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| hidden markov models in finance: Hidden Markov Models in Finance Rogemar S. Mamon, Robert J Elliott, 2007-04-26 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. |
| hidden markov models in finance: Hidden Markov Models in Finance Rogemar S. Mamon, Robert J Elliott, 2010-11-25 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. |
| hidden markov models in finance: Hidden Markov Models in Finance Rogemar S. Mamon, Robert J. Elliott, 2014-05-14 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. |
| hidden markov models in finance: Hidden Markov Models in Finance Rogemar S. Mamon, Robert J. Elliott, 2014-06-30 |
| hidden markov models in finance: Hidden Markov Models for Time Series Walter Zucchini, Iain L. MacDonald, Roland Langrock, 2017-12-19 Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture-recapture data |
| hidden markov models in finance: Inference in Hidden Markov Models Olivier Cappé, Eric Moulines, Tobias Ryden, 2006-04-12 This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view. |
| hidden markov models in finance: Hidden Markov Models Ramaprasad Bhar, Shigeyuki Hamori, 2006-04-18 Markov chains have increasingly become useful way of capturing stochastic nature of many economic and financial variables. Although the hidden Markov processes have been widely employed for some time in many engineering applications e.g. speech recognition, its effectiveness has now been recognized in areas of social science research as well. The main aim of Hidden Markov Models: Applications to Financial Economics is to make such techniques available to more researchers in financial economics. As such we only cover the necessary theoretical aspects in each chapter while focusing on real life applications using contemporary data mainly from OECD group of countries. The underlying assumption here is that the researchers in financial economics would be familiar with such application although empirical techniques would be more traditional econometrics. Keeping the application level in a more familiar level, we focus on the methodology based on hidden Markov processes. This will, we believe, help the reader to develop more in-depth understanding of the modeling issues thereby benefiting their future research. |
| hidden markov models in finance: Hidden Markov Models Robert J Elliott, Lakhdar Aggoun, John B. Moore, 2008-09-27 As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors’ general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control. |
| hidden markov models in finance: Detecting Regime Change in Computational Finance Jun Chen, Edward P K Tsang, 2020-09-14 Based on interdisciplinary research into Directional Change, a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction (zigzags). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002. |
| hidden markov models in finance: Mathematics of Financial Markets Robert J Elliott, P. Ekkehard Kopp, 2013-11-11 This book explores the mathematics that underpins pricing models for derivative securities such as options, futures and swaps in modern markets. Models built upon the famous Black-Scholes theory require sophisticated mathematical tools drawn from modern stochastic calculus. However, many of the underlying ideas can be explained more simply within a discrete-time framework. This is developed extensively in this substantially revised second edition to motivate the technically more demanding continuous-time theory. |
| hidden markov models in finance: Markov Processes for Stochastic Modeling Oliver Ibe, 2013-05-22 Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. - Presents both the theory and applications of the different aspects of Markov processes - Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented - Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis. |
| hidden markov models in finance: Python Machine Learning Cookbook Prateek Joshi, 2016-06-23 100 recipes that teach you how to perform various machine learning tasks in the real world About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide Learn about perceptrons and see how they are used to build neural networks Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques Who This Book Is For This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Explore data visualization techniques to interact with your data in diverse ways Find out how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Analyze stock market data using Conditional Random Fields Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system In Detail Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. Style and approach You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book. |
| hidden markov models in finance: Data Analytics in Bioinformatics Rabinarayan Satpathy, Tanupriya Choudhury, Suneeta Satpathy, Sachi Nandan Mohanty, Xiaobo Zhang, 2021-01-20 Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more. |
