Relevant bibliographies by topics / Financial market modelling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Financial market modelling'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Financial market modelling"

1

Platen, Eckhard, and Rolando Rebolledo. "Principles for modelling financial markets." Journal of Applied Probability 33, no. 3 (September 1996): 601–13. http://dx.doi.org/10.2307/3215342.

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The paper introduces an approach focused towards the modelling of dynamics of financial markets. It is based on the three principles of market clearing, exclusion of instantaneous arbitrage and minimization of increase of arbitrage information. The last principle is equivalent to the minimization of the difference between the risk neutral and the real world probability measures. The application of these principles allows us to identify various market parameters, e.g. the risk-free rate of return. The approach is demonstrated on a simple financial market model, for which the dynamics of a virtual risk-free rate of return can be explicitly computed.
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Platen, Eckhard, and Rolando Rebolledo. "Principles for modelling financial markets." Journal of Applied Probability 33, no. 03 (September 1996): 601–13. http://dx.doi.org/10.1017/s002190020010004x.

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The paper introduces an approach focused towards the modelling of dynamics of financial markets. It is based on the three principles of market clearing, exclusion of instantaneous arbitrage and minimization of increase of arbitrage information. The last principle is equivalent to the minimization of the difference between the risk neutral and the real world probability measures. The application of these principles allows us to identify various market parameters, e.g. the risk-free rate of return. The approach is demonstrated on a simple financial market model, for which the dynamics of a virtual risk-free rate of return can be explicitly computed.
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Iovane, Gerardo, Antonio Briscione, and Elmo Benedetto. "Financion: A quantum approach to financial market modelling." Journal of Statistics and Management Systems 24, no. 5 (July 4, 2021): 1127–49. http://dx.doi.org/10.1080/09720510.2021.1930665.

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Umurzakov, Sardor. "Business Process Management in Financial and Non-Financial Institutions: Payment Process Modelling in Financial Flows Management." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 3, no. 5 (2017): 50–54. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.35.1006.

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Business process management is a progressively developing area of science, which is seen as the most modern and forward-looking innovative. Modern business operations remain highly dependent on IT solutions to steer the processes. Business process management solutions have been the clue for easing daily business operations. IT solutions have actively penetrated the working environment in all areas of business, especially the financial sector. It is beyond to imagine modern financial markets and institutions without IT software support. Not only billing, calculation and payment processes, even stock pricing, market analysis and risk monitor tools are fully computerized through business process modeling. This article studies the current role of business process management in the sample of internal payment and transaction in non-financial and financial firms.
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Ahelegbey, Daniel Felix. "Statistical Modelling of Downside Risk Spillovers." FinTech 1, no. 2 (April 1, 2022): 125–34. http://dx.doi.org/10.3390/fintech1020009.

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We study the sensitivity of stock returns to the tail risk of major equity market indices, including the G10 countries. We model the sensitivity relationship via extreme downside hedging and estimate the parameters via a Bayesian graph structural learning method. The empirical application examines whether downside risk connections among the major stock markets are merely anecdotal or provide a signal of contagion and the nature of sensitivity among major equity markets during the global financial crisis and the coronavirus pandemic. The result showed that the COVID-19 crisis recorded the historically highest spike in the downside risk interconnectedness among the major equity market indices, suggesting higher financial market vulnerability in the coronavirus pandemic than during the global financial crisis.
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Ivanović, Zoran, and Vanja Ivanović. "OPTIMAL FINANCIAL STRUCTURE MODELLING." Tourism and hospitality management 8, no. 1-2 (May 23, 2017): 1–12. http://dx.doi.org/10.20867/thm.8.1-2.1.

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This article aims at defining a model of optimal financial structure, in the borders of a wide financial business problematics and a development of enterprises in the conditions of developed money market and capital. Special accent is given on slurping optimal structure of capital by using numerous possibilities that financial manager has in modem financial society. Modem financial society enables flexibility in modelling various optimal capital structures in enterprises.
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McGough, Tony, and Jim Berry. "Pricing risk and its use in modelling real estate market yields." Journal of Property Investment & Finance 38, no. 5 (August 3, 2020): 419–33. http://dx.doi.org/10.1108/jpif-08-2019-0111.

