Statistical Models and Methods for Financial Markets (Springer Texts in Statistics) - Softcover

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Jay Bartroff is Associate Professor of Mathematics at the University of Southern California where he is a member of the Laboratory of Applied Pharmacokinetics at the USC Keck School of Medicine.  He is a leading expert on group sequential and multistage adaptive statistical procedures and their applications to clinical trial designs, and he is a sought-after consultant in academia and industry.  Tze Leung Lai is Professor of Statistics, and by courtesy, of Health Research and Policy and of the Institute of Computational and Mathematical Engineering at Stanford University, where he is the Director of the Financial and Risk Modeling Institute and Co-director of the Biostatistics Core at the Stanford Cancer Institute and of the Center for Innovative Study Design at the School of Medicine. He made seminal contributions to sequential analysis, innovative clinical trial designs, adaptive methods, survival analysis, nonlinear and generalized mixed models, hybrid resampling methods, and received the Committee of Presidents of Statistical Societies (COPSS) Award in 1983.  Mei-Chiung Shih is Assistant Professor of Biostatistics and a member of the Stanford Cancer Institute and of the Center for Innovative Study Design at the School of Medicine at Stanford University.  She is also Associate Director for Scientific and Technical Operations at the Department of Veterans Affairs (VA) Cooperative Studies Program Coordinating Center at Palo Alto Health Care System.  She is a leading expert on group sequential and adaptive designs and inference of clinical trials, longitudinal and survival data analysis, and has been leading the design, conduct and analysis of several large trials at the VA.

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This book presents statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modeling and decision making. Part I provides basic background in statistics, which includes linear regression and extensions to generalized linear models and nonlinear regression, multivariate analysis, likelihood inference and Bayesian methods, and time series analysis. It also describes applications of these methods to portfolio theory and dynamic models of asset returns and their volatilities. Part II presents advanced topics in quantitative finance and introduces a substantive-empirical modeling approach to address the discrepancy between finance theory and market data. It describes applications to option pricing, interest rate markets, statistical trading strategies, and risk management. Nonparametric regression, advanced multivariate and time series methods in financial econometrics, and statistical models for high-frequency transactions data are also introduced in this connection.

The book has been developed as a textbook for courses on statistical modeling in quantitative finance in master's level financial mathematics (or engineering) and computational (or mathematical) finance programs. It is also designed for self-study by quantitative analysts in the financial industry who want to learn more about the background and details of the statistical methods used by the industry. It can also be used as a reference for graduate statistics and econometrics courses on regression, multivariate analysis, likelihood and Bayesian inference, nonparametrics, and time series, providing concrete examples and data from financial markets to illustrate the statistical methods.

Tze Leung Lai is Professor of Statistics and Director of Financial Mathematics at Stanford University. He received the Ph.D. degree in 1971 from Columbia University, where he remained on the faculty until moving to Stanford University in 1987. He received the Committee of Presidents of Statistical Societies Award in 1983 and is an elected member of Academia Sinica and the International Statistical Institute. His research interests include quantitative finance and risk management, sequential statistical methodology, stochastic optimization and adaptive control, probability theory and stochastic processes, econometrics, and biostatistics.

Haipeng Xing is Assistant Professor of Statistics at Columbia University. He received the Ph.D. degree in 2005 from Stanford University. His research interests include financial econometrics and engineering, time series modeling and adaptive control, fault detection, and change-point problems.