Analyze and Forecast Stock Market Volatility

ISBN-13: 9783639151824 / Angielski / Miękka / 2009 / 132 str.

Qianru Li

Opis

Research on conditional volatility of asset prices has been a topic of expanding interest in the field of finance. It is known that volatility is inherently unobservable, thus the selection of models and how to define them is crucial for financial research. This book attempts to analyze and forecast stock market volatility by both parametric and non-parametric approaches. Augmented GARCH models with an investor sentiment effect derived from trading volume are compared with conventional GARCH models. Furthermore, A Monte Carlo experiment is adopted to generate stock-return data and a neural network approach is applied to forecast Value-at-Risk of the stock market. Results suggest that accuracy of GARCH models is improved by accounting for the volume effect and non-parametric neural network technique can be a good alternative to forecasting stock market volatility.

O autorze

Qianru Li
Ph.D. Utah State University, USA, 2008