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Author: Xiaojing Xi Publisher: ISBN: Category : Languages : en Pages :
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: Xiaojing Xi Publisher: ISBN: Category : Languages : en Pages :
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: Rogemar S. Mamon Publisher: Springer ISBN: 1489974423 Category : Business & Economics Languages : en Pages : 280
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: Wai-Ki Ching Publisher: Springer Science & Business Media ISBN: 038729337X Category : Mathematics Languages : en Pages : 212
Book Description
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.
Author: Zhenyu Cui Publisher: ISBN: Category : Languages : en Pages : 32
Book Description
In this chapter, we present recent developments in using the tools of continuous-time Markov chains for the valuation of European and path-dependent financial derivatives. We also survey results on a newly proposed regime switching approximation to stochastic volatility, and stochastic local volatility models. The presented framework is part of an exciting recent stream of literature on numerical option pricing, and offers a new perspective that combines the theory of diffusion processes, Markov chains, and Fourier techniques. It is also elegantly connected to partial differential equation (PDE) approaches.
Author: G. George Yin Publisher: Springer Science & Business Media ISBN: 0387268715 Category : Mathematics Languages : en Pages : 354
Book Description
This book focuses on two-time-scale Markov chains in discrete time. Our motivation stems from existing and emerging applications in optimization and control of complex systems in manufacturing, wireless communication, and ?nancial engineering. Much of our e?ort in this book is devoted to designing system models arising from various applications, analyzing them via analytic and probabilistic techniques, and developing feasible compu- tionalschemes. Ourmainconcernistoreducetheinherentsystemcompl- ity. Although each of the applications has its own distinct characteristics, all of them are closely related through the modeling of uncertainty due to jump or switching random processes. Oneofthesalientfeaturesofthisbookistheuseofmulti-timescalesin Markovprocessesandtheirapplications. Intuitively,notallpartsorcom- nents of a large-scale system evolve at the same rate. Some of them change rapidly and others vary slowly. The di?erent rates of variations allow us to reduce complexity via decomposition and aggregation. It would be ideal if we could divide a large system into its smallest irreducible subsystems completely separable from one another and treat each subsystem indep- dently. However, this is often infeasible in reality due to various physical constraints and other considerations. Thus, we have to deal with situations in which the systems are only nearly decomposable in the sense that there are weak links among the irreducible subsystems, which dictate the oc- sional regime changes of the system. An e?ective way to treat such near decomposability is time-scale separation. That is, we set up the systems as if there were two time scales, fast vs. slow. xii Preface Followingthetime-scaleseparation,weusesingularperturbationmeth- ology to treat the underlying systems.
Author: Silvia Chiappa Publisher: ISBN: 9781601988317 Category : Markov processes Languages : en Pages : 83
Book Description
Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time. The switching mechanism between regimes is controlled by unobserved random variables that form a first-order Markov chain. Explicit-duration MSMs contain additional variables that explicitly model the distribution of time spent in each regime. This allows to define duration distributions of any form, but also to impose complex dependence between the observations and to reset the dynamics to initial conditions. Models that focus on the first two properties are most commonly known as hidden semi-Markov models or segment models, whilst models that focus on the third property are most commonly known as changepoint models or reset models. In this monograph, we provide a description of explicit-duration modelling by categorizing the different approaches into three groups, which differ in encoding in the explicit-duration variables different information about regime change/reset boundaries. The approaches are described using the formalism of graphical models, which allows to graphically represent and assess statistical dependence and therefore to easily describe the structure of complex models and derive inference routines. The presentation is intended to be pedagogical, focusing on providing a characterization of the three groups in terms of model structure constraints and inference properties. The monograph is supplemented with a software package that contains most of the models and examples described. The material presented should be useful to both researchers wishing to learn about these models and researchers wishing to develop them further.