| hidden markov models in finance: Economic Growth and Convergence Michał Bernardelli, Mariusz Próchniak, Bartosz Witkowski, 2021-06-30 There are many different types of convergence within economics, as well as several methods to analyse each of them. This book addresses the concept of real economic convergence or the gradual levelling-off of GDP (gross domestic product) per capita rates across economies. In addition to a detailed, holistic overview of the history and theory, the authors include a description of two modern methods of assessing the occurrence and rate of convergence, BMA-based and HMM-based, as well as the results of the empirical analysis. Readers will have access not only to the conventional econometric approach of β convergence but also to an alternative one, allowing for the convergence issue to be expressed in the context of automatic pattern recognition. This approach is universal as it can be adapted to a variety of input data. The lowest aggregation level study investigates regional convergence through the case of Polish voivodships, where convergence towards the leader is tested. On a higher level of aggregation, the authors examine the existence of GDP convergence in such groups as the EU28, North Africa and the Middle East, sub-Saharan Africa, South America, Caribbean, South-East Asia, Australia and Oceania, or post-socialist countries. For each group, the real β convergence is tested using the two above-mentioned approaches. The results are widely discussed, broadly illustrated, interpreted, and compared. The analysis allows readers to draw interesting conclusions about the causes of convergence or the drivers behind divergence. The book will stimulate further research in the field, but the research was conducted from the point of view of individual countries. |
| hidden markov models in finance: Algorithmic and High-Frequency Trading Álvaro Cartea, Sebastian Jaimungal, José Penalva, 2015-08-06 The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms, then this is the book for you. |
| hidden markov models in finance: Hidden Markov Models and Dynamical Systems Andrew M. Fraser, 2008-01-01 Presents algorithms for using HMMs and explains the derivation of those algorithms for the dynamical systems community. |
| hidden markov models in finance: Secondary Analysis of Electronic Health Records MIT Critical Data, 2016-09-09 This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients. |
| hidden markov models in finance: Stochastic Optimization Stanislav Uryasev, Panos M. Pardalos, 2013-03-09 Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering. |
| hidden markov models in finance: Machine Learning Kevin P. Murphy, 2012-08-24 A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. |
| hidden markov models in finance: Hidden Markov Models: Applications In Computer Vision Horst Bunke, Terry Michael Caelli, 2001-06-04 Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001). |
| hidden markov models in finance: Finite Mixture and Markov Switching Models Sylvia Frühwirth-Schnatter, 2006-11-24 The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers. |
| hidden markov models in finance: Bayesian Reasoning and Machine Learning David Barber, 2012-02-02 A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus. |
| hidden markov models in finance: Introduction to Hidden Semi-Markov Models John Van der Hoek, Robert J. Elliott, 2018 Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications |
| hidden markov models in finance: Handbook Of Software Aging And Rejuvenation: Fundamentals, Methods, Applications, And Future Directions Tadashi Dohi, Kishor S Trivedi, Alberto Avritzer, 2020-04-22 The Handbook of Software Aging and Rejuvenation provides a comprehensive overview of the subject, making it indispensable to graduate students as well as professionals in the field. It begins by introducing fundamental concepts, definitions, and the history of software aging and rejuvenation research, followed by methods, tools, and strategies that can be used to detect, analyze, and overcome software aging. |
| hidden markov models in finance: AI and Financial Markets Shigeyuki Hamori, Tetsuya Takiguchi, 2020-07-01 Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets. |
| hidden markov models in finance: Hidden Markov and Other Models for Discrete- valued Time Series Iain L. MacDonald, Walter Zucchini, 1997-01-01 Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time Series introduces a new, versatile, and computationally tractable class of models, the hidden Markov models. It presents a detailed account of these models, then applies them to data from a wide range of diverse subject areas, including medicine, climatology, and geophysics. This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics. Researchers and applied statisticians who analyze time series data in medicine, animal behavior, hydrology, and sociology will also find this information useful. |
| hidden markov models in finance: Binomial Models in Finance John van der Hoek, Robert J Elliott, 2006-04-18 This book describes the modelling of prices of ?nancial assets in a simple d- crete time, discrete state, binomial framework. By avoiding the mathematical technicalitiesofcontinuoustime?nancewehopewehavemadethematerial accessible to a wide audience. Some of the developments and formulae appear here for the ?rst time in book form. We hope our book will appeal to various audiences. These include MBA s- dents,upperlevelundergraduatestudents,beginningdoctoralstudents,qu- titative analysts at a basic level and senior executives who seek material on new developments in ?nance at an accessible level. The basic building block in our book is the one-step binomial model where a known price today can take one of two possible values at a future time, which might, for example, be tomorrow, or next month, or next year. In this simple situation “risk neutral pricing” can be de?ned and the model can be applied to price forward contracts, exchange rate contracts and interest rate derivatives. In a few places we discuss multinomial models to explain the notions of incomplete markets and how pricing can be viewed in such a context, where unique prices are no longer available. The simple one-period framework can then be extended to multi-period m- els.TheCox-Ross-RubinsteinapproximationtotheBlackScholesoptionpr- ing formula is an immediate consequence. American, barrier and exotic - tions can all be discussed and priced using binomial models. More precise modelling issues such as implied volatility trees and implied binomial trees are treated, as well as interest rate models like those due to Ho and Lee; and Black, Derman and Toy. |
