arbitrary addition of a stress scenario to the model can greatly improve risk estimation. The last chapter deals with optimal investment problem in a model with behavioural factors. This may be linked to the pricing kernel discussion from the first chapter by the marginal utility maximisation approach to pricing derivatives.
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Leemans, Vasco. "Modelling local order book dynamics in financial markets." Thesis, University of Cambridge, 2007. https://www.repository.cam.ac.uk/handle/1810/252052.
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Birch, Jenna. "Modelling financial markets using methods from network theory." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2028739/.
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This thesis discusses how properties of complex network theory can be used to study financial time series, in particular time series for stocks on the DAX 30. First, we make a comparison between three correlation-based networks: minimum spanning trees; assets graphs and planar maximally filtered graphs. A series of each of these network types is created for the same dataset of time series' of DAX 30 stocks and we consider what information each network can provide about the relationship between the stock prices from the underlying time series. We also analyse two specific time periods in further detail - a period of crisis and a period of recovery for the German economy. Next, we look at the structure and representations of planar maximally filtered graphs and in particular we consider the vertices that form the 3-cliques and 4-cliques [Tumminello et al. (2005)] state '... normalizing quantities are n_s - 3 for 4-cliques and 3n_s - 8 for 3-cliques. Although we lack a formal proof, our investigations suggest that these numbers are the maximal number of 4-cliques and 3-cliques, respectively, that can be observed in a PMFG of n_s elements.' Within this thesis we provide a proof for these quantities and a different construction algorithm. Finally, rather than correlation-based networks, we discuss two relatively new types of networks: visibility graphs and the geometrically simpler horizontal visibility graphs. We review the field's that these networks have already been applied to and consider if this is an appropriate method to apply to financial time series - specifically stock prices. We also consider using horizontal visibility graphs as a method for distinguishing between random and chaotic series within stock price time series.
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Lips, Johannes [Verfasser]. "Econometric Modelling of Energy & Financial Markets / Johannes Lips." Gießen : Universitätsbibliothek, 2019. http://d-nb.info/1199811742/34.
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Mahieu, Ronaldus Johannes. "Financial market volatility statistical models and empirical analysis /." Maastricht : Maastricht : Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 1995. http://arno.unimaas.nl/show.cgi?fid=8347.
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Oh, Ji Yeol Jimmy. "Essays on modelling financial markets with ambiguity and liquidity constraints." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610002.
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Iles, R. J. "Financial modelling and derivative pricing in the energy markets with jump processes." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543458.
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McIntyre-Bahatty, Yasen Timothy. "Neural network modelling, evaluation and end-user orientation in the financial markets." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389130.
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So, Ka-pui, and 蘇家培. "On the statistical modelling of stochastic volatility and its applications to financial markets." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31235311.
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Booth, Ash. "Automated algorithmic trading : machine learning and agent-based modelling in complex adaptive financial markets." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/397453/.
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Over the last three decades, most of the world's stock exchanges have transitioned to electronic trading through limit order books, creating a need for a new set of models for understanding these markets. In this thesis, a number of models are described which provide insight into the dynamics of modern financial markets as well as providing a platform for optimising trading and regulatory decisions. The first part of this thesis proposes an autonomous system that uses novel machine learning techniques to predict the price return over well documented seasonal events and uses these predictions to develop a profitable trading strategy. The DAX, FTSE 100 and S&P 500 are explored for the presence of seasonality events before an automated trading system based on performance weighted ensembles of random forests is introduced and shown to improve the profitability and stability of trading such events. The performance of the models is analysed using a large sample of stocks and the results show that the system described in this section produces superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. The second part of this thesis explores price impact. For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This section of the thesis proposes an adaptation of the system introduced in the ?rst part for predicting the price impact of order book events. The system's performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process. The final part of this thesis addresses the requirement for testing algorithmic trading strategies laid out in the Markets in Financial Instruments Directive (MiFID) II by describing an agent-based simulation. Five types of agent operate in a limit order market producing a model that is able to reproduce a number of stylised market properties including: clustered volatility, autocorrelation of returns, long memory in order flow, concave price impact and the presence of extreme price events. The model is found to be insensitive to reasonable parameter variations. Finally, the model is used to explore how trading strategy affects the implementation shortfall of trading a large order. A number of execution strategies with various order types, are evolved and evaluated in the agent-based market. It is shown that the evolved strategies outperform the simple, well known strategies significantly, suggesting that execution strategy plays an important role in determining the implementation shortfall of trading large orders.
