This chapter surveys the methods available for extracting information from option prices that
can be used in forecasting. We consider option-implied volatilities, skewness, kurtosis, and …

We develop a GARCH option model with a new pricing kernel allowing for a variance
premium. While the pricing kernel is monotonic in the stock return and in variance, its …

The empirical pricing kernels estimated from index options are non-monotone (Rosenberg
and Engle, 2002; Bakshi, Madan, and Panayotov, 2010) and the corresponding risk …

In this paper, we propose a general data-driven framework that unifies the valuation and risk
measurement of financial derivatives, which is especially useful in markets with thinly-traded …

This paper suggests a new method of implementing the principle of maximum entropy to
retrieve the risk neutral density of future stock, or any other asset, returns from European call …

This study presents the uncertainty analysis of a distribution network (DNR) caused by unit
power output control (UPC) distributed generations (DGs)(solar, wind) and load demand by …

Entropy pricing applies notions of information theory to derive the theoretical value of
options. This paper employs the maximum entropy (ME) formulation of option pricing, given …

In this paper we explore important implications of capturing volatility risk premium (VRP)
within a parametric GARCH setting. We study the transmission mechanism of shocks from …

Lévy processes have been successfully applied in the modeling of financial assets. Useful
information such as implied volatility, skewness, and risk-preferences can be derived from …

We propose a non-structural method to retrieve the risk-neutral density (RND) implied by
options on the CBOE Volatility Index (VIX). The methodology is based on orthogonal …