We study whether prices of traded options contain information about future extreme market events. Our option-implied conditional expectation of market loss due to tail events, or tail loss measure, predicts future market returns, magnitude, and probability of the market crashes, beyond and above...

We study whether option-implied conditional expectation of market loss due to tail events, or tail loss measure, contains information about future returns, especially the negative ones. Our tail loss measure predicts future market returns, magnitude, and probability of the market crashes, beyond...

This paper investigates whether ETF returns lead the returns of underlying bonds and similar style bond funds. Bond prices are often stale due to their lack of liquidity, and price discovery may occur in ETFs and then in underlying bonds. As predicted, we find that ETF returns predict its own...

We study the effects of the predictability in stock returns for the fair value of American Executive Stock Options (ESOs). By assuming a trending Ornstein-Uhlenbeck process for stock returns, we solve for the executive's optimal exercise policy using a methodology based on the least-squares...

This paper examines whether the inclusion of oil price shocks of different origin as exogenous variables in a wide set of GARCH-X models improves the accuracy of their volatility forecasts for spot and 1-year time-charter tanker freight rates. Kilian's (2009) oil price shocks of different origin...

We solve a dynamic general equilibrium model with generalized disappointment aversion preferences and continuous state endowment dynamics. We apply the framework to the term structure of interest rates and show that the model generates an upward sloping term structure of nominal interest rates,...

The role of public sentiment in stock market volatility has recently become increasingly relevant. Twitter, in theory, offers an inexpensive way to measure real-time public sentiment. We take advantage of a natural experiment to assess the potential improvement that social media adds to forecast...

This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of...

In this paper we propose a simple one-factor quantile regression model based on realized volatility to forecast Value-at-Risk (VaR). The model only uses daily realized volatility as input and thus simplifies estimation substantially compared with most other methodologies currently used to...

We derive risk-neutral option price formulas for plain-vanilla temperature futures derivatives on the basis of several multi-factor Ornstein-Uhlenbeck temperature models which allow for seasonality in the mean level and volatility. Our main innovation consists in an incorporation of omnipresent...