Market Models provides an authoritative and up–to–date treatment of the use of market data to develop models for financial analysis. Written by a leading figure in the field of financial data analysis, this book is the first of its kind to address the vital techniques required for model selection and development. Model developers are faced with many decisions, about the pricing, the data, the statistical methodology and the calibration and testing of the model prior to implementation. It is important to make the right choices and Carol Alexander s clear exposition provides valuable insights at every stage. In each of the 13 Chapters, Market Models presents real world illustrations to motivate theoretical developments. The accompanying CD contains spreadsheets with data and programs; this enables you to Market models: a guide to financial data analysis Market models: A guide to financial data analysis. Home > Research > Our research > STUDY WITH US ABOUT US NEWS & EVENTS INTERNATIONAL RESEARCH HOME implement and adapt many of the examples. The pricing of options using normal mixture density functions to model returns; the use of Monte Carlo simulation to calculate the VaR of an options portfolio; modifying the covariance VaR to allow for fat–tailed P&L distributions; the calculation of implied, EWMA and historic volatilities; GARCH volatility term structure forecasting; principal components analysis; and many more are all included. Carol Alexander brings many new insights to the pricing and hedging of options with her understanding of volatility and correlation, and the uncertainty which surrounds these key determinants of option portfolio risk. Modelling the market risk of portfolios is covered where the main focus is on a linear algebraic approach; the covariance matrix and principal component analysis are developed as key tools for the analysis of financial systems. The traditional time series econometric approach is also explained with coverage ranging from the application cointegration to long–short equity hedge funds, to high–frequency data prediction using neural networks and nearest neighbour algorithms. Throughout this text the emphasis is on understanding concepts and implementing solutions. It has been designed to be accessible to a very wide audience: the coverage is comprehensive and complete and the technical appendix makes the book largely self–contained. Market Models: A Guide to Financial Data Analysis is the ideal reference for all those involved in market risk measurement, quantitative trading and investment analysis. Item Type: Book Schools and Departments: School of Business, Management and Economics > Business and Management Subjects: H Social Sciences > HG Finance Depositing User: Carol Alexander Date Deposited: 27 Sep 2012 12:36 Last Modified: 27 Sep 2012 12:36 URI: http://sro.sussex.ac.uk/id/eprint/40646

Market Models: A Guide to Financial Data Analysis

textStudying the behavior of market participants is important due to its potential impact on asset prices and the dynamics of financial markets. The idea of individual investors who are prone to biases in judgment and who use various heuristics, which might lead to anomalies on the market level, has been explored within the field of behavioral finance. In this dissertation, we analyze market-wise implications of investor behavior and their irrationalities by means of agent-based simulations of financial markets. The usefulness of agent-based artificial markets for studying the behavioral finance topics stems from their ability to relate the micro-level behavior of individual market participants (represented as agents) and the macro-level behavior of the market (artificial time-series). This micro-macro mapping of agent-based methodology is particularly useful for behavioral finance, because that link is often broken when using other methodological approaches. In this thesis, we study various biases commented in the behavioral finance literature and propose novel models for some of the behavioral phenomena. We provide mathematical definitions and computational implementations for overconfidence (miscalibration and better-than-average effect), investor sentiment (optimism and pessimism), biased self-attribution, loss aversion, and recency and primacy effects. The levels of these behavioral biases are related to the features of the market dynamics, such as the bubbles and crashes, and the excess volatility of the market price. The impact of behavioral biases on investor performance is also studied.

Behavioral Finance and Agent-Based Artificial Markets

Real-time railway operations are subject to stochastic disturbances However, a railway timetable is a deterministic plan Thus a timetable should be designed in such a way that it can absorb the stochastic disturbances as well as possible To that end, a timetable contains buffer times between trains and supplements in running times and dwell times This paper first describes a stochastic optimization model that can be used to find an optimal allocation of the running time supplements of a single train on a number of consecutive trips along the same line The aim of this model is to minimize the average delay of the train The model is then extended such that it can be used to improve a given cyclic timetable for a number of trains on a common infrastructure Computational results show that the average delay of the trains can be reduced substantially by applying relatively small modifications to the timetable In particular, allocating the running time supplements in a different way than what is usual in practice can be useful

Cyclic Railway Timetabling: A Stochastic Optimization Approach

textThe past two decades have witnessed an exponential growth of research on corporate governance around the world and on the role of the ownership concentration more specifically. In line with a longer tradition of ownership studies in U.S. context, most corporate governance researchers have commonly taken a classical agency theoretical view of ownership concentration. The research presented in this dissertation show that classical view of ownership seems overly crude. I provide a more fine-grained understanding about the role of ownership in different contexts; one that takes into account the subtly different formal and informal institutional that can be found around the world on the one hand, and that distinguish between the identity of concentrated owners on the other. I show, first, that a crucial factor with respect to the ownership concentration – firm strategy and performance relationships involve owner identity: i.e. who owns a firm matters significantly for that firm’s objectives, strategies, and performance. Second, I contribute to emerging institution-based view of corporate governance by expanding its empirical domain and testing empirically the interaction between formal and informal institutions.

