This paper evaluates the underperformance of individual equity options relative to their replicating portfolios. Considering a high-dimensional set of variables, we use a machine learning approach to identify the characteristics of options and their underlying stocks that provide incremental...

The paper studies methods of dynamic estimation of volatility for financial time series. We suggest to estimate the volatility as the implied volatility inferred from some artificial 'dynamically purified' price process that in theory allows to eliminate the impact of the stock price movements....

In this paper, we study the statistical properties of the moneyness scaling transformation by Leung and Sircar (2015). This transformation adjusts the moneyness coordinate of the implied volatility smile in an attempt to remove the discrepancy between the IV smiles for levered and unlevered ETF...

Numerous empirical studies find pricing kernels that are not-monotonically decreasing; the findings are at odds with the pricing kernel being marginal utility of a risk-averse, so-called representative agent. We study in detail the common procedure which estimates the pricing kernel as the ratio...

In order to better capture empirical phenomena, research on option price and implied volatility modeling increasingly advocates the use of nonparametric methods over simple functional forms. This, however, comes at a price, since these methods require dense observations to yield sensible...

In this work, we suggest a novel quadratic programming-based algorithm to generate an arbitrage-free call option surface. Our approach relies on a regression spline-based implementation of the framework proposed by Orosi (2011) who presents a multi-parameter extension of the models of Figlewski...