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Dynamic Portfolio Choice With Bayesian Learning
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Book Synopsis Portfolio Choice Problems by : Nicolas Chapados
Download or read book Portfolio Choice Problems written by Nicolas Chapados and published by Springer Science & Business Media. This book was released on 2011-07-12 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief offers a broad, yet concise, coverage of portfolio choice, containing both application-oriented and academic results, along with abundant pointers to the literature for further study. It cuts through many strands of the subject, presenting not only the classical results from financial economics but also approaches originating from information theory, machine learning and operations research. This compact treatment of the topic will be valuable to students entering the field, as well as practitioners looking for a broad coverage of the topic.
Book Synopsis Bayesian Forecasting and Dynamic Models by : Mike West
Download or read book Bayesian Forecasting and Dynamic Models written by Mike West and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
Book Synopsis Portfolio Management under Stress by : Riccardo Rebonato
Download or read book Portfolio Management under Stress written by Riccardo Rebonato and published by Cambridge University Press. This book was released on 2013 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: A rigorous presentation of a novel methodology for asset allocation in financial portfolios under conditions of market distress.
Book Synopsis Frontiers of Statistical Decision Making and Bayesian Analysis by : Ming-Hui Chen
Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Book Synopsis Predictions, Nonlinearities and Portfolio Choice by : Friedrich Christian Kruse
Download or read book Predictions, Nonlinearities and Portfolio Choice written by Friedrich Christian Kruse and published by BoD – Books on Demand. This book was released on 2012 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finance researchers and asset management practitioners put a lot of effort into the question of optimal asset allocation. With this respect, a lot of research has been conducted on portfolio decision making as well as quantitative modeling and prediction models. This study brings together three fields of research, which are usually analyzed in an isolated manner in the literature: - Predictability of asset returns and their covariance matrix - Optimal portfolio decision making - Nonlinear modeling, performed by artificial neural networks, and their impact on predictions as well as optimal portfolio construction Including predictability in asset allocation is the focus of this work and it pays special attention to issues related to nonlinearities. The contribution of this study to the portfolio choice literature is twofold. First, motivated by the evidence of linear predictability, the impact of nonlinear predictions on portfolio performances is analyzed. Predictions are empirically performed for an investor who invests in equities (represented by the DAX index), bonds (represented by the REXP index) and a risk-free rate. Second, a solution to the dynamic programming problem for intertemporal portfolio choice is presented. The method is based on functional approximations of the investor's value function with artificial neural networks. The method is easily capable of handling multiple state variables. Hence, the effect of adding predictive parameters to the state space is the focus of analysis as well as the impacts of estimation biases and the view of a Bayesian investor on intertemporal portfolio choice. One important empirical result shows that residual correlation among state variables have an impact on intertemporal portfolio decision making.
Book Synopsis High Performance Optimization by : Hans Frenk
Download or read book High Performance Optimization written by Hans Frenk and published by Springer Science & Business Media. This book was released on 2000 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a long time the techniques of solving linear optimization (LP) problems improved only marginally. Fifteen years ago, however, a revolutionary discovery changed everything. A new `golden age' for optimization started, which is continuing up to the current time. What is the cause of the excitement? Techniques of linear programming formed previously an isolated body of knowledge. Then suddenly a tunnel was built linking it with a rich and promising land, part of which was already cultivated, part of which was completely unexplored. These revolutionary new techniques are now applied to solve conic linear problems. This makes it possible to model and solve large classes of essentially nonlinear optimization problems as efficiently as LP problems. This volume gives an overview of the latest developments of such `High Performance Optimization Techniques'. The first part is a thorough treatment of interior point methods for semidefinite programming problems. The second part reviews today's most exciting research topics and results in the area of convex optimization. Audience: This volume is for graduate students and researchers who are interested in modern optimization techniques.
Book Synopsis Stochastic Analysis, Filtering, and Stochastic Optimization by : George Yin
Download or read book Stochastic Analysis, Filtering, and Stochastic Optimization written by George Yin and published by Springer Nature. This book was released on 2022-04-22 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a collection of research works to honor the late Professor Mark H.A. Davis, whose pioneering work in the areas of Stochastic Processes, Filtering, and Stochastic Optimization spans more than five decades. Invited authors include his dissertation advisor, past collaborators, colleagues, mentees, and graduate students of Professor Davis, as well as scholars who have worked in the above areas. Their contributions may expand upon topics in piecewise deterministic processes, pathwise stochastic calculus, martingale methods in stochastic optimization, filtering, mean-field games, time-inconsistency, as well as impulse, singular, risk-sensitive and robust stochastic control.