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An Institution-Based View of Ownership

textThe dynamics of financial markets is subject of much debate among researchers and financial experts trying to understand and explain how financial markets work and traders behave. Diversified explanations result from the complexity of markets, and the hardly observable aspects of price formation mechanisms and of participants' motivation behind trading decisions. In an attempt to provide a better understanding of market dynamics, studies in the realm of agent-based computational economics represent markets from bottom-up. 
The aim of this thesis is to contribute to the understanding of market dynamics by extending the agent-based computational approach. In order to achieve our goal we propose a modular, continuous-time, agent-based trading environment, with individual, autonomous representation of market participants. In order to be able to develop such an environment we first analyze and compare real and artificial stock markets (ASMs). Based on this analysis we propose a conceptual framework to describe real markets. By enriching the framework with design and implementation issues we get a multi-dimensional taxonomy of artificial stock markets. ABSTRACTE, the proposed modular environment is an operational form of these frameworks. ABSTRACTE is aimed to embed the common aspects of real markets that exhibit big variations and are rarely represented in artificial stock markets. This environment provides the user with a flexible mechanism to implement many of the varying and hardly observable aspects of stock markets and traders' behavior. In this way it can contribute to the understanding of market dynamics as it can be used both as a test bed to replicate and evaluate existing market models, and to compare dynamics of multiple ASMs, as well as a tool to conduct experiments with new models and traders.

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Agent-Based Simulation of Financial Markets: A Modular, Continuous-time Approach

Artificial stock markets are designed with the aim to study and understand market dynamics
by representing (part of) real stock markets. Since there is a large variety of real
stock markets with several partially observable elements and hidden processes, artificial
markets differ regarding their structure and implementation. In this paper we analyze to
what degree current artificial stock markets reflect the workings of real stock markets. In
order to conduct this analysis we set up a list of factors which influence market dynamics
and are as a consequence important to consider for designing market models. We differentiate
two categories of factors: general, well-defined aspects that characterize the organization
of a market and hidden aspects that characterize the functioning of the markets and the
behaviour of the traders.

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On the design of artificial stock markets

Agent-Based Simulation of Financial Markets

Artificial stock markets are built with diffuse priors in mind regarding trading strategies
and price formation mechanisms. Diffuse priors are a natural consequence of the
unknown relation between the various elements that drive market dynamics and the large
variety of market organizations, findings, however, might hold only within the specific market
settings. In this paper we propose a framework for building agent-based artificial stock
markets. We present the mechanism of the framework based on a previously identified list
of organizational and behavioural aspects. Within the framework experiments with arbitrary
many trading strategies, acting in various market organizations can be conducted in a
flexible way, without changing its architecture. In this way experiments of other artificial
stock markets, as well as theoretical models can be replicated and their findings compared.
Comparisons of the different experimental results might indicate whether findings are due
to traders’ behaviour or to the chosen market structure and could suggest how to improve
market quality.

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A modular agent-based environment for studying stock markets

Even though the internet is a very useful asset to everyday life, it can facilitate crime as it can beused to achieve unlawful goals. Stepping up against cybercrime effectively requires extensiveinternational cooperation between law enforcement agencies and the private sector, and betweenthe law enforcement agencies themselves. Internet intermediary service providers such as ISPs,hosting providers and search engine providers are in a special position when it comes to tacklingcybercrime: they have to balance carefully between protecting the rights of their users (such as theright to privacy or free speech) and exercising corporate responsibility to prevent and respond tocybercrimes. These providers are sometimes indispensable participants of a successfulinvestigation, because they are the entities that are in a position to provide data to law enforcementagencies and carry out blocking orders. One of the aims of this paper is to give a short overview ofthose voluntary and obligatory actions that the providers take in order to support the investigativeprocess in Hungary. Besides these actions that stem from the social responsibility and legallyenacted obligations, the providers may also be held liable for the actions of third parties (althoughthey may be exempted if certain conditions are met). The second aim of this paper is to analyze thetwofold nature of the position of intermediary service providers and to map the arising conflictsbetween their liability and their role as participants of cybercrime investigations.

K Sorban

Papers

On the design of artificial stock markets

Agent-Based Simulation of Financial Markets

A modular agent-based environment for studying stock markets

The role of Internet intermediaries in combatting cybercrime: Organisation and liabilities