| hidden markov models in finance: Advances in Credit Risk Modeling and Management Frédéric Vrins, 2020-07-01 Credit risk remains one of the major risks faced by most financial and credit institutions. It is deeply connected to the real economy due to the systemic nature of some banks, but also because well-managed lending facilities are key for wealth creation and technological innovation. This book is a collection of innovative papers in the field of credit risk management. Besides the probability of default (PD), the major driver of credit risk is the loss given default (LGD). In spite of its central importance, LGD modeling remains largely unexplored in the academic literature. This book proposes three contributions in the field. Ye & Bellotti exploit a large private dataset featuring non-performing loans to design a beta mixture model. Their model can be used to improve recovery rate forecasts and, therefore, to enhance capital requirement mechanisms. François uses instead the price of defaultable instruments to infer the determinants of market-implied recovery rates and finds that macroeconomic and long-term issuer specific factors are the main determinants of market-implied LGDs. Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. Another topic deserving more attention is trade credit, which consists of the supplier providing credit facilities to his customers. Whereas this is likely to stimulate exchanges in general, it also magnifies credit risk. This is a difficult problem that remains largely unexplored. Kanapickiene & Spicas propose a simple but yet practical model to assess trade credit risk associated with SMEs and microenterprises operating in Lithuania. Another topical area in credit risk is counterparty risk and all other adjustments (such as liquidity and capital adjustments), known as XVA. Chataignier & Crépey propose a genetic algorithm to compress CVA and to obtain affordable incremental figures. Anagnostou & Kandhai introduce a hidden Markov model to simulate exchange rate scenarios for counterparty risk. Eventually, Boursicot et al. analyzes CoCo bonds, and find that they reduce the total cost of debt, which is positive for shareholders. In a nutshell, all the featured papers contribute to shedding light on various aspects of credit risk management that have, so far, largely remained unexplored. |
| hidden markov models in finance: State-Space Models Yong Zeng, Shu Wu, 2013-08-15 State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. The second part focuses on the application of Linear State-Space Models in Macroeconomics and Finance. The third part deals with Hidden Markov Models, Regime Switching and Mathematical Finance and the fourth part is on Nonlinear State-Space Models for High Frequency Financial Data. The book will appeal to graduate students and researchers studying state-space modeling in economics, statistics, and mathematics, as well as to finance professionals. |
| hidden markov models in finance: Machine Learning in Finance Matthew F. Dixon, Igor Halperin, Paul Bilokon, 2020-07-01 This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance. |
| hidden markov models in finance: Robustness Lars Peter Hansen, Thomas J. Sargent, 2016-06-28 The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes. |
| hidden markov models in finance: Handbook of Markov Chain Monte Carlo Steve Brooks, Andrew Gelman, Galin Jones, Xiao-Li Meng, 2011-05-10 Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie |
| hidden markov models in finance: Markov Chains: Models, Algorithms and Applications Wai-Ki Ching, Michael K. Ng, 2006-06-05 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. |
| hidden markov models in finance: Mixture and Hidden Markov Models with R Ingmar Visser, Maarten Speekenbrink, 2022-06-28 This book discusses mixture and hidden Markov models for modeling behavioral data. Mixture and hidden Markov models are statistical models which are useful when an observed system occupies a number of distinct “regimes” or unobserved (hidden) states. These models are widely used in a variety of fields, including artificial intelligence, biology, finance, and psychology. Hidden Markov models can be viewed as an extension of mixture models, to model transitions between states over time. Covering both mixture and hidden Markov models in a single book allows main concepts and issues to be introduced in the relatively simpler context of mixture models. After a thorough treatment of the theory and practice of mixture modeling, the conceptual leap towards hidden Markov models is relatively straightforward. This book provides many practical examples illustrating the wide variety of uses of the models. These examples are drawn from our own work in psychology, as well as other areas such as financial time series and climate data. Most examples illustrate the use of the authors’ depmixS4 package, which provides a flexible framework to construct and estimate mixture and hidden Markov models. All examples are fully reproducible and the accompanying hmmR package provides all the datasets used, as well as additional functionality. This book is suitable for advanced students and researchers with an applied background. |
