Motivated by extensive evidence that stock-return correlations are stochastic, we analyze whether the risk of correlation changes (affecting diversification benefits) is priced. We propose a direct and intuitive test by comparing option-implied correlations between stock returns (obtained by...

We propose a novel model-free approach to obtain the joint risk-neutral distribution among several assets that is consistent with market prices of options on these assets and their weighted index. In an empirical application, we use options on the S&P 500 index and its nine industry sectors. The...

Many asset pricing theories treat the cross-section of returns volatility and correlations as two intimately related quantities driven by common factors, which hinders achieving a neat definition of a correlation premium. We formulate a model without factors, but with a continuum of securities...

We study a new constrained equity premium forecasting approach which employs the option-implied lower bounds for the conditional market premium from Martin (2017) and Chabi-Yo and Loudis (2020), respectively, as forecast constraints. This constrained approach delivers considerable out-of-sample...

In a one-period economy, Martin (2017) and Chabi-Yo and Loudis (2020) derive bounds for the equity risk premium that use options of the same maturity as the horizon at which the premium is measured. In contrast, we provide an expression and an empirical methodology to measure the premium at a...

Skewness is specifically considered to develop semi-parametric upper bounds for option prices and expected payoffs for call options. Bounds on variance default swaps, a new asset, and for the variance risk premium are derived.The Technical Proof for this paper is available at the following URL:...

We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. This approach takes advantage of boosting's feature selection capability to select an optimal combination of...