Likelihood based estimation of the parameters of state space models can be carried out via a particle filter.  In this paper we show how to make valid inference on such parameters when the model is incorrect.  In particular we develop a simulation strategy for computing sandwich covariance...

We will review the econometrics of non-parametric estimation of the components of the variation of asset prices. This very active literature has been stimulated by the recent advent of complete records of transaction prices, quote data and order books. In our view the interaction of the new data...

We propose a new measure of risk, based entirely on downward moves measured using high frequency data.  Realised semivariances are shown to have important predictive qualities for future market volatility.  The theory of these new measures is spelt out, drawing on some new results from...

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices.  We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading.  It is the first...

Suppose we wish to carry out likelihood based inference but we solely have an unbiased simulation based estimator of the likelihood.  We note that unbiasedness is enough when the estimated likelihood is used inside a Metropolis-Hastings algorithm.  This result has recently been introduced in...

This paper studies in some detail a class of high frequency based volatility (HEAVY) models.  These models are direct models of daily asset return volatility based on realized measures constructed from high frequency data.  Our analysis identifies that the models have momentum and mean...

Building models for high dimensional portfolios is important in risk management and asset allocation.  Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied...

In this paper we review the history and recent developments of stochastic volatility, which is the main way financial economists and mathematical finance specialists model time varying volatility.

A key ingredient of many particle filters is the use of the sampling importance resampling algorithm (SIR), which transforms a sample of weighted draws from a prior distribution into equally weighted draws from a posterior distribution.  We give a novel analysis of the SIR algorithm and analyse...

We investigate the properties of the composite likelihood (CL) method for (T x NT) GARCH panels.  The defining feature of a GARCH panel with time series length T is that, while nuisance parameters are allowed to vary across NT series, other parameters of interest are assumed to be common.  CL...