| hidden markov models in finance: Finance at Fields Matheus R. Grasselli, Lane P. Hughston, 2013 This outstanding collection of articles includes papers presented at the Fields Institute, Toronto, as part of the Thematic Program in Quantitative Finance that took place in the first six months of the year 2010. The scope of the volume in very broad, including papers on foundational issues in mathematical finance, papers on computational finance, and papers on derivatives and risk management. Many of the articles contain path-breaking insights that are relevant to the developing new order of post-crisis financial risk management. |
| hidden markov models in finance: Applied Predictive Modeling Max Kuhn, Kjell Johnson, 2013-05-17 Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. |
| hidden markov models in finance: Machine Trading Ernest P. Chan, 2017-02-06 Dive into algo trading with step-by-step tutorials and expert insight Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins. The companion website provides downloadable software codes, and you'll learn to design your own proprietary tools using MATLAB. The author's experiences provide deep insight into both the business and human side of systematic trading and money management, and his evolution from proprietary trader to fund manager contains valuable lessons for investors at any level. Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets you up to speed, and walks you through the process of developing your own proprietary trading operation using the latest tools. Utilize the newer, easier algorithmic trading platforms Access markets previously unavailable to systematic traders Adopt new strategies for a variety of instruments Gain expert perspective into the human side of trading The strength of algorithmic trading is its versatility. It can be used in any strategy, including market-making, inter-market spreading, arbitrage, or pure speculation; decision-making and implementation can be augmented at any stage, or may operate completely automatically. Traders looking to step up their strategy need look no further than Machine Trading for clear instruction and expert solutions. |
| hidden markov models in finance: Applied Semi-Markov Processes Jacques Janssen, Raimondo Manca, 2006-02-08 Aims to give to the reader the tools necessary to apply semi-Markov processes in real-life problems. The book is self-contained and, starting from a low level of probability concepts, gradually brings the reader to a deep knowledge of semi-Markov processes. Presents homogeneous and non-homogeneous semi-Markov processes, as well as Markov and semi-Markov rewards processes. The concepts are fundamental for many applications, but they are not as thoroughly presented in other books on the subject as they are here. |
| hidden markov models in finance: Handbook of Mixture Analysis Sylvia Fruhwirth-Schnatter, Gilles Celeux, Christian P. Robert, 2019-01-04 Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems. |
| hidden markov models in finance: Latent Markov Models for Longitudinal Data Francesco Bartolucci, Alessio Farcomeni, Fulvia Pennoni, 2012-10-29 Drawing on the authors' extensive research in the analysis of categorical longitudinal data, this book focuses on the formulation of latent Markov models and the practical use of these models. It demonstrates how to use the models in three types of analysis, with numerous examples illustrating how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB routines used for the examples are available on the authors' website. |
Lecture 9: Hidden Markov Models - McGill University
Hidden Markov Models (HMMs) are used for situations in which: { The data consists of a sequence of observations { The observations depend (probabilistically) on the internal state of a
Hidden Markov Models in Finance - download.e-bookshelf.de
Hidden Markov Models in Finance Edited by Rogemar S. Mamon Robert J. Elliott. Rogemar S. Mamon Robert J. Elliott University of Western Ontario University of Calgary London, Canada …
Hidden Markov Models - Springer
attention to the features of hidden Markov models (HMM) that distin-guish them from similar modeling approaches, for example, the regime switching models familiar to most financial …
Hidden Markov Models
A hidden Markov model (HMM) is a temporal probabilistic model in which the state of the process is described by a single discrete random variable. Loosely speaking, it is a Markov chain …
Stock Market Prediction Using Hidden Markov Models - Duke …
To incorporate these, Hidden Markov Models (HMM's) have recently been applied to forecast and predict the stock market. We present the Maximum a Posteriori HMM approach for forecasting …
fHMM: Fitting Hidden Markov Models to Financial Data
Title Fitting Hidden Markov Models to Financial Data. Version 1.4.1. Description Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See …
Hidden Markov Models Fundamentals - Stanford University
A Hidden Markov Model (HMM) can be used to explore this scenario. We don't get to observe the actual sequence of states (the weather on each day). Rather, we can only observe some …
PREDICTION OF FINANCIAL TIME SERIES WITH HIDDEN …
In this thesis, we develop an extension of the Hidden Markov Model (HMM) that addresses two of the most important challenges of nancial time series modeling: non-stationary and non-linearity.
