香港大學本科畢業，1972年獲美國哥倫比亞大學統計學博士學位。現為美國斯坦福大學教授。1983年獲國際統計學界的考普斯“總統奬”。 黎子良教授的主要研究領域包括序列實驗、自適應設計和控製、隨機最優化、時間序列和預測、變點監測、隱馬爾可夫模型和粒子濾波、經驗貝葉斯模型、多元生存分析、概率理論和隨機過程、生物統計、計量經濟學、定量金融和風險控製。 南開大學本科畢業，2005年獲斯坦福大學統計學博士學位。現為紐約州立大學石溪分校助理教授。 邢海鵬的主要研究領域為定量金融、多變點檢測分析及其在計量經濟學、工程及生物學上的應用。

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.