Author: James D. Hamilton Publisher: Springer Science & Business Media ISBN: 3642511821 Category : Business & Economics Languages : en Pages : 267
Book Description
This book is a collection of state-of-the-art papers on the properties of business cycles and financial analysis. The individual contributions cover new advances in Markov-switching models with applications to business cycle research and finance. The introduction surveys the existing methods and new results of the last decade. Individual chapters study features of the U. S. and European business cycles with particular focus on the role of monetary policy, oil shocks and co movements among key variables. The short-run versus long-run consequences of an economic recession are also discussed. Another area that is featured is an extensive analysis of currency crises and the possibility of bubbles or fads in stock prices. A concluding chapter offers useful new results on testing for this kind of regime-switching behaviour. Overall, the book provides a state-of-the-art over view of new directions in methods and results for estimation and inference based on the use of Markov-switching time-series analysis. A special feature of the book is that it includes an illustration of a wide range of applications based on a common methodology. It is expected that the theme of the book will be of particular interest to the macroeconomics readers as well as econometrics professionals, scholars and graduate students. We wish to express our gratitude to the authors for their strong contributions and the reviewers for their assistance and careful attention to detail in their reports.
Author: A. Tamir Publisher: Elsevier ISBN: 0080527396 Category : Mathematics Languages : en Pages : 617
Book Description
Markov chains make it possible to predict the future state of a system from its present state ignoring its past history. Surprisingly, despite the widespread use of Markov chains in many areas of science and technology, their applications in chemical engineering have been relatively meager. A possible reason for this phenomenon might be that books containing material on this subject have been written in such a way that the simplicity of Markov chains has been shadowed by the tedious mathematical derivations. Thus, the major objective of writing this book has been to try to change this situation.There are many advantages, detailed in Chapter 1, of using the discrete Markov-chain model in chemical engineering. Probably, the most important advantage is that physical models can be presented in a unified description via state vector and a one-step transition probability matrix. Consequently, a process is demonstrated solely by the probability of a system to occupy or not occupy a state.The book has been written in an easy and understandable form, where complex mathematical derivations are abandoned. The fundamentals of Markov chains are presented in Chapter 2 with examples from the bible, art and real life problems. An extremely wide collection is given of examples viz., reactions, reactors, reactions and reactors as well as combined processes, including their solution and a graphical presentation of it, all of which demonstrates the usefulness of applying Markov chains in chemical engineering.
Author: Sean Meyn Publisher: Cambridge University Press ISBN: 1139477978 Category : Mathematics Languages : en Pages : 595
Book Description
Meyn and Tweedie is back! The bible on Markov chains in general state spaces has been brought up to date to reflect developments in the field since 1996 - many of them sparked by publication of the first edition. The pursuit of more efficient simulation algorithms for complex Markovian models, or algorithms for computation of optimal policies for controlled Markov models, has opened new directions for research on Markov chains. As a result, new applications have emerged across a wide range of topics including optimisation, statistics, and economics. New commentary and an epilogue by Sean Meyn summarise recent developments and references have been fully updated. This second edition reflects the same discipline and style that marked out the original and helped it to become a classic: proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background.
Author: Tugrul Dayar Publisher: Springer Science & Business Media ISBN: 1461441900 Category : Mathematics Languages : en Pages : 91
Book Description
Kronecker products are used to define the underlying Markov chain (MC) in various modeling formalisms, including compositional Markovian models, hierarchical Markovian models, and stochastic process algebras. The motivation behind using a Kronecker structured representation rather than a flat one is to alleviate the storage requirements associated with the MC. With this approach, systems that are an order of magnitude larger can be analyzed on the same platform. The developments in the solution of such MCs are reviewed from an algebraic point of view and possible areas for further research are indicated with an emphasis on preprocessing using reordering, grouping, and lumping and numerical analysis using block iterative, preconditioned projection, multilevel, decompositional, and matrix analytic methods. Case studies from closed queueing networks and stochastic chemical kinetics are provided to motivate decompositional and matrix analytic methods, respectively.
Author: Xiaojing Xi
Author: Xiaojing Xi
Author: Rogemar S. Mamon
Author: Wai-Ki Ching
Author: Zhenyu Cui
Author: G. George Yin
Author: Silvia Chiappa
Author: James D. Hamilton
Author: A. Tamir
Author: Sean Meyn
Author: Tugrul Dayar