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Nissen, Franciscus Gertruda Josephus Anna. "International financial market dynamics an empirical investigation of exchange rates, interest rates and stock returns /." Maastricht : Maastricht : Universiteit Maastricht ; University Library, Maastricht University [Host], 1997. http://arno.unimaas.nl/show.cgi?fid=6818.
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Liu, F. "Advanced quantitative modelling and analysis of anomalies on financial markets : feedback trading and realized volatility." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3011878/.
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This thesis is mainly concerned with two broad topics: i) the impact of country ETF’s premiums and discounts over feedback trading; ii) modelling high-frequency realized volatility on liquid assets. Out of the first topic, it investigates whether feedback trading exists in US-listed country ETFs and whether it varies with their observed/forecast premiums and discounts by using a sample of twenty country ETFs for the 2000-2016 window, it shows that feedback trading is present in several of them, particularly those targeting Asia Pacific markets. For the second topic, it analyses the forecastability and tradability of realized volatility on financial markets, specifically, major stock indices and their tradable derivatives are used to help decision makers in taking better hedging or trading positions in the short term. This thesis also extends the study on energy commodities which do not have their own (implied) volatility futures to trade. A heterogenous autoregressive model including jumps is used to model realized volatility, additionally, recurrent neural networks and a hybrid model are also added to the toolbox. It has been noticed, that the linear heterogeneous autoregressive model produces on average the most stable results.
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Matassini, Lorenzo. "Signal analysis and modelling of non-linear non-stationary phenomena from human voice to financial markets /." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963273256.
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Kaval, Katsiaryna. "Aspects of link-save trading in markets with frictions and financial modelling using hidden Markov models." Thesis, University of Glasgow, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419174.
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Chatziantoniou, Ioannis. "Essays on macroeconometric modelling : housing and financial markets in the light of inflation targeting monetary policy : evidence from the United Kingdom." Thesis, University of Portsmouth, 2013. https://researchportal.port.ac.uk/portal/en/theses/essays-on-macroeconometric-modelling(56288b70-6135-4ad6-9ebe-6a13602bd747).html.
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The aim of this study is to present four essays related to the macroeconometric modelling of specific relations within the economy of the United Kingdom for the period 1992-2012. The focal point of these essays is the link between inflation targeting monetary policy decision making and housing or financial prices. In particular, we investigate whether traditional channels of monetary policy are still in effect under the adopted monetary policy regime. At the same time, findings associated with the specific relation between both asset markets or with the various working assumptions which facilitate our investigation are also reported. The specific econometric methods employed include the development of structural vector autoregressive (SVAR), Markov regime-switching, as well as, multivariate generalised autoregressive conditionally heteroskedastic (MGARCH) models. The formulation of these models is predicated upon the selection of appropriate approximations for all financial and macroeconomic indicators of interest. The main findings of the first essay suggest that under the inflation targeting monetary policy regime, innovations in the monetary policy instrument have no direct effect on the stock market as previously suggested by traditional channels of monetary policy. The said innovations though, appear to have a significant negative impact on the housing market. Furthermore, variation in the stock market can be explained by innovations in the housing market. Turning to the second essay, prominent among our results is the fact that innovations in fiscal policy have a significantly negative effect on the stock market (direct impact). In addition, the effects of monetary policy on the stock market also become negative (indirect impact). According to the third essay when both the stock and the housing market are in a highly volatile regime, then contractionary monetary policy pushes both markets to remain at that regime. Finally, the main outcome from the fourth essay is that the time-varying correlation between monetary policy and housing or financial prices becomes stronger during turbulent times. Overall, our findings suggest that within an inflation targeting monetary policy regime the effects of monetary policy decisions on the stock market strongly depend on the broader economic conditions. By contrast, traditional monetary policy channels with respect to the housing market appear to be in effect; however, broader economic conditions have a key role to play in this case as well.