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Purpose In the light of past financial and economic turmoil, there has been a marked increase in the volatility in real estate markets. This has impacted on the pricing of property assets, partly through market sentiment and particularly concerning risk. It also limits modelling accuracy model accuracy. The purpose of this paper is to create a new variable and model to enhance analysis of what drives real estate yields incorporating market sentiment to risk. Design/methodology/approach This paper specifically considers the modelling of property pricing within a volatile economic environment. The theoretical context begins by analysing the relationship between property yields and government bonds. The analytical context then moves on to specifically include a measurement of risk which stresses its role and importance in investment markets since the Global Financial Crisis. The model thus incorporates macroeconomic and real estate data, together with an international risk multiplier, which is calculated within the paper. Findings The paper finds the use of measurements of market sentiment and risk are more powerful tools for modelling yields than previous techniques alone. Research limitations/implications This is an initial paper outlining the creation of sentiment and risk measurements in the financial market and showing an example of its application to a commercial real estate market. The implication is that this could add a major new explanatory variable to modelling of yields. Practical implications The paper highlights the importance of risk in the pricing of commercial real estate, over and above normal variables. It highlights how this can help explain over and undershooting of yields within commercial real estate which would be of great importance in the investment world. Originality/value This paper attempts to explicitly measure market sentiment, pricing of risk and how this impacts real estate pricing.
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Domoto, Eri, Koji Okuhara, and Antonio Oliveira Nzinga Rene. "Market Forecasting by Variable Selection of Indicators and Emotion Scores from Text Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 3 (May 20, 2022): 382–92. http://dx.doi.org/10.20965/jaciii.2022.p0382.

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The size of financial markets has become huge, research on mechanisms is becoming more critical, and research is progressing. In addition to research on financial market modelling, there have been increasing attempts to clarify the mechanism of the Financial Markets Commission by associating markets with events that occur outside the market. Therefore, in this study, we investigate the effects of factors outside the market on exchange rates and consider the mechanisms necessary to consider the effects of automatic trading. We propose an analysis method for automatic trading of foreign exchange that considers external and internal factors in the market.
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Niyitegeka, Olivier, and Dev D. Tewari. "Modelling Financial Contagion in the South African Equity Markets Following the Subprime Crisis." Journal of Economics and Behavioral Studies 10, no. 6A (January 16, 2019): 164–76. http://dx.doi.org/10.22610/jebs.v10i6a.2672.

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This paper used wavelet analysis and Dynamic Conditional Correlations model derived from the Multivariate Autoregressive Conditional Heteroskedasticity (MGARCH-DCC) to investigate the possible presence of financial contagion in the South African equity market in the wake of the subprime crisis that occurred in the United States. The study uses Dornbusch, Park and Claessens’s (2000) broader definition which asserts that financial contagion only takes place if cross-correlation between two markets is relatively low during the tranquil period, and that a crisis in one market results in a substantial increase cross-market correlation. Using wavelet analysis, the study found high levels of correlation during the subprime financial crisis in both smaller and longer timescales. In the former, high correlation was identified as financial contagion, whereas in the latter it was found to indicate co-movement due to financial fundamentals. The high correlation was identified for small scales 3, 4 and 5 that range from a week to one month indicates the presence of contagion. The study also used the MGARCH-DCC model to compare the cross-market correlation between the SA and the US markets, during a ‘pre-crisis’ and ‘crisis’ period. The study used data for the period between January 2005 and December 2007 for the ‘pre-crisis’ period and that for the period from January 2008 to December 2014 for the ‘crisis’ period. The results indicate cross-market linkages only during the crisis period; hence, it was concluded that cross-market correlation during the period of financial turmoil in the US was the result of financial contagion.
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Niklis, Dimitrios, Michalis Doumpos, and Constantin Zopounidis. "Credit Risk Modelling." International Journal of Sustainable Economies Management 7, no. 3 (July 2018): 50–64. http://dx.doi.org/10.4018/ijsem.2018070105.

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The assessment of businesses' credit risk is a difficult and important process in the area of financial risk management. In a classical multivariate model, financial ratios are combined in order to achieve a credit risk score, which signals if a loan application is approved or discarded. Despite their good performance, the developed multivariate models using statistical methods have been widely criticized. They are based on models that use accounting data, which have the disadvantage of being static and so often fail to follow the changes in the economic and business environment. In recent years, market models (structural and reduced form models) have become popular among banks and financial institutions, because of their theoretical background and the use of updated information. The aim of this article is to present an overview of basic market models (structural models, reduced form models and market models used from credit institutions) together with their characteristics in order to outline their development throughout the last decades.
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Dissertations / Theses on the topic "Financial market modelling"

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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 risque
This 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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Books on the topic "Financial market modelling"

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Market practice in financial modelling. Singapore: World Scientific Pub., 2012.

