We propose a new, price-based measure of information risk called abnormal idiosyncratic volatility (AIV) that captures information asymmetry faced by uninformed investors. AIV is the idiosyncratic volatility prior to information events in excess of normal levels. Using earnings announcements as...

When Bayesian risk-averse investors are uncertain about their assets' cash flows' exposure to systematic risk, stock prices react more to news in downturns than in upturns, implying higher volatility in downturns and negatively skewed returns. The reason is that, in good times, less desirable...

Despite momentum's strong historical performance, its returns have large negative skewness and occasionally experiences persistent strings of sharp negative returns, referred as "momentum crashes" in the recent literature. I argue that momentum crashes are due to crowded trades which push prices...

This research considers the strategies on the initial public offering of company equity at the stock exchanges in the imperfect highly volatile global capital markets with the nonlinearities. We provide the IPO definition and compare the initial listing requirements on the various markets. We...

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...

Define the number of buy-sell “switching points” as the number of times that individual traders change the direction of their trading. Based on the hypothesis that switching points take place in business time, market microstructure invariance predicts that the aggregate number of switching...

This paper applies a Bayesian break method to studying the empirical time-varying relations between stock price ratios and subjective expectations across the market and 30 industry portfolios monthly from 1976 to 2020. Cash flow expectations unconditionally explain 80% of price variations since...

We investigate the effect of including variance derivatives as calibration and hedging instruments for pricing and hedging exotic structures. This is studied empirically using market data for SPX and VIX derivatives applied in a stochastic volatility jump diffusion model

An enhanced option pricing framework that makes use of both continuous and discontinuous time paths based on a geometric Brownian motion and Poisson-driven jump processes respectively is performed in order to better fit with real-observed stock price paths while maintaining the analytical...

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...