Forecasting the High-Frequency Exchange Rate Volatility with Smooth Transition Exponential Smoothing
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Abstract
Smooth Transition Exponential Smoothing (STES) is a popular exponential smoothing method for volatility forecasting; whereby the success of the STES model lies in the choice of the transition variable. In this paper, three realized variance (RV), daily, weekly and monthly RV were used as the transition variables in STES methods to evaluate the performance of intraday data. While daily squared return is a noisy series, squared residual and daily RV were employed as the proxy for actual volatilities in this study. With five series of exchange rates, a comparative analysis was conducted for Ad Hoc methods, Generalised Autoregressive Conditional Heteroscedastic (GARCH) models, and STES methods using various RV combinations. The empirical results showed that when daily RV was used as proxy for actual volatility, the traditional STES models and STES models with RV as the transition variables outperformed Ad Hoc methods and GARCH models under the RMSE evaluation criteria. Similar promising results were also observed for traditional STES models and STES models with RV as the transition variables under MAE evaluation. The MCS results generally reaffirmed the results from both the MAE and RMSE evaluation criteria.
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References
Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review, 39, 885–905.
Andersen, T. G., Bollerslev, T., Christoffersen, P. F., & Diebold, F. X. (2006). Volatility and correlation forecasting. Handbook of Economic Forecasting, 1, 777–878.
Bahmani-Oskooee, M., & Gelan, A. (2018). Exchange-rate volatility and international trade performance: Evidence from 12 African countries. Economic Analysis and Policy, 58, 14–21. https://doi.org/10.1016/j.eap.2017.12.005
Benavides, G., & Capistran, C. (2012). Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts. Journal of Empirical Finance, 19, 627–639.
Blair, B. J., Poon, S. H., & Taylor, S. J. (2001). Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high frequency index returns. Journal of Econometrics, 105, 5–26.
Black, F. (1976). Studies of stock market volatility changes. Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economic Statistics Section, 177–181. https://doi.org/10.1016/0304-405X(76)90024-6
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Bollerslev, T., Litvinova, J., & Tauchen, G. (2006). Leverage and volatility feedback effects in high-frequency data. Journal of Financial Econometrics, 4(3), 353–384.
Chan, T. H., Lye, C. T., & Hooy, C. W. (2013). Forecasting Malaysian Ringgit: before and after the global crisis. Asian Academy of Management Journal of Accounting and Finance, 9(2), 157–175.
Day. T. E., & Lewis, C. M. (1992). Stock market volatility and the information content of stock index options. Journal of Econometrics, 52(1- 2), 267–287.
Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. International Journal of Forecasting, 13(3), 253–264. https://doi.org/10.1016/0169-2070(88)90124-0
Ekaputra, I. A. (2014). Impact of foreign and domestic order imbalances on return and volatility-volume relation. Asian Academy of Management Journal of Accounting and Finance, 10(1), 1–19.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity, with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773.
Fama, E. F. (1965). The behaviour of stock market prices. The Journal of Business, 38(1), 34–105.
Gooi, L. M., Choo, W. C., Md. Nassir, & Ng, S. I. (2018). Volatility forecasting of real estate stock in Malaysia with Smooth Transition Exponential Smoothing. International Journal of Economics and Management, 12(S2): 731–745.
Glosten, L. R., Jagannathan, R., & Runkle, D. E., (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
Habibi, A., & Lee, C. (2019). Asymmetric effects of exchange rates on stock prices in G7 countries. Capital Markets Review, 27(1), 19–33.
Hagerud, G.E. (1997). A new non-linear GARCH model. Unpublished doctoral dissertation, IFE, Stockhold School of Economics.
Hansen, P. R., Lund, A., & Nason, J. M. (2003). Choosing the best volatility models: The model confidence set approach. Oxford Bulletin of Economics and Statistics, 65(s1), 839–861.
Hansen, P. R., & Lund, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20(7), 873–889. https://doi.org/10.1002/jae.800.
Hansen, P. R., Lund, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497. https://doi.org/10.3982/ecta5771
Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4
Julianto, L., & Ekaputra, A. (2019). Max-effect in the Indonesian market. Capital Markets Review, 28(2), 19–27.
Kinyo, T. (2020). Volatility interdependence on foreign exchange markets: The contribution of cross-rates. North American Journal of Economics