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Modelling international financial markets: An empirical study. Amsterdam: Thesis Publishers, 1994.

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Chevallier, Julien, Stéphane Goutte, David Guerreiro, Sophie Saglio, and Bilel Sanhaji, eds. Financial Mathematics, Volatility And Covariance Modelling: Volume 2. Milton, Cambridge, UK: Routledge, 2019.

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Watson, Iain David. An investigation of the use of market and industry data in financial distress modelling: Basedon data derived from the unlisted securities market and offical list. [s.l: The Author], 1995.

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Goutte, Stéphane, David Guerreiro, Sophie Saglio, and Bilel Sanhaji, eds. International Financial Markets: Volume 1. London, UK: Routledge, 2019.

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Cockerline, Jon P. Multicountry modelling of financial markets. Cambridge, MA: National Bureau of Economic Research, 1988.

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Marida, Bertocchi, Cavalli Enrico, Komlósi S. 1947-, EURO Working Group on Financial Modelling. Meeting., and EURO Working Group on Financial Modelling. Meeting., eds. Modelling techniques for financial markets and bank management. Heidelberg: Physica-Verlag, 1996.

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Bertocchi, Marida, Enrico Cavalli, and Sándor Komlósi, eds. Modelling Techniques for Financial Markets and Bank Management. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-51730-3.

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Prices in financial markets. New York: Oxford University Press, 1990.

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Chaos and order in the capital markets: A new view of cycles, prices, and market volatility. 2nd ed. New York: Wiley, 1996.

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Book chapters on the topic "Financial market modelling"

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Beltratti, Andrea, and Sergio Margarita. "An Artificial Adaptive Speculative Stock Market." In Financial Modelling, 155–78. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-86706-4_9.

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Battistini, Egidio, Luigi Ferrari, and Lorenzo Peccati. "Expectations and News in an Imitative Stock-Market." In Financial Modelling, 138–54. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-86706-4_8.

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Becchetti, Leonardo, and Laura Cavallo. "Do Stock Market Anomalies Disappear? The Example of Small Size and Market-to-Book Premia at the London Stock Exchange." In Financial Modelling, 13–29. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57652-2_2.

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Corha, A., and A. Tourani Rad. "Risk Measurement and Size Effect on the Dutch Stock Market." In Financial Modelling, 286–95. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-86706-4_18.

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Sala, J. Carlos Gómez. "Stock-Split Ex-Dates: Evidence from the Spanish Stock Market." In Financial Modelling, 181–202. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57652-2_13.

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Musiela, Marek, and Marek Rutkowski. "Market Imperfections." In Martingale Methods in Financial Modelling, 87–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-22132-7_4.

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Speranza, M. G. "Linear Models for Portfolio Selection and their Application to the Milano Stock Market." In Financial Modelling, 320–33. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-86706-4_20.

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Musiela, Marek, and Marek Rutkowski. "Foreign Market Derivatives." In Martingale Methods in Financial Modelling, 159–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-22132-7_7.

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Kremer, G. H. M. J. "Tax Effects in the Dutch Bond Market." In Modelling for Financial Decisions, 63–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76761-6_5.

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Landes, Thomas, and Otto Loistl. "The Continuous Quotations at an Auction Market." In Modelling for Financial Decisions, 73–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76761-6_6.

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Conference papers on the topic "Financial market modelling"

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Serguieva, Antoaneta, Fang Liu, and Paresh Date. "Financial contagion simulation through modelling behavioural characteristics of market participants and capturing cross-market linkages." In Economics -Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/cifer.2011.5953569.

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Devine, Paul, and Rahul Savani. "A Data Rich Money Market Model - Agent-based Modelling for Financial Stability." In 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and Technology Publications, 2014. http://dx.doi.org/10.5220/0005096602310236.

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Cliff, Dave. "Simulation-based evaluation of automated trading strategies: a manifesto for modern methods." In The 19th International Conference on Modelling and Applied Simulation. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.mas.018.

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In many investment banks and major fund-management companies, automated "robot" trading systems now do work that 20 years ago would have required large numbers of human traders to perform: the rise of robot traders is a major success-story for artificial intelligence (AI) research. Although the technical details of currently profitable automated trading systems are closely guarded commercial secrets, the rise of robot trading can be traced back to a sequence of key AI research papers. Each of these key papers relied on minimal abstract simulation models of real financial markets: the simulators provide test-beds for trials in which the performance of different trading strategies could be evaluated and compared. Recent studies have revisited these seminal results, using more realistic simulations of contemporary financial markets, and have cast major doubts on core conclusions drawn in the original publications. Therefore, it seems reasonable to argue that present-day simulation methods are exposing significant problems in past research on automated trading. This position paper presents no new empirical results but instead presents a review of key past papers and an argument, a manifesto, for establishing a shared market-simulator test-bed that adequately reflects current real-world financial markets, for use in future evaluation and comparison of trading strategies.
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Rutkauskas, Aleksandras Vytautas, Nijolė Maknickienė, and Algirdas Maknickas. "Modelling of the history and predictions of financial market time series using Evolino." In The 6th International Scientific Conference "Business and Management 2010". Vilnius, Lithuania: Vilnius Gediminas Technical University Publishing House Technika, 2010. http://dx.doi.org/10.3846/bm.2010.024.

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Akduğan, Umut, and Yasemin Koldere Akın. "Volatility Modelling in Parametric Value at Risk Calculation: An Application on Pension Funds in Turkey." In International Conference on Eurasian Economies. Eurasian Economists Association, 2013. http://dx.doi.org/10.36880/c04.00713.

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Risk management has strategic importance with deepness and globalization of financial markets. Financial markets are faced with systematic or non-systematic risk factors. Risk management has a great importance for banks, as well as other financial institutions and investors.In this context, "Value at Risk (VaR)" is recommended as one of the methods to measure market risk by international organizations and Turkish Banking Regulation and Supervision Agency (BRSA). The parametric method (Variance-Covariance Method), one of the methods used in VaR calculation, used in pension funds which have reached large numbers and established by the Turkish pension companies. Moreover, this method is applied in two different ways based on the assumption of constant variance and based on the basis of conditional heteroscedasticity, and the results were compared. The accuracy of the calculations have been tested by backtesting method. For this purpose, daily returns company's growth equity fund between 03/01/2011 - 04/30/2013 is used for the four pension companies which is the maximum size of the fund and operating in Turkey.
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Freimanis, Kristaps, and Maija Šenfelde. "Approach of scaling the level of government intervention in the financial market." In 11th International Scientific Conference „Business and Management 2020“. VGTU Technika, 2020. http://dx.doi.org/10.3846/bm.2020.591.

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In the field of the economics’ regulation researchers so far have built the conceptual framework showing how the deadweight loss of market failures decrease and costs of the government intervention in-crease with the increased level of the government intervention. In order to quantify relationships between the level of intervention, intervention costs and the deadweight loss with econometric models it is im-portant to understand how to apply coordinates for the data points to be included in the modelling. The main goal of the research presented in this paper is to find the unit measure for the asis of the independentvariable, i.e. to shape the categorical scale corresponding to the level of intervention.
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Kreye, Melanie E., Yee Mey Goh, and Linda B. Newnes. "Modelling Uncertainty in Competitive Bidding." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70841.

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Due to the current market development of servitisation, manufacturing companies are required to compete through the provision of services as opposed to physical products. For such companies, the shift towards being a service provider often means they have to bid for service contracts, sometimes competitively. In the context of competitive bidding, the decision makers face various influencing uncertainties. Ignoring these uncertainties or their influences can result in problems such as the generation of too little profit or even a loss or the exposure to financial risks. Raising the decision maker’s awareness of the uncertainties can provide valuable information to assist in the decision-making process. The research presented in this paper presents an approach to modeling the uncertainties at the competitive bidding stage for long-life, high-value service contracts. The aim of this research is to provide decision makers with a decision matrix which illustrates the probability of winning the service contract and the probability of making a profit. The framework utilized for identifying the uncertainties and a layered approach for analyzing these uncertainties is described. These are then applied to a case study where the modeling approaches and data gathering methods are explained and the results are displayed via the decision matrix.
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Suhendra, Euphrasia Susy. "The Influence of Intellectual Capital on Firm Value towards Manufacturing Performance in Indonesia." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01192.

