We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

Abstract This paper is devoted to the study of asymptotic properties of the regression function kernel estimate in the setting of continuous time stationary and ergodic data. More precisely, considering the Nadaraya–Watson type estimator, say m̂ T ( x ) , of the l -indexed regression function...

This paper is devoted to the study of asymptotic properties of the regression function kernel estimate in the setting of continuous time stationary and ergodic data. More precisely, considering the Nadaraya–Watson type estimator, say m̂T(x), of the l-indexed regression function...

This article reviews the exciting and rapidly expanding literature on realized volatility. After presenting a general univariate framework for estimating realized volatilities, a simple discrete time model is presented in order to motivate the main results. A continuous time specification...

This paper examines whether or not a discrete-time econometric test for nonlinearity in mean may be used in cases where the data are believed to be generated in continuous time. It is demonstrated that appropriate bootstrapping techniques are required to yield a test statistic with sensible...

This paper explores and compares the empirical distribution of the US dollar–deutsche mark exchange rate returns with well-known continuous-times processes at different frequencies. We use a variety of parametric models to simulate the unconditional density of the exchange rate returns at...