An introduction to the use of hidden Markov models for stock …
Hidden Markov models (HMMs) are known for their applications to speech processing and pattern recognition. They are attractive models for discrete time series analysis because of their …
Hidden Markov Models in Finance - GBV
Hidden Markov Models in Finance , Edited by. Rogemar S. Mamon Robert J. Elliott. B 375492. fyA Springer. Contents. 1 An Exact Solution of the Term Structure of Interest Rate under …
Inference in Hidden Markov Models - Inria
We now formally describe hidden Markov models, setting the notations that will be used throughout the book. We start by reviewing the basic de nitions and concepts pertaining to …
Long memory of financial time series and hidden Markov models …
The hidden Markov model In a hidden Markov model, the probability distribution that generates an observation depends on the state of an underlying and unobserved Markov process. An HMM …
Analysis of Hidden Markov Models and Support Vector Machines …
Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications. Jerry Hong. University of California, Berkeley Soda Hall, 2599 Hearst Ave Berkeley, CA 94720 …
History and Theoretical Basics of Hidden Markov Models
Hidden Markov Models (HMMs) are learnable finite stochastic automates. Nowadays, they are considered as a specific form of dynamic Bayesian networks. Dynamic Bayesian networks are …
Stylized facts of financial time series and hidden Markov models …
Hidden Markov models are often applied in quantitative nance to capture the stylised facts of nancial returns. They are usually discrete-time models and the number of states rarely …
METASTABLE HIDDEN MARKOV PROCESSES: A THEORY …
In this paper, we propose a theory that justifies such a modeling under the assumption that there exists a market formed by agents whose states evolve on time as an interacting Markov …
[cs229 Project] Stock Forecasting using Hidden Markov Processes
In this project, we would like to construct this regime and utilize it for the stock forecasting using one of the machine learning algorithms. For this, we model the stock series has Gaussian …
Detecting Regime Changes in Financial Markets using Hidden …
The Hidden Markov Model with Directional Change indicators is able to effectively classify regimes on the basis of their statistical properties viz. Mean and standard deviation. The …
Regime-switching factor investing with hidden Markov models
Abstract: This study uses the hidden Markov model (HMM) to identify different market regimes in the US stock market and proposes an investment strategy that switches factor investment …
Stock prediction using LSTM and Hidden Markov Models - LiU
The purpose of this article is to evaluate the per-formance of two different models, Long-Short Term Memory (LSTM) models and Hidden Markov Mod-els (HMM). Recurrent Neural …
Lecture 9: Hidden Markov Models - McGill University
Hidden Markov Models (HMMs) are used for situations in which: { The data consists of a sequence of observations { The observations depend (probabilistically) on the internal state of a
Hidden Markov Models in Finance - download.e-bookshelf.de
Hidden Markov Models in Finance Edited by Rogemar S. Mamon Robert J. Elliott. Rogemar S. Mamon Robert J. Elliott University of Western Ontario University of Calgary London, Canada Calgary, Canada Library of Congress Control Number: 2007921976 ISBN-10: 0-387-71081-7 (HB) ISBN-10: 0-387-71163-5 (e-book)
Hidden Markov Models - Springer
attention to the features of hidden Markov models (HMM) that distin-guish them from similar modeling approaches, for example, the regime switching models familiar to most financial economists. The contents of later chapters of this book also require some understanding of state space methodology (SSM) and the related filtering techniques. To help
Hidden Markov Models
A hidden Markov model (HMM) is a temporal probabilistic model in which the state of the process is described by a single discrete random variable. Loosely speaking, it is a Markov chain observed in noise.
Stock Market Prediction Using Hidden Markov Models
To incorporate these, Hidden Markov Models (HMM's) have recently been applied to forecast and predict the stock market. We present the Maximum a Posteriori HMM approach for forecasting stock values for the next day given historical data.
fHMM: Fitting Hidden Markov Models to Financial Data
Title Fitting Hidden Markov Models to Financial Data. Version 1.4.1. Description Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. Detecting Bearish and Bullish …
Hidden Markov Models Fundamentals - Stanford University
A Hidden Markov Model (HMM) can be used to explore this scenario. We don't get to observe the actual sequence of states (the weather on each day). Rather, we can only observe some outcome generated by each state (how many ice creams were eaten that day). ormallyF, an HMM is a Markov model for which we have a series of observed outputs x= fx 1;x ...
PREDICTION OF FINANCIAL TIME SERIES WITH HIDDEN MARKOV MODELS
In this thesis, we develop an extension of the Hidden Markov Model (HMM) that addresses two of the most important challenges of nancial time series modeling: non-stationary and non-linearity.
An introduction to the use of hidden Markov models for …
Hidden Markov models (HMMs) are known for their applications to speech processing and pattern recognition. They are attractive models for discrete time series analysis because of their simple structures. It is therefore not surprising that there has been research on …
Hidden Markov Models in Finance - GBV
Hidden Markov Models in Finance , Edited by. Rogemar S. Mamon Robert J. Elliott. B 375492. fyA Springer. Contents. 1 An Exact Solution of the Term Structure of Interest Rate under Regime-Switching Risk. Shu Wu, Yong Zeng. 1.1 Introduction. 1.2 A new representation for modeling regime shift. 1.3 The model. 1.3.1 Two state variables. 1.3.2.
Inference in Hidden Markov Models - Inria
We now formally describe hidden Markov models, setting the notations that will be used throughout the book. We start by reviewing the basic de nitions and concepts pertaining to Markov chains. 1.1 Markov Chains 1.1.1 Transition Kernels De nition 1 (Transition Kernel). Let (X;X) and (Y;Y) be two measurable spaces.
Long memory of financial time series and hidden Markov models …
The hidden Markov model In a hidden Markov model, the probability distribution that generates an observation depends on the state of an underlying and unobserved Markov process. An HMM is a particular kind of dependent mixture and is therefore also referred to as a Markov-switching mixture model. General
Analysis of Hidden Markov Models and Support Vector …
Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications. Jerry Hong. University of California, Berkeley Soda Hall, 2599 Hearst Ave Berkeley, CA 94720-1776. jerricality@gmail.com. ABSTRACT. This paper presents two approaches in helping investors make better decisions.
History and Theoretical Basics of Hidden Markov Models
Hidden Markov Models (HMMs) are learnable finite stochastic automates. Nowadays, they are considered as a specific form of dynamic Bayesian networks. Dynamic Bayesian networks are based on the theory of Bayes (Bayes & Price, 1763). Hidden Markov Model consists of two stochastic processes. The first stochastic process is.
Stylized facts of financial time series and hidden Markov models …
Hidden Markov models are often applied in quantitative nance to capture the stylised facts of nancial returns. They are usually discrete-time models and the number of states rarely exceeds two because of the quadratic increase in the number of parameters with the number of states. This paper presents
METASTABLE HIDDEN MARKOV PROCESSES: A …
In this paper, we propose a theory that justifies such a modeling under the assumption that there exists a market formed by agents whose states evolve on time as an interacting Markov system that has a metastable behavior described by the hidden Markov chain.
[cs229 Project] Stock Forecasting using Hidden Markov Processes
In this project, we would like to construct this regime and utilize it for the stock forecasting using one of the machine learning algorithms. For this, we model the stock series has Gaussian distribution in a regime and each regime is modeled Hidden Markov Model (HMM) to obtain the discrete economic situations.
Detecting Regime Changes in Financial Markets using Hidden Markov ...
The Hidden Markov Model with Directional Change indicators is able to effectively classify regimes on the basis of their statistical properties viz. Mean and standard deviation. The motivation of this study is to model and detect regime changes in US financial markets over the 22-year period from 2000 to 2022. Hidden Markov Models have ...
Regime-switching factor investing with hidden Markov models …
Abstract: This study uses the hidden Markov model (HMM) to identify different market regimes in the US stock market and proposes an investment strategy that switches factor investment models depending on the current detected regime.
Stock prediction using LSTM and Hidden Markov Models - LiU
The purpose of this article is to evaluate the per-formance of two different models, Long-Short Term Memory (LSTM) models and Hidden Markov Mod-els (HMM). Recurrent Neural Networks and LSTMs in particular have previously been used to handle time series and stock data well [10].
