Long memory and nonlinearity in conditional variances: A smooth transition FIGARCH model
This paper introduces the Smooth Transition version of FIGARCH model which is designed to account for both long memory and nonlinear dynamics in the conditional variance. Nonlinearity is introduced via a logistic transition function. The model can capture smooth changes in the volatility across different regimes as well as asymmetric response to negative and positive shocks and allows for nonzero thresholds. Simulations find that the Smooth Transition FIGARCH model outperforms the standard FIGARCH model when nonlinearity is present, and ignoring nonlinearity in the data may induce considerable costs in terms of bias and efficiency. Applications to exchange rate and stock market data show that the proposed model performs well both in-sample fit as well as in forecasting one-day ahead volatility.
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- Robert F. Engle & Victor K. Ng, 1991.
"Measuring and Testing the Impact of News on Volatility,"
NBER Working Papers
3681, National Bureau of Economic Research, Inc.
- Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-78, December.
- Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus, 2000. "Stationary Arch Models: Dependence Structure And Central Limit Theorem," Econometric Theory, Cambridge University Press, vol. 16(01), pages 3-22, February.
- Fornari, Fabio & Mele, Antonio, 1997.
"Sign- and Volatility-Switching ARCH Models: Theory and Applications to International Stock Markets,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 12(1), pages 49-65, Jan.-Feb..
- Fornari, F. & Mele, A., 1995. "Sign- and Volatility -Switching ARCH Models: Theory and Applications to International Stock Markets," Papers 251, Banca Italia - Servizio di Studi.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- Dacorogna, Michael M. & Muller, Ulrich A. & Nagler, Robert J. & Olsen, Richard B. & Pictet, Olivier V., 1993. "A geographical model for the daily and weekly seasonal volatility in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 12(4), pages 413-438, August.
- Lee, Junsoo & Degennaro, Ramon P, 2000. " Smooth Transition ARCH Models: Estimation and Testing," Review of Quantitative Finance and Accounting, Springer, vol. 15(1), pages 5-20, July.
- Lundbergh, Stefan & Teräsvirta, Timo, 1998.
"Evaluating GARCH models,"
SSE/EFI Working Paper Series in Economics and Finance
292, Stockholm School of Economics, revised 03 May 1999.
- Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
- Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
- Mika Meitz & Pentti Saikkonen, 2008.
"Parameter estimation in nonlinear AR-GARCH models,"
Economics Series Working Papers
396, University of Oxford, Department of Economics.
- Mika Meitz & Pentti Saikkonen, 2010. "Parameter estimation in nonlinear AR–GARCH models," Koç University-TUSIAD Economic Research Forum Working Papers 1002, Koc University-TUSIAD Economic Research Forum.
- Mika Meitz & Pentti Saikkonen, 2008. "Parameter estimation in nonlinear AR-GARCH models," CREATES Research Papers 2008-30, School of Economics and Management, University of Aarhus.
- Mika Meitz & Pentti Saikkonen, 2008. "Parameter Estimation in Nonlinear AR-GARCH Models," Economics Working Papers ECO2008/25, European University Institute.
- Pagan, Adrian R. & Schwert, G. William, 1990.
"Alternative models for conditional stock volatility,"
Journal of Econometrics,
Elsevier, vol. 45(1-2), pages 267-290.
- Pagan, A.R. & Schwert, G.W., 1989. "Alternative Models For Conditional Stock Volatility," Papers 89-02, Rochester, Business - General.
- Adrian R. Pagan & G. William Schwert, 1990. "Alternative Models For Conditional Stock Volatility," NBER Working Papers 2955, National Bureau of Economic Research, Inc.
- Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996.
"Fractionally integrated generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 74(1), pages 3-30, September.
- Tom Doan, . "RATS programs to replicate Baillie, Bollerslev, Mikkelson FIGARCH results," Statistical Software Components RTZ00009, Boston College Department of Economics.
- Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002.
"Smooth Transition Autoregressive Models — A Survey Of Recent Developments,"
Taylor & Francis Journals, vol. 21(1), pages 1-47.
- van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," SSE/EFI Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001.
- van Dijk, D.J.C. & Terasvirta, T. & Franses, Ph.H.B.F., 2000. "Smooth transition autoregressive models - A survey of recent developments," Econometric Institute Research Papers EI 2000-23/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993.
"On the relation between the expected value and the volatility of the nominal excess return on stocks,"
157, Federal Reserve Bank of Minneapolis.
- Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
- Clark, Todd E. & West, Kenneth D., 2007.
"Approximately normal tests for equal predictive accuracy in nested models,"
Journal of Econometrics,
Elsevier, vol. 138(1), pages 291-311, May.
- Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
- 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.
- Li, C W & Li, W K, 1996. "On a Double-Threshold Autoregressive Heteroscedastic Time Series Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(3), pages 253-74, May-June.
- Christian Conrad & Berthold R. Haag, 2006. "Inequality Constraints in the Fractionally Integrated GARCH Model," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(3), pages 413-449.
- Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
- Bollerslev, Tim & Ole Mikkelsen, Hans, 1996.
"Modeling and pricing long memory in stock market volatility,"
Journal of Econometrics,
Elsevier, vol. 73(1), pages 151-184, July.
- Tom Doan, . "RATS program to replicate Bollerslev-Mikkelson(1996) FIEGARCH models," Statistical Software Components RTZ00173, Boston College Department of Economics.
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