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Mari, Konstantina. "Essays in financial economics : option pricing, behavioural finance, stochastic terms in modelling, and relationships between sectors within stock markets." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/17419/.
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Finance Theory is built mainly based on the assumption that investors are risk neutral. We run two finance experiments to test American option pricing theory. Dynamic programming and optimal stopping theory are used for the construction of the models. Simulation studies are conducted for the experiments. In the first experiment, we test a disinvestment case of real options theory in discrete time. Our results show that risk aversion explains better the decisions of the participants than risk neutrality as few of the participants appeared to be risk neutral. Furthermore, risk aversion explains better the behaviour of the subjects than myopic behaviour. In the second experiment, we examine the timing of the exercising of an American call option contract in continuous time. We estimate the risk aversion parameters of the subjects from the main experiment, and we elicit their risk aversion parameters by running another small experiment with allocation questions. Based on these parameters we find the estimated and the elicited risk averse optimal trigger respectively. From our analysis we show that the estimated risk averse optimal trigger explains better the actual stopping decisions of the subjects, while the risk neutral optimal trigger has the next highest explanatory power and the elicited risk optimal trigger the lowest. The third project tests the stochastic assumptions underlying an analysis by examining the statistical properties of the estimated parameters and comparing them to the actual values. This study shows that the stochastic specification underlying any analysis matters for the interpretation of its results. In the last project, we check the interdependency relationships among the five major market sectors of Greece, Italy and Portugal. By using dependency tests, such as Johansen cointegration and Granger causality tests, we find that the Greek sectors provide some diversification benefits for sector-level investments, while the opposite is true for the Italian sectors. Only in the case of Portugal do the results suggest non-existence of interdependency relationships among the sectors for the first sub-period of the total period we examine and their existence later. Moreover, by using the variance decomposition and the time-varying volatility methodologies we conclude that for all the three countries the majority of the sectors are exogenous and their volatility is highly increased due to crisis, particularly in the case of the Financials sector.
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Niklewski, Jacek. "Multivariate GARCH and portfolio optimisation : a comparative study of the impact of applying alternative covariance methodologies." Thesis, Coventry University, 2014. http://curve.coventry.ac.uk/open/items/a8d7bf49-198d-49f2-9894-12e22ce2d7f1/1.
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This thesis investigates the impact of applying different covariance modelling techniques on the efficiency of asset portfolio performance. The scope of this thesis is limited to the exploration of theoretical aspects of portfolio optimisation rather than developing a useful tool for portfolio managers. Future work may entail taking the results from this work further and producing a more practical tool from a fund management perspective. The contributions made by this thesis to the knowledge of the subject are that it extends literature by applying a number of different covariance models to a unique dataset that focuses on the 2007 global financial crisis. The thesis also contributes to the literature as the methodology applied also enables a distinction to be made in respect to developed and emerging/frontier regional markets. This has resulted in the following findings: First, it identifies the impact of the 2007–2009 financial crisis on time-varying correlations and volatilities as measured by the dynamic conditional correlation model (Engle 2002). This is examined from the perspective of a United States (US) investor given that the crisis had its origin in the US market. Prima facie evidence is found that economic structural adjustment has resulted in long-term increases in the correlation between the US and other markets. In addition, the magnitude of the increase in correlation is found to be greater in respect to emerging/frontier markets than in respect to developed markets. Second, the long-term impact of the 2007–2009 financial crisis on time-varying correlations and volatilities is further examined by comparing estimates produced by different covariance models. The selected time-varying models (DCC, copula DCC, GO-GARCH: MM, ICA, NLS, ML; EWMA and SMA) produce statistically significantly different correlation and volatility estimates. This finding has potential implication for the estimation of efficient portfolios. Third, the different estimates derived using the selected covariance models are found to have a significant impact on the calculated weights and turnovers of efficient portfolios. Interestingly, however, there was no significant difference between their respective returns. This is the main finding of the thesis, which has potentially very important implications for portfolio management.
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Ozel, Bulent. "Designing scalable and stock-flow-consistent agent-based models: Policy scenarios and experiments on housing markets, monetary unions and interbank networks." Doctoral thesis, Universitat Jaume I, 2019. http://hdl.handle.net/10803/666909.
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The recent debates in economics, following the 2008 crisis, have pointed out a necessity for micro-founded macroeconomic modelling approaches for policy analyses. Agent based models have been adopted to address two underlining aspects of a micro-founded macroeconomic approach.
This dissertation as a whole is an effort at fulfilling this necessity. It is composed of a number of interrelated studies. Specific research questions are raised around the debates on monetary unions, housing markets and interbank networks. The overall objective in these works is to be able to address policy questions while employing sound and reusable stock-flow-consistent models. A common methodological practice is elicited at reaching this objective: delineating the design of a top-down policy experimentation set-up from the design of individual agent behaviors for a bottom up emergence first, and then coupling them back to reach a conceptual coherence between the policy issue and the assumptions on agents’ behavioral choices.Los recientes debates en economía tras la crisis de 2008, han señalado la necesidad de utilizar modelos macroeconómicos micro fundados para el análisis de políticas. Se han utilizado modelos basados en agentes para abordar dos aspectos destacados dentro de los modelos macroeconómicos micro fundados. Esta tesis es un esfuerzo para satisfacer esta necesidad. Se compone de una serie de es tudios interrelacionados. En ella se plantean cuestiones específicas en torno a los debates sobre uniones monetarias, mercados de vivienda y redes interbancarias. El objetivo general de estos trabajos es poder abordar diferentes cuestiones de política económica, a la vez que se utilizan modelos sólidos y stock-flujo consistentes reutilizables. Se utiliza una misma metodología para lograr este objetivo: primero, delinear un entorno de experimentación de políticas de arriba hacia abajo a partir del diseño de comportamientos de agentes individuales para una emergencia ascendente, para luego unirlos de nuevo para alcanzar una coherencia conceptual entre la cuestión de política introducida y los supuestos sobre las elecciones de comportamiento de los agentes.
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RILLOSI, FRANCESCO. "Modelli a generazioni sovrapposte per due paesi con un mercato finanziario integrato." Doctoral thesis, Università Cattolica del Sacro Cuore, 2013. http://hdl.handle.net/10280/1954.
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La tesi, costituita da due parti e tre capitoli, si concentra sulle conseguenze macroeconomiche della globalizzazione, prendendo in considerazione vari schemi di un modello a generazioni sovrapposte, in un mercato finanziario integrato. Per ipotesi, gli agenti vivono per due periodi e sono divisi in due gruppi: i "vecchi" che posseggono il fattore capitale e i "giovani" che offrono lavoro e risparmio. Nella prima parte si suppone che i mercati siano perfetti. Dopo aver ricevuto il loro reddito, i giovani ottimizzano consumo e risparmio. Diverse ipotesi vengono avanzate sull'apertura dei mercati, ma le economie convergono sempre verso uno stato stazionario asintoticamente stabile. Nella seconda parte i mercati finanziari sono imperfetti e i prestiti sono razionati. I giovani agenti risparmiano tutto il loro reddito e consumano solo nel secondo periodo della loro vita. In queste nuove ipotesi si possono riscontrare dinamiche endogene di tipo periodico.The essay, made by two parts and three chapters, focuses on macroeconomic effects of globalization, considering various schemes of a two-country OLG model with integrated financial market. For hypothesis, agents live for two periods and are divided in two groups: the "old" that own the capital factor and the "young" that supply labor and savings. In the first part markets are supposed to be perfect. After received their income, the young optimize their consumption and savings. Different hypotheses about the opening markets are considered, but the economies ever converge to an asymptotically stable steady state. In the second part the financial markets are imperfect and borrowing is constrained. The young agents save all their income and consume only in the second period of their life. In these new hypotheses endogenous, periodic dynamics may occur.
