We develop a specification test for discretely-sampled jump-diffusions, based on a comparison of a nonparametric estimate of the transition density or distribution function to their corresponding parametric counterparts. As a special case, our method applies to pure diffusions. We propose three...

We propose an empirical implementation of the consumption-investment problem using the martingale representation alternative to dynamic programming. Our method is based on the direct observation of state prices from options data. This greatly simplifies the investor's task of specifying the...

We compare the forecasts of Quadratic Variation given by the Realized Volatility (RV) and the Two Scales Realized Volatility (TSRV) computed from high frequency data in the presence of market microstructure noise, under several different dynamics for the volatility process and assumptions on the...

Typical value-at-risk (VAR) calculations involve the probabilities of extreme dollar losses, based on the statistical distributions of market prices. Such quantities do not account for the fact that the same dollar loss can have two very different economic valuations, depending on business...

Classical statistics suggest that for inference purposes one should always use as much data as is available. We study how the presence of market microstructure noise in high-frequency financial data can change that result. We show that the optimal sampling frequency at which to estimate the...

Implicit in the prices of traded financial assets are Arrow-Debreu prices or, with continuous states, the state-price density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and derive its asymptotic sampling theory. This estimator provides an arbitrage-free...

This paper develops and estimates a continuous-time model of a financial market where investors' trading strategies and the market maker's rule of price adjustments are best response to each other. There exists an equilibrium where the market maker finds it optimal to add volatility to the price...

This paper derives a nonparametric estimation procedure for continuous-time models and provides the asymptotic distribution of the estimator. Since the pricing of derivative securities depends crucially on the form of the instantaneous volatility of the underlying asset, the diffusion function...

This paper proposes to build "implied stochastic volatility models" designed to fit option-implied volatility data, and implements a method to construct such models. The method is based on explicitly linking shape characteristics of the implied volatility surface to the specification of the...

We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the usual assumption of betas being piecewise constant. This property makes our framework particularly suitable to study individual stocks. We provide an inference framework for all...