An Application of Hidden Markov Models to Asset Allocation Problems PDF Download

Author: Robert J. Elliott
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Filtering and parameter estimation techniques from Hidden Markov Models are applied to a discrete time asset allocation problem. For the commonly used mean-variance utility explicit optimal strategies are obtained.

Author: Rogemar S. Mamon
Publisher: Springer Science & Business Media
ISBN: 0387711635
Category : Business & Economics
Languages : en
Pages : 203

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Book Description
A number of methodologies have been employed to provide decision making solutions globalized markets. Hidden Markov Models in Finance offers the first systematic application of these methods to specialized financial problems: option pricing, credit risk modeling, volatility estimation and more. The book provides tools for sorting through turbulence, volatility, emotion, chaotic events – the random "noise" of financial markets – to analyze core components.

Author: Rogemar S. Mamon
Publisher: Springer
ISBN: 1489974423
Category : Business & Economics
Languages : en
Pages : 280

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Book Description
Since the groundbreaking research of Harry Markowitz into the application of operations research to the optimization of investment portfolios, finance has been one of the most important areas of application of operations research. The use of hidden Markov models (HMMs) has become one of the hottest areas of research for such applications to finance. This handbook offers systemic applications of different methodologies that have been used for decision making solutions to the financial problems of global markets. As the follow-up to the authors’ Hidden Markov Models in Finance (2007), this offers the latest research developments and applications of HMMs to finance and other related fields. Amongst the fields of quantitative finance and actuarial science that will be covered are: interest rate theory, fixed-income instruments, currency market, annuity and insurance policies with option-embedded features, investment strategies, commodity markets, energy, high-frequency trading, credit risk, numerical algorithms, financial econometrics and operational risk. Hidden Markov Models in Finance: Further Developments and Applications, Volume II presents recent applications and case studies in finance and showcases the formulation of emerging potential applications of new research over the book’s 11 chapters. This will benefit not only researchers in financial modeling, but also others in fields such as engineering, the physical sciences and social sciences. Ultimately the handbook should prove to be a valuable resource to dynamic researchers interested in taking full advantage of the power and versatility of HMMs in accurately and efficiently capturing many of the processes in the financial market.

Author: Xiaojing Xi
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
We consider a higher-order hidden Markov models (HMM), also called weak HMM (WHMM), to capture the regime-switching and memory properties of financial time series. A technique of transforming a WHMM into a regular HMM is employed, which in turn enables the development of recursive filters. With the use of the change of reference probability measure methodology and EM algorithm, a dynamic estimation of model parameters is obtained. Several applications and extensions were investigated. WHMM is adopted in describing the evolution of asset prices and its performance is examined through a forecasting analysis. This is extended to the case when the drift and volatility components of the logreturns are modulated by two independent WHMMs that are not necessarily having the same number of states. Numerical experiment is conducted based on simulated data to demonstrate the ability of our estimation approach in recovering the true model parameters. The analogue of recursive filtering and parameter estimation to handle multivariate data is also established. Some aspects of statistical inference arising from model implementation such as the assessment of model adequacy and goodness of fit are examined and addressed. The usefulness of the WHMM framework is tested on an asset allocation problem whereby investors determine the optimal investment strategy for the next time step through the results of the algorithm procedure. As an application in the modelling of yield curves, it is shown that the WHMM, with its memory-capturing mechanism, outperforms the usual HMM. A mean-reverting interest rate model is further developed whereby its parameters are modulated by a WHMM along with the formulation of a self-tuning parameter estimation. Finally, we propose an inverse Stieltjes moment approach to solve the inverse problem of calibration inherent in an HMM-based regime-switching set-up.

Author: Dongmei Zhu
Publisher:
ISBN: 9781361345467
Category :
Languages : en
Pages :

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Book Description
This dissertation, "Construction of Non-standard Markov Chain Models With Applications" by Dongmei, Zhu, 朱冬梅, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In this thesis, the properties of some non-standard Markov chain models and their corresponding parameter estimation methods are investigated. Several practical applications and extensions are also discussed. The estimation of model parameters plays a key role in the real-world applications of Markov chain models. Some widely used estimation methods for Markov chain models are based on the existence of stationary vectors. In this thesis, some weaker sufficient conditions for the existence of stationary vectors for highorder Markov chain models, multivariate Markov chain models and high-order multivariate Markov chain models are proposed. Furthermore, for multivariate Markov chain models, a new estimation method based on minimizing the prediction error is proposed. Numerical experiments are conducted to demonstrate the efficiency of the proposed estimation methods with an application in demand prediction. Hidden Markov Model (HMM) is a bivariate stochastic process such that one of the process is hidden and the other is observable. The distribution of observable sequence depends on the hidden sequence. In a traditional HMM, the hidden states directly affect the observable states but not vice versa. However, in reality, observable sequence may also have effect on the hidden sequence. For this reason, the concept of Interactive Hidden Markov Model (IHMM) is introduced, whose key idea is that the transitions of the hidden states depend on the observable states too. In this thesis, efforts are devoted in building a highorder IHMM where the probability laws governing both observable and hidden states can be written as a pair of high-order stochastic difference equations. We also propose a new model by capturing the effect of observable sequence on the hidden sequence through using the threshold principle. In this case, reference probability methods are adopted in estimating the optimal model parameters, while for unknown threshold parameter, Akaike Information Criterion (AIC) is used. We explore asset allocation problems from both domestic and foreign perspective where asset price dynamics follows autoregressive HMM. The object of an investor is not only to maximize the expected utility of the terminal wealth, but also to ensure that the risk of the portfolio described by the Value-at-Risk (VaR) does not exceed a specified level. In many decision processes, fuzziness is a major source of imprecision. As a perception of usual Markov chains, the definition of fuzzy Markov chains is introduced. Compared to traditional Markov chain models, fuzzy Markov chains are relatively new and many properties of them are still unknown. Due to the potential applications of fuzzy Markov chains, we provide some characterizations to ensure the ergodicity of these chains under both max-min and max-product compositions. DOI: 10.5353/th_b5295517 Subjects: Markov processes