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Penm, Jack H. W. "Time-series modelling in financial markets : new approaches and exchange rate applications." Phd thesis, 2001. http://hdl.handle.net/1885/146094.
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Makhwiting, Monnye Rhoda. "Modelling volatility and financial market risks of shares on the Johannesburg Stock Exchange." Thesis, 2014. http://hdl.handle.net/10386/1389.
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Thesis (M.Sc. (Statistics)) -- University of Limpopo. 2014A number of previous research studies have investigated volatility and financial risks in the ermeging markets. This dissertation investigates stock returns volatility and financial risks in the Johannesburg Stock Exchange (JSE). The investigation is con- ducted in modelling volatility using Autoregressive Moving Average-Generalised Au- toregressive Conditional Heteroskedastic (ARMA-GARCH)-type models. Daily data of the log returns at the JSE over the period 08 January, 2002 to 30 December, 2011 is used. The results suggest that daily returns can be characterised by an ARMA (1, 0) process. Empirical results show that ARMA (1, 0)-GARCH (1, 1) model achieves the most accurate volatility forecast. Modelling tail behaviour of rare and extreme events is an important issue in the risk management of a financial portfolio. Extreme Value Theory (EVT) is applied to quantify upper extreme returns. Generalised Ex- treme Value (GEV) distribution is used to model the behaviour of extreme returns. Empirical results show that the Weibull distribution can be used to model stock re- turns on the JSE. In using the Generalised Pareto Distribution (GPD), the modelling framework used accommodates ARMA and GARCH models. The GPD is applied to ARMA-GARCH filtered returns series and the model is referred to as the ARMA- GARCH-GPD model. The threshold value is estimated using Pareto quantile plot while peak-over-threshold approach is used to model the upper extreme returns. In general, the ARMA-GARCH-GPD model produces more accurate estimates of ex- treme returns than the ARMA-GARCH model. The out of sample forecast indicates that the ARMA (1, 3)-GARCH (1, 1) model provides the most accurate results.
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(9811184), Galina Korotkikh. "A computational approach in dealing with uncertainty of financial markets." Thesis, 2002. https://figshare.com/articles/thesis/A_computational_approach_in_dealing_with_uncertainty_of_financial_markets/21723323.
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Financial markets play a very important role in our life. During last decades a substantial progress has been made in their understanding. A main result of these developments is the change from random walk models to models that view the financial market as a complex dynamical system. Recently, it is discovered that financial markets have non-random modes.
Non-random modes are significant in dealing with uncertainty of financial markets. However, understanding of non-random modes is limited and there is no fundamental theory about them in general. Currently, intensive investigations are underway to change the situation. A contribution to the solution of these problems is made in the thesis. In particular, a computational approach to characterise and quantify non-random modes of the financial market is developed. The development is realised to a stage where the approach may be tested and used for real data of financial markets.
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Alic, Irina. "Decision Support Systems for Financial Market Surveillance." Doctoral thesis, 2016. http://hdl.handle.net/11858/00-1735-0000-002B-7D04-4.