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The aim of this study is to analyse the influence of intellectual capital on firm value through firm performance (profitability, productivity, market valuation and growth). Intellectual capital is measured by using a Value Added Intellectual Coefficient (VAIC™). Firm value is measured by Tobin's Q. The financial performance consists of Return on assets (ROA), Asset turn over (ATO), Market to Book Value (MB) and Earnings per Share (EPS). Data from this study was obtained from financial statements and annual reports of manufacturing companies that are taken from the Indonesia Stock Exchange. The sample of this study is manufacturing companies listed on the Indonesia Stock Exchange during the year of 2011-2013 for 37 companies. The types of data used are secondary data in the form of annual reports by the manufacturing companies. Empirical analysis is conducted by using Structural Equation Modelling (SEM). The results of this study indicate that Intellectual capital has a significant effect on profitability, market valuation and growth. Intellectual capital does not significantly affect productivity and firm value. Market valuation significantly affects the firm value. Profitability, productivity and growth do not significantly affect firm value. Furthermore, Intellectual capital which is intervened by the firm performance has a positive effect on firm value.
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Ferreira, Nuno Rafael Barbosa, Diana Aldea Mendes, and Vivaldo Manuel Pereira Mendes. "Comparative multivariate forecast performance for the G7 Stock Markets: VECM Models vs deep learning LSTM neural networks." In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11616.

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The prediction of stock prices dynamics is a challenging task since these kind of financial datasets are characterized by irregular fluctuations, nonlinear patterns and high uncertainty dynamic changes.The deep neural network models, and in particular the LSTM algorithm, have been increasingly used by researchers for analysis, trading and prediction of stock market time series, appointing an important role in today’s economy.The main purpose of this paper focus on the analysis and forecast of the Standard & Poor’s index by employing multivariate modelling on several correlated stock market indexes and interest rates with the support of VECM trends corrected by a LSTM recurrent neural network.
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SIDKI, Marcus, David BOLL, and Harry MÜLLER. "Do Public Enterprises Manage Earnings? Evidence from Germany." In Current Trends in Public Sector Research. Brno: Masaryk University Press, 2020. http://dx.doi.org/10.5817/cz.muni.p210-9646-2020-13.

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Earnings management is among the most extensively analysed topics of empirical accounting research since the 1980s. However, studies have focussed on commercial enterprises only and not yet properly covered the case of public enterprises. We try to close this research gap by modelling the management incentives to ma-nipulate earnings with reference to institutional economic theory. Based on that, we analyse the financial reporting of 14,800 German public companies over a period of 16 years. The data shows clear signs of earn-ings management for those public enterprises that are more independently run, rather detached from tradi-tional municipal activities, and operating in a more competitive market environment, which is in line with our theoretical model.
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Reports on the topic "Financial market modelling"

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Gamboa-Estrada, Fredy, and Jose Vicente Romero. Modelling CDS Volatility at Different Tenures: An Application for Latin-American Countries. Banco de la República de Colombia, May 2022. http://dx.doi.org/10.32468/be.1199.

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Assessing the dynamics of risk premium measures and its relationship with macroeconomic fundamentals is important for both macroeconomic policymakers and market practitioners. This paper analyzes the main determinants of CDS in Latin-America at different tenures, focusing on their volatility. Using a component GARCH model, we decompose volatility between permanent and transitory components. We find that the permanent component of CDS volatility in all tenors was higher and more persistent in the global financial crisis than during the recent COVID-19 shock. JEL Classification: C22, C58, G01, G15. Keywords: Credit default swaps (CDS), CDS in Latin-American countries, sovereign risk, volatility, crisis, component GARCH models
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Soloviev, V. N., and Y. V. Romanenko. Economic analog of Heisenberg uncertainly principle and financial crisis. ESC "IASA" NTUU "Igor Sikorsky Kyiv Polytechnic Institute", May 2017. http://dx.doi.org/10.31812/0564/2463.

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The Heisenberg uncertainty principle is one of the cornerstones of quantum mechanics. The modern version of the uncertainty principle, deals not with the precision of a measurement and the disturbance it introduces, but with the intrinsic uncertainty any quantum state must possess, regardless of what measurement is performed. Recently, the study of uncertainty relations in general has been a topic of growing interest, specifically in the setting of quantum information and quantum cryptography, where it is fundamental to the security of certain protocols. The aim of this study is to analyze the concepts and fundamental physical constants in terms of achievements of modern theoretical physics, they search for adequate and useful analogues in the socio-economic phenomena and processes, and their possible use in early warning of adverse crisis in financial markets. The instability of global financial systems depending on ordinary and natural disturbances in modern markets and highly undesirable financial crises are the evidence of methodological crisis in modelling, predicting and interpretation of current socio-economic conditions.
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