Author: Shangzhen Luo
Publisher:
ISBN:
Category : Filters (Mathematics)
Languages : en
Pages : 216

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Book Description

Author: William Kinlaw
Publisher: John Wiley & Sons
ISBN: 1119402425
Category : Business & Economics
Languages : en
Pages : 259

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Book Description
Since the formalization of asset allocation in 1952 with the publication of Portfolio Selection by Harry Markowitz, there have been great strides made to enhance the application of this groundbreaking theory. However, progress has been uneven. It has been punctuated with instances of misleading research, which has contributed to the stubborn persistence of certain fallacies about asset allocation. A Practitioner's Guide to Asset Allocation fills a void in the literature by offering a hands-on resource that describes the many important innovations that address key challenges to asset allocation and dispels common fallacies about asset allocation. The authors cover the fundamentals of asset allocation, including a discussion of the attributes that qualify a group of securities as an asset class and a detailed description of the conventional application of mean-variance analysis to asset allocation.. The authors review a number of common fallacies about asset allocation and dispel these misconceptions with logic or hard evidence. The fallacies debunked include such notions as: asset allocation determines more than 90% of investment performance; time diversifies risk; optimization is hypersensitive to estimation error; factors provide greater diversification than assets and are more effective at reducing noise; and that equally weighted portfolios perform more reliably out of sample than optimized portfolios. A Practitioner's Guide to Asset Allocation also explores the innovations that address key challenges to asset allocation and presents an alternative optimization procedure to address the idea that some investors have complex preferences and returns may not be elliptically distributed. Among the challenges highlighted, the authors explain how to overcome inefficiencies that result from constraints by expanding the optimization objective function to incorporate absolute and relative goals simultaneously. The text also explores the challenge of currency risk, describes how to use shadow assets and liabilities to unify liquidity with expected return and risk, and shows how to evaluate alternative asset mixes by assessing exposure to loss throughout the investment horizon based on regime-dependent risk. This practical text contains an illustrative example of asset allocation which is used to demonstrate the impact of the innovations described throughout the book. In addition, the book includes supplemental material that summarizes the key takeaways and includes information on relevant statistical and theoretical concepts, as well as a comprehensive glossary of terms.

Author: João Paulo Coelho
Publisher: CRC Press
ISBN: 0429536631
Category : Computers
Languages : en
Pages : 222

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Book Description
This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB®. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB®. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach. Key Selling Points: Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory Covers the analysis of both continuous and discrete Markov chains Discusses the translation of HMM concepts from the realm of formal mathematics into computer code Offers many examples to supplement mathematical notation when explaining new concepts

Author: Söhnke M. Bartram
Publisher: CFA Institute Research Foundation
ISBN: 195292703X
Category : Business & Economics
Languages : en
Pages : 95

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Book Description
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Author: Wai-Ki Ching
Publisher: Springer Science & Business Media
ISBN: 1461463122
Category : Business & Economics
Languages : en
Pages : 259

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Book Description
This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data. This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs). Chapter 2 discusses the applications of continuous time Markov chains to model queueing systems and discrete time Markov chain for computing the PageRank, the ranking of websites on the Internet. Chapter 3 studies Markovian models for manufacturing and re-manufacturing systems and presents closed form solutions and fast numerical algorithms for solving the captured systems. In Chapter 4, the authors present a simple hidden Markov model (HMM) with fast numerical algorithms for estimating the model parameters. An application of the HMM for customer classification is also presented. Chapter 5 discusses Markov decision processes for customer lifetime values. Customer Lifetime Values (CLV) is an important concept and quantity in marketing management. The authors present an approach based on Markov decision processes for the calculation of CLV using real data. Chapter 6 considers higher-order Markov chain models, particularly a class of parsimonious higher-order Markov chain models. Efficient estimation methods for model parameters based on linear programming are presented. Contemporary research results on applications to demand predictions, inventory control and financial risk measurement are also presented. In Chapter 7, a class of parsimonious multivariate Markov models is introduced. Again, efficient estimation methods based on linear programming are presented. Applications to demand predictions, inventory control policy and modeling credit ratings data are discussed. Finally, Chapter 8 re-visits hidden Markov models, and the authors present a new class of hidden Markov models with efficient algorithms for estimating the model parameters. Applications to modeling interest rates, credit ratings and default data are discussed. This book is aimed at senior undergraduate students, postgraduate students, professionals, practitioners, and researchers in applied mathematics, computational science, operational research, management science and finance, who are interested in the formulation and computation of queueing networks, Markov chain models and related topics. Readers are expected to have some basic knowledge of probability theory, Markov processes and matrix theory.