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Entscheidungsunterstützungssysteme in der Finanzwirtschaft sind nicht nur für die Wis-senschaft, sondern auch für die Praxis von großem Interesse. Um die Finanzmarktüber-wachung zu gewährleisten, sehen sich die Finanzaufsichtsbehörden auf der einen Seite, mit der steigenden Anzahl von onlineverfügbaren Informationen, wie z.B. den Finanz-Blogs und -Nachrichten konfrontiert. Auf der anderen Seite stellen schnell aufkommen-de Trends, wie z.B. die stetig wachsende Menge an online verfügbaren Daten sowie die Entwicklung von Data-Mining-Methoden, Herausforderungen für die Wissenschaft dar. Entscheidungsunterstützungssysteme in der Finanzwirtschaft bieten die Möglichkeit rechtzeitig relevante Informationen für Finanzaufsichtsbehörden und Compliance-Beauftragte von Finanzinstituten zur Verfügung zu stellen. In dieser Arbeit werden IT-Artefakte vorgestellt, welche die Entscheidungsfindung der Finanzmarktüberwachung unterstützen. Darüber hinaus wird eine erklärende Designtheorie vorgestellt, welche die Anforderungen der Regulierungsbehörden und der Compliance-Beauftragten in Finan-zinstituten aufgreift.
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Voev, Valeri [Verfasser]. "Three essays on estimation and dynamic modelling of multivariate market risks using high frequency financial data / vorgelegt von Valeri Voev." 2008. http://d-nb.info/988092379/34.
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Minkner, Benedict Philipp. "Financial implications of product channel scenarios: a production of an excel tool for oceano fresco." Master's thesis, 2021. http://hdl.handle.net/10362/122920.
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This work addresses market entry decision making by management under significant uncertainty in a real business case. It was developed in close collaboration with a Portuguese early-stage, food technology company. In the context of its market entry plans for 2021, the company’s management is challenged to find a product channel mix that best serves its financial situation and objectives. In this context, I built an extensive Excel-based finance tool that enables the management of the company to plan product channel scenarios and to observe their implications on profits, cash flows, company valuation, and market shares.
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Liu, Chun. "Modelling volatility in financial markets." 2007. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=742088&T=F.
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Alfeus, Mesias. "Stochastic modelling of new phenomena in financial markets." Thesis, 2019. http://hdl.handle.net/10453/134047.
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University of Technology Sydney. Faculty of Business.The Global Financial Crisis (GFC) has revealed a number of new phenomena in financial markets, to which stochastic models have to be adapted. This dissertation presents two new methodologies, one for modeling the “basis spread”, and the other for “rough volatility”. The former gained prominence during the GFC and continues to persist, while the latter has become increasingly evident since 2014. The dissertation commences with a study of the interest rate market. Since 2008, in this market we have observed “basis spreads” added to one side of the single-currency floating-for-floating swaps. The persistence of these spreads indicates that the market is pricing a risk that is not captured by existing models. These risks driving the spreads are closely related to the risks affecting the funding of banks participating in benchmark interest rate panels, specifically “roll-over” risk, this being the risk of not being able to refinance borrowing at the benchmark interest rate. We explicitly model funding liquidity and credit risk, as these are the two components of roll-over risk, developing first a model framework and then considering a specific instance of this framework based on affine term structure models. Subsequently, another specific instance of the model of roll-over risk is constructed sing polynomial processes. Instead of pricing options through closed-form expressions for conditional moments with respect to observed process, the price of a zero-coupon bond is expressed as a polynomial of a finite degree in the sense of Cheng & Tehranchi (2015). A formula for discrete-tenor benchmark interest rates (e.g., LIBOR) under roll-over risk is constructed, which depends on the quotient of polynomial processes. It is shown how such a model can be calibrated to market data for the discount factor bootstrapped from the overnight index swap (OIS) rate curve. This is followed by a chapter in which a numerical method for the valuation of financial derivatives with a two-dimensional underlying risk is considered, in particular as applied to the problem of pricing spread options. As is common, analytically closed-form solutions for pricing these payoffs are unavailable, and numerical pricing methods turn out to be non-trivial. We price spread options in a model where asset prices are driven by a multivariate normal inverse Gaussian (NIG) process. We consider a pricing problem in the fixed-income market, specifically, on cross-currency interest rate spreads and on LIBOR-OIS spreads. The final contribution in this dissertation tackles regime switching in a rough-volatility Heston model, which incorporates two important features. The regime switching is motivated by fundamental economic changes, and a Markov chain to model the switches in the long-term mean of the volatility is proposed. The rough behaviour is a more local property and is motivated by the stylized fact that volatility is less regular than a standard Brownian motion. The instantaneous volatility process is endowed with a kernel that induces rough behaviour in the model. Pricing formulae are derived and implemented for call and put options using the Fourier-inversion formula of Gil-Pelaez (1951).
