We derive an approximation to the bias in regression-based Monte Carlo estimators of American option values. This derivation holds for general asset-price processes of any dimensionality and for general pay-off structures. It uses the large sample properties of least-squares regression...

In this paper we propose a feasible way to price American options in a model with time varying volatility and conditional skewness and leptokurtosis using GARCH processes and the Normal inverse Gaussian distribution. We show how the risk neutral dynamics can be obtained in this model, we...

In the present paper we suggest to model Realized Volatility, an estimate of daily volatility based on high frequency data, as an Inverse Gaussian distributed variable with time varying mean, and we examine the joint properties of Realized Volatility and asset returns. We derive the appropriate...

Estimation of the volatility process of asset returns is of paramount importance in many financial applications particularly when the ultimate objective is the pricing of derivatives. Recently the use of high-frequency intraday data for estimating daily volatilities has received extensive...

In this paper we propose a feasible way to price American options in a model with time varying volatility and conditional skewness and leptokurtosis using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk neutral dynamics can be obtained in this model using the...

In this paper we propose a feasible way to price American options in a model with time-varying volatility and conditional skewness and leptokurtosis, using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk-neutral dynamics can be obtained in this model, we...

In this paper we consider option pricing using multivariate models for asset returns. Specifically, we demonstrate the existence of an equivalent martingale measure, we characterize the risk neutral dynamics, and we provide a feasible way for pricing options in this framework. Our application...