A Cyclical Model of Exchange Rate Volatility
AbstractIn 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Bristol, UK in its series Bristol Economics Discussion Papers with number 10/618.
Length: 31 pages
Date of creation: Oct 2010
Date of revision:
Conditional volatility; Intraday range; Hodrick-Prescott filter;
Other versions of this item:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-10-02 (All new papers)
- NEP-ECM-2010-10-02 (Econometrics)
- NEP-IFN-2010-10-02 (International Finance)
- NEP-MON-2010-10-02 (Monetary Economics)
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- Kellard, Neil & Dunis, Christian & Sarantis, Nicholas, 2010. "Foreign exchange, fractional cointegration and the implied-realized volatility relation," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 882-891, April.
- Marianne Baxter & Robert G. King, 1999.
"Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series,"
The Review of Economics and Statistics,
MIT Press, vol. 81(4), pages 575-593, November.
- Tom Doan, . "BKFILTER: RATS procedure to implement band pass filter using Baxter-King method," Statistical Software Components RTS00026, Boston College Department of Economics.
- Marianne Baxter & Robert G. King, 1995. "Measuring Business Cycles Approximate Band-Pass Filters for Economic Time Series," NBER Working Papers 5022, National Bureau of Economic Research, Inc.
- Peter Christoffersen & Kris Jacobs & Chayawat Ornthanalai & Yintian Wang, 2008.
"Option Valuation with Long-run and Short-run Volatility Components,"
CREATES Research Papers
2008-11, School of Economics and Management, University of Aarhus.
- Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
- Peter Christoffersen & Kris Jacobs & Yintian Wang, 2004. "Option Valuation with Long-run and Short-run Volatility Components," CIRANO Working Papers 2004s-56, CIRANO.
- Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
- Mikhail Chernov & A. Ronald Gallant & Eric Ghysels & George Tauchen, 2002.
"Alternative Models for Stock Price Dynamics,"
CIRANO Working Papers
- Lawrence J. Christiano & Terry J. Fitzgerald, 2003.
"The Band Pass Filter,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, 05.
- Lawrence J. Christiano & Terry J. Fitzgerald, 1999. "The Band Pass Filter," NBER Working Papers 7257, National Bureau of Economic Research, Inc.
- Lawrence J. Christiano & Terry J. Fitzgerald, 1999. "The Band pass filter," Working Paper 9906, Federal Reserve Bank of Cleveland.
- Tom Doan, . "CFFILTER: RATS procedure to perform band pass filter using Christiano-Fitzgerald method," Statistical Software Components RTS00034, Boston College Department of Economics.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-44, January.
- A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999.
"Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance,"
The Review of Economics and Statistics,
MIT Press, vol. 81(4), pages 617-631, November.
- Gallant, A. Ronald & Hsu, Chien-Te & Tauchen, George, 2000. "Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance," Working Papers 00-04, Duke University, Department of Economics.
- Bollerslev, Tim & Zhou, Hao, 2002.
"Estimating stochastic volatility diffusion using conditional moments of integrated volatility,"
Journal of Econometrics,
Elsevier, vol. 109(1), pages 33-65, July.
- Tim Bollerslev & Hao Zhou, 2001. "Estimating stochastic volatility diffusion using conditional moments of integrated volatility," Finance and Economics Discussion Series 2001-49, Board of Governors of the Federal Reserve System (U.S.).
- Maheu John, 2005. "Can GARCH Models Capture Long-Range Dependence?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-43, December.
- repec:att:wimass:9317 is not listed on IDEAS
- West, K.D. & Cho, D., 1993.
"The Predictive Ability of Several Models of Exchange Rate Volatility,"
9317r, Wisconsin Madison - Social Systems.
- West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
- Kenneth D. West & Dongchul Cho, 1994. "The Predictive Ability of Several Models of Exchange Rate Volatility," NBER Technical Working Papers 0152, National Bureau of Economic Research, Inc.
- Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 1-46 National Bureau of Economic Research, Inc.
- Peter F. Christoffersen & Francis X. Diebold, 1997.
"How Relevant is Volatility Forecasting for Financial Risk Management?,"
Center for Financial Institutions Working Papers
97-45, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
- Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-080, New York University, Leonard N. Stern School of Business-.
- Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," NBER Working Papers 6844, National Bureau of Economic Research, Inc.
- Brandt, Michael W. & Jones, Christopher S., 2006. "Volatility Forecasting With Range-Based EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 470-486, October.
- Chou, Ray Yeutien, 2005. "Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 561-82, June.
- Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
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