This paper analyzes empirical market utility functions and pricing kernels derived from the DAX and DAX option data for three market regimes. A consistent parametric framework of stochastic volatility is used. All empirical market utility functions show a region of risk proclivity that is...

The news impact curve of EGARCH captures the asymmetric impact of negative news on volatility. It also captures the impact of large shocks, negative and positive. The interpretation of the curve is complicated by its composition, making it difficult to interpret its coefficients and the...

The predominant fear in capital markets is that of a price spike. Commodity markets differ in that there is a fear of both upward and down jumps, this results in implied volatility curves displaying distinct shapes when compared to equity markets. The use of a novel functional data analysis...

March 2020 packed 2 ½ years of normal U.S. stock market volatility into one month, making it the most volatile month on record. Daily variability clocked in at 6%, six times higher than the average over the past 90 years. How should an investor respond to such volatility? In this article we...

AdaBoost tweaks the sample weight for each training set used in the iterative process, however, it is demonstrated that it provides more correlated errors as the boosting iteration proceeds if models’ accuracy is high enough. Therefore, in this study, we propose a novel way to improve the...

This paper estimates dynamic factors from the term structure of credit spreads and the term structure of equity option implied volatilities, and it provides a comprehensive characterization of the dynamic relationships among those credit spread factors and equity volatility factors. The paper...

We show that there is strong commonality in the volatility of a wide range of diversified equity portfolios. Common factor volatility (CFV) exists even when factor or anomaly returns are market-adjusted and does not appear to be attributable to common microstructure noise or a lack of...

Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal...