Forecasting Currency Volatility: A Comparison of Implied Volatilities and AR(FI)MA Models

40 Pages Posted: 16 Mar 2002

See all articles by Shiu-yan Eddie Pong

Shiu-yan Eddie Pong

Lancaster University - Department of Accounting and Finance

Mark B. Shackleton

Lancaster University - Department of Accounting and Finance

Stephen J. Taylor

Lancaster University - Department of Accounting and Finance

Xinzhong Xu

Peking University - Guanghua School of Management

Multiple version iconThere are 2 versions of this paper

Date Written: October 15, 2003

Abstract

We compare forecasts of the realized volatility of the pound, mark and yen exchange rates against the dollar, calculated from intraday rates, over horizons ranging from one day to three months. Our forecasts are obtained from a short memory ARMA model, a long memory ARFIMA model, a GARCH model and option implied volatilities. We find intraday rates provide the most accurate forecasts for the one-day and one-week forecast horizons while implied volatilities are at least as accurate as the historical forecasts for the one-month and three-month horizons. The superior accuracy of the historical forecasts, relative to implied volatilities, comes from the use of high frequency returns, and not from a long memory specification. We find significant incremental information in historical forecasts, beyond the implied volatility information, for forecast horizons up to one week.

Note: Previously titled "Forecasting Sterling/Dollar Volatility: A Comparison of Implied Volatilities and AR(FI)MA Models"

Keywords: Realized volatility, Fractional integration, Forecasting, Implied volatilities, Exchange rates

JEL Classification: C22, C53, G13, G14

Suggested Citation

Pong, Shiu-yan Eddie and Shackleton, Mark B. and Taylor, Stephen J. and Xu, Gary Xinzhong, Forecasting Currency Volatility: A Comparison of Implied Volatilities and AR(FI)MA Models (October 15, 2003). Available at SSRN: https://ssrn.com/abstract=301981 or http://dx.doi.org/10.2139/ssrn.301981

Shiu-Yan Eddie Pong

Lancaster University - Department of Accounting and Finance ( email )

The Management School
Lancaster LA1 4YX
United Kingdom
+ 44 1524 847321 (Phone)

Mark B. Shackleton

Lancaster University - Department of Accounting and Finance ( email )

The Management School
Lancaster LA1 4YX
United Kingdom
44 1524 594131 (Phone)
44 1524 847321 (Fax)

Stephen J. Taylor (Contact Author)

Lancaster University - Department of Accounting and Finance ( email )

The Management School
Lancaster LA1 4YX
United Kingdom
+ 44 15 24 59 36 24 (Phone)
+ 44 15 24 84 73 21 (Fax)

HOME PAGE: http://www.lancs.ac.uk/staff/afasjt

Gary Xinzhong Xu

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
1,482
Abstract Views
8,012
Rank
32,012
PlumX Metrics