A Cyclical Model of Exchange Rate Volatility

In this paper, we investigate the long run dynamics of the intraday range of the GBP/USD, JPY/USD and CHF/USD exchange rates. We use a non-parametric filter to extract the low frequency component of the intraday range, and model the cyclical deviation of the range from the long run trend as a stationary autoregressive process. We find that the long run trend is time-varying but highly persistent, while the cyclical component is strongly mean reverting. This has important implications for modelling and forecasting volatility over both short and long horizons. As an illustration, we use the cyclical volatility model to generate out-of-sample forecasts of exchange rate volatility for horizons of up to one year under the assumption that the long run trend is fully persistent. As a benchmark, we compare the forecasts of the cyclical volatility model with those of the two-factor intraday range-based EGARCH model of Brandt and Jones (2006). Not only is the cyclical volatility model significantly easier to estimate than the EGARCH model, but it also offers a substantial improvement in out-of-sample forecast performance.Conditional volatility, Intraday range, Hodrick-Prescott filter

Dynamic hedge fund portfolio construction

Working paperIn this paper, we provide further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation, using monthly hedge fund index return data for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), we consider a broad set of multivariate GARCH models as well as the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996). We find that while multivariate GARCH models provide some improvement in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, we show that these results are robust across low-volatility and high-volatility sub-periods

A cyclical model of exchange rate volatility

Draft version issued as working paper by University of Exeter Business School. Final version published by Elsevier. Available online at http://www.journals.elsevier.com/journal-of-banking-and-finance/In this paper, we investigate the long run dynamics of the intraday range of the GBP/USD, JPY/USD and CHF/USD exchange rates. We use a non-parametric filter to extract the low frequency component of the intraday range, and model the cyclical deviation of the range from the long run trend as a stationary autoregressive process. We find that the long run trend is time-varying but highly persistent, while the cyclical component is strongly mean reverting. This has important implications for modelling and forecasting volatility over both short and long horizons. As an illustration, we use the cyclical volatility model to generate out-of-sample forecasts of exchange rate volatility for horizons of up to one year under the assumption that the long run trend is fully persistent. As a benchmark, we compare the forecasts of the cyclical volatility model with those of the two-factor intraday range-based EGARCH model of Brandt and Jones (2006). Not only is the cyclical volatility model significantly easier to estimate than the EGARCH model, but it also offers a substantial improvement in out-of-sample forecast performance