High frequency financial data allows us to learn more about volatility, volatility of volatility and jumps.  One of the key techniques developed in the literature in recent years has been bipower variation and its multipower extension, which estimates time-varying volatility robustly to...

This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing...

India's development experience over the past fifty years suggests that the increasing importance of the services sector deserves analysis.  The literature on structural change has emphasised changing patterns of demand as an explanation for the increasing importance of the services sector.  In...

I discuss models which allow the local level model, which rationalised exponentially weighted moving averages, to have a time-varying signal/noise ratio.  I call this a martingale component model.  This makes the rate of discounting of data local.  I show how to handle such models...

We study the role of the propensity scores in estimating treatment effects for the treated with a multi-valued treatment.  Assume assignment to one of the multiple treatments is random given observed characteristics.  Valid causal comparisons for the subpopulation who has been treated a...

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...

Motivated by features of low latency data in finance we study in detail discrete-valued Levy processes as the basis of price processes for high frequency econometrics.  An important case of this is a Skellam process, which is the difference of two independent Poisson processes.  We propose a...

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...

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...