We investigate the stock return volatility predictability using firm’s fundamental risk with machine learning approaches in China’s stock market. We find the machine learning models substantially improve the out-of-sample performance of fundamental risk in forecasting future volatility. The...

This paper investigates US Treasury market volatility and develops new ways of dealing with the underlying interest rate volatility risk. We adopt an innovative approach which is based on a class of model-free interest rate volatility (VXI) indices we derive from options traded on the CBOE. The...

The field of computational finance is evolving ever faster. This book collects a number of novel contributions on the use of computational methods and techniques for modelling financial asset prices, returns, and volatility, and on the use of numerical methods for pricing, hedging, and risk...

We propose a modelling treatment for the option-implied risk neutral distribution (RND) which disaggregates its long-term and short-term dynamics. Long memory parameters calibrated on the RND moments serve as tractable mathematical constructs to filter out effects of smooth structural change...