Book Synopsis Online Portfolio Selection by : Bin Li
Download or read book Online Portfolio Selection written by Bin Li and published by CRC Press. This book was released on 2018-10-30 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.
Book Synopsis Discrete Choice Methods with Simulation by : Kenneth Train
Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Book Synopsis Empirical Asset Pricing by : Wayne Ferson
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Book Synopsis Portfolio Decision Analysis by : Ahti Salo
Download or read book Portfolio Decision Analysis written by Ahti Salo and published by Springer Science & Business Media. This book was released on 2011-08-12 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio Decision Analysis: Improved Methods for Resource Allocation provides an extensive, up-to-date coverage of decision analytic methods which help firms and public organizations allocate resources to 'lumpy' investment opportunities while explicitly recognizing relevant financial and non-financial evaluation criteria and the presence of alternative investment opportunities. In particular, it discusses the evolution of these methods, presents new methodological advances and illustrates their use across several application domains. The book offers a many-faceted treatment of portfolio decision analysis (PDA). Among other things, it (i) synthesizes the state-of-play in PDA, (ii) describes novel methodologies, (iii) fosters the deployment of these methodologies, and (iv) contributes to the strengthening of research on PDA. Portfolio problems are widely regarded as the single most important application context of decision analysis, and, with its extensive and unique coverage of these problems, this book is a much-needed addition to the literature. The book also presents innovative treatments of new methodological approaches and their uses in applications. The intended audience consists of practitioners and researchers who wish to gain a good understanding of portfolio decision analysis and insights into how PDA methods can be leveraged in different application contexts. The book can also be employed in courses at the post-graduate level.
Book Synopsis Handbook on Information Technology in Finance by : Detlef Seese
Download or read book Handbook on Information Technology in Finance written by Detlef Seese and published by Springer Science & Business Media. This book was released on 2008-05-27 with total page 812 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook contains surveys of state-of-the-art concepts, systems, applications, best practices as well as contemporary research in the intersection between IT and finance. Included are recent trends and challenges, IT systems and architectures in finance, essential developments and case studies on management information systems, and service oriented architecture modeling. The book shows a broad range of applications, e.g. in banking, insurance, trading and in non-financial companies. Essentially, all aspects of IT in finance are covered.
Book Synopsis Bayesian Methods in Finance by : Svetlozar T. Rachev
Download or read book Bayesian Methods in Finance written by Svetlozar T. Rachev and published by John Wiley & Sons. This book was released on 2008-02-13 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management—since these are the areas in finance where Bayesian methods have had the greatest penetration to date.
Book Synopsis Handbook of Financial Econometrics by : Yacine Ait-Sahalia
Download or read book Handbook of Financial Econometrics written by Yacine Ait-Sahalia and published by Elsevier. This book was released on 2009-10-19 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of original articles—8 years in the making—shines a bright light on recent advances in financial econometrics. From a survey of mathematical and statistical tools for understanding nonlinear Markov processes to an exploration of the time-series evolution of the risk-return tradeoff for stock market investment, noted scholars Yacine Aït-Sahalia and Lars Peter Hansen benchmark the current state of knowledge while contributors build a framework for its growth. Whether in the presence of statistical uncertainty or the proven advantages and limitations of value at risk models, readers will discover that they can set few constraints on the value of this long-awaited volume. - Presents a broad survey of current research—from local characterizations of the Markov process dynamics to financial market trading activity - Contributors include Nobel Laureate Robert Engle and leading econometricians - Offers a clarity of method and explanation unavailable in other financial econometrics collections
Book Synopsis Dynamic Linear Models with R by : Giovanni Petris
Download or read book Dynamic Linear Models with R written by Giovanni Petris and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Book Synopsis Risk and Asset Allocation by : Attilio Meucci
Download or read book Risk and Asset Allocation written by Attilio Meucci and published by Springer Science & Business Media. This book was released on 2009-05-22 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discusses in the practical and theoretical aspects of one-period asset allocation, i.e. market Modeling, invariants estimation, portfolia evaluation, and portfolio optimization in the prexence of estimation risk The book is software based, many of the exercises simulate in Matlab the solution to practical problems and can be downloaded from the book's web-site
Book Synopsis Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) by : Cheng Few Lee
Download or read book Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) written by Cheng Few Lee and published by World Scientific. This book was released on 2020-07-30 with total page 5053 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