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Silva, Bernardo Rui Vaz. "Predicting and distinguishing bankruptcy: an application of a market and hybrid model to US publicly listed firms from 2008 to 2018." Master's thesis, 2020. http://hdl.handle.net/10071/21449.
Full textAbstract:
Assessing the probability of bankruptcy has been a key topic approached by researchers
and academics throughout the last half century. The bankruptcy of considerable firms, such as
Enron or WorldCom, coupled with the rigorous regulatory environment triggered by Basel II
guidelines, fostered even further the interest in the topic.
Moreover, in the outcome of financial crisis, Credit Rating Agencies were criticized for
addressing inflated ratings and not properly anticipating defaults. Besides, leading CRA’s do
not assess the creditworthiness of all firms, and our intention is to provide to individual investor
the best option available to autonomously estimate the probability of bankruptcy
We analyse if either a market-based model, KMV, or a hybrid model, CHS, are able to properly
anticipate the event of bankruptcy, and in case this is verified, which of them better distinguish
between bankrupt and non-bankrupt firms. In order to do so, we resort to a sample of 354 US
publicly listed firms, divided into bankrupt and non-bankrupt firms, and applied the ROC
technique to assess our results, for a 10-year period.
Our results prove that KMV model is slightly superior to the CHS model at maximizing the
Area Under the Curve (AUC). Besides, it provided a higher optimal probability’s cut off point
that distinguish both type of firms. Our results indicate that the KMV model is the best option
available for an individual investor to assess the probability of default, given the results
achieved and the easiness of application when compared to the CHS model.A avaliação da probabilidade de falência tem sido um tema-chave abordado por investigadores e académicos ao longo do último meio século. A falência de empresas consideráveis como a Enron ou a WorldCom, aliada ao rigoroso ambiente regulamentar desencadeado pelas diretrizes de Basileia II, fomentou ainda mais o interesse pelo tema. Além disso, na sequência da crise financeira, as agências de notação de crédito (ANC) foram criticadas por endereçarem notações inflacionadas e não anteciparem corretamente os incumprimentos. Ademais, as principais ANC não avaliam todas as empresas, e a nossa intenção é proporcionar ao investidor individual a melhor opção disponível para estimar autonomamente a probabilidade de falência. Neste estudo analisou-se se um modelo baseado em dados de mercado, o KMV, e um modelo híbrido, o CHS, diferenciam o evento de falência e, caso isso seja verificado, qual deles melhor distingue entre empresas falidas e não falidas. Para tal, recorremos a uma amostra de 354 empresas cotadas nos EUA, divididas em empresas falidas e não falidas, aplicando a técnica estatística "ROC", num período de 10 anos. Os nossos resultados sugerem que o modelo KMV é ligeiramente superior ao modelo CHS, maximizando a área sob a curva (AUC). Além disso, o primeiro proporcionou um ponto de corte de probabilidade mais elevado que distingue ambos os tipos de empresas. Os nossos resultados indiciam que o KMV é a melhor opção disponível para um investidor individual avaliar a probabilidade de incumprimento, dado os resultados alcançados e a facilidade de aplicação em comparação com o modelo CHS.
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Antwi, Albert. "Profit risk models for South African banking sector." Diss., 2016. http://hdl.handle.net/11602/767.
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