Stochastic Volatility: Selected Readings
本书收录了随机波动率（SV）模型领域的经典文献，该模型将波动率视为不可观测的随机过程，弥补了ARCH类模型与连续时间金融理论的脱节，适合对波动率建模和金融计量感兴趣的读者。
Although it was recognised many decades ago that financial markets are characterised by volatility which changes over time, it has only been in the last 20 years or so that a large interest has been shown by econometricians in time series models with time‐varying volatility. Undoubtedly, this was mainly due to Robert Engle’s seminal 1982 work on ARCH processes, which model the risky part of returns as the product process where ɛt is a martingale difference with unit conditional variance and (the conditional variance of the return) is a random variable measurable with respect to the past information set, say ℑt−1. A drawback of the ARCH class of models is that they do not link in well with the large body of continuous time models widely used in financial economics, e.g., to price derivatives. However, a different interpretation, much closer to modern finance theory, can be given to the product process in (1). In particular, σt might be viewed as being generated by an unobserved stochastic process (e.g., a latent autoregression), and not necessarily ℑt−1‐measurable. This assumption defines a class of models, which permit one to specify a separate stochastic process for volatility without having to worry about the implied distribution of the returns; this is what is referred to as the class of ‘stochastic volatility’ [SV] models, and it is to this class of processes that Neil Shephard has dedicated this volume of readings.