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
|Author||: Yacine Aït-Sahalia,Lars Peter Hansen|
|Release Date||: 2010|
|ISBN 10||: 9780444535542|
|Pages||: 329 pages|
Vol 1 covers fundamental econometric techniques and tools on recent advances in financial econometrics. Parametric and nonparametric, in continuous time and discrete time, these techniques and tools include Markov processes, a system for categorizing volatility concepts, a simulated method of moments indicator, and models for the timing of events. Together they reveal the ways that local characterizations can lead to long-run implications and how relationships between observed and unobserved values can be inferred. Vol 2 covers important research even as they make unique empirical contributions to the literature. These subjects are familiar: portfolio choice, trading volume, the risk-return tradeoff, option pricing, bond yields, and the management, supervision, and measurement of extreme and infrequent risks. Yet their treatments are exceptional, drawing on current data and evidence to reflect recent events and scholarship. This set is the collection of Volumes 1 & 2. Its contributors include Nobel Laureate Robert Engle and leading econometricians. It offers a clarity of method and explanation unavailable in other financial econometrics collections.
|Author||: Cheng-few Lee,John C Lee|
|Publisher||: World Scientific|
|Release Date||: 2020-07-30|
|ISBN 10||: 9811202400|
|Pages||: 5056 pages|
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.
A comprehensive guide to financial econometrics Financial econometrics is a quest for models that describe financial time series such as prices, returns, interest rates, and exchange rates. In Financial Econometrics, readers will be introduced to this growing discipline and the concepts and theories associated with it, including background material on probability theory and statistics. The experienced author team uses real-world data where possible and brings in the results of published research provided by investment banking firms and journals. Financial Econometrics clearly explains the techniques presented and provides illustrative examples for the topics discussed. Svetlozar T. Rachev, PhD (Karlsruhe, Germany) is currently Chair-Professor at the University of Karlsruhe. Stefan Mittnik, PhD (Munich, Germany) is Professor of Financial Econometrics at the University of Munich. Frank J. Fabozzi, PhD, CFA, CFP (New Hope, PA) is an adjunct professor of Finance at Yale University’s School of Management. Sergio M. Focardi (Paris, France) is a founding partner of the Paris-based consulting firm The Intertek Group. Teo Jasic, PhD, (Frankfurt, Germany) is a senior manager with a leading international management consultancy firm in Frankfurt.
|Author||: Burcu Adıgüzel Mercangöz|
|Publisher||: Springer Nature|
|Release Date||: 2021-03-21|
|ISBN 10||: 3030541088|
|Pages||: 456 pages|
This handbook presents emerging research exploring the theoretical and practical aspects of econometric techniques for the financial sector and their applications in economics. By doing so, it offers invaluable tools for predicting and weighing the risks of multiple investments by incorporating data analysis. Throughout the book the authors address a broad range of topics such as predictive analysis, monetary policy, economic growth, systemic risk and investment behavior. This book is a must-read for researchers, scholars and practitioners in the field of economics who are interested in a better understanding of current research on the application of econometric methods to financial sector data.
A broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing.
High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.
An accessible guide to the growing field of financial econometrics As finance and financial products have become more complex, financial econometrics has emerged as a fast-growing field and necessary foundation for anyone involved in quantitative finance. The techniques of financial econometrics facilitate the development and management of new financial instruments by providing models for pricing and risk assessment. In short, financial econometrics is an indispensable component to modern finance. The Basics of Financial Econometrics covers the commonly used techniques in the field without using unnecessary mathematical/statistical analysis. It focuses on foundational ideas and how they are applied. Topics covered include: regression models, factor analysis, volatility estimations, and time series techniques. Covers the basics of financial econometrics—an important topic in quantitative finance Contains several chapters on topics typically not covered even in basic books on econometrics such as model selection, model risk, and mitigating model risk Geared towards both practitioners and finance students who need to understand this dynamic discipline, but may not have advanced mathematical training, this book is a valuable resource on a topic of growing importance.
|Author||: Luc Bauwens,Christian M. Hafner,Sebastien Laurent|
|Publisher||: John Wiley & Sons|
|Release Date||: 2012-03-22|
|ISBN 10||: 1118272056|
|Pages||: 568 pages|
A complete guide to the theory and practice of volatility modelsin financial engineering Volatility has become a hot topic in this era of instantcommunications, spawning a great deal of research in empiricalfinance and time series econometrics. Providing an overview of themost recent advances, Handbook of Volatility Models and TheirApplications explores key concepts and topics essential formodeling the volatility of financial time series, both univariateand multivariate, parametric and non-parametric, high-frequency andlow-frequency. Featuring contributions from international experts in the field,the book features numerous examples and applications fromreal-world projects and cutting-edge research, showing step by stephow to use various methods accurately and efficiently whenassessing volatility rates. Following a comprehensive introductionto the topic, readers are provided with three distinct sectionsthat unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and StochasticVolatility presents ARCH and stochastic volatility models, with afocus on recent research topics including mean, volatility, andskewness spillovers in equity markets Other Models and Methods presents alternative approaches, suchas multiplicative error models, nonparametric and semi-parametricmodels, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement ofvolatility by realized variances and covariances, guiding readerson how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications isan essential reference for academics and practitioners in finance,business, and econometrics who work with volatility models in theireveryday work. The book also serves as a supplement for courses onrisk management and volatility at the upper-undergraduate andgraduate levels.
Debt Markets and Investments provides an overview of the dynamic world of markets, products, valuation, and analysis of fixed income and related securities. Experts in the field, practitioners and academics, offer both diverse and in-depth insights into basic concepts and their application to increasingly intricate and real-world situations. This volume spans the entire spectrum from theoretical to practical, while attempting to offer a useful balance of detailed and user-friendly coverage. The volume begins with the basics of debt markets and investments, including basic bond terminology and market sectors. Among the topics covered are the relationship between fixed income and other asset classes as well as the differences in fundamental risk. Particular emphasis is given to interest rate risk as well as credit risks as well as those associated with inflation, liquidity, reinvestment, and ESG. Authors then turn to market sectors, including government debt, municipal bonds, the markets for corporate bonds, and developments in securitized debt markets along with derivatives and private debt markets. The third section focuses on models of yield curves, interest rates, and swaps, including opportunities for arbitrage. The next two sections focus on bond and securitized products, from sovereign debt and mutual funds focused on bonds to how securitization has increased liquidity through such innovations as mortgaged-and asset- backed securities, as well as collateralized debt-, bond-, and loan obligations. Authors next discuss various methods of valuation of bonds and securities, including the use of options and derivatives. The volume concludes with discussions of how debt can play a role in financial strategies and portfolio creation. Readers interested in a broad survey will benefit as will those looking for more in-depth presentations of specific areas within this field of study. In summary, the book provides a fresh look at this intriguing and dynamic but often complex subject.
An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization. The Handbook in Monte Carlo Simulation features: An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach An accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation The Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics is a complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation.