The last decade has seen substantial advances in the measurement, modelling and forecasting of volatility which has centered around the realized volatility literature. To date, most of the focus has been on the daily and monthly frequencies, with little attention on longer horizons such as the...

For the major foreign exchange rates, it is found that the optimal modelling frequency of volatility is weekly for forecast horizons ranging from 1 week up to 1 month. Autoregressive modelling is based on realized volatility measures computed from 30 min returns.

This paper proposes a novel derivation of the Hodrick-Prescott, Department of Economics (HP) minimisation problem which leads to a generalisation of the Hodrick-Prescott filter. The main result is the development of a new filter to extract a localised maximum likelihood estimate of the cycle...

This paper demonstrates that a conditional version of the Capital Asset Pricing Model (CAPM) explains the cross section of expected returns, just as well as the three factor model of Fama and French. This is achieved by measuring beta (systematic risk) with short-, medium- and long-run...

Value at Risk (VaR) forecasts have been increasingly accepted globally by both risk managers and regulators as a tool to identify and control exposure to financial market risk. However, modern portfolios are characterized by a constantly changing composition of security holdings that reflect...

The recent advent of high-frequency data and advances in financial econometrics allow market participants to evaluate the accuracy of different beta (systematic risk) measurements. Benchmarking against the monthly realized beta formed by 30-minute data, we compare the popular Fama--MacBeth...

In this paper, we show the effects that outliers have on estimation and inference for autoregressive conditional heteroskedasticity (ARCH) models. We propose for a wide class of ARCH models commonly estimated, an empirically tractable solution to this problem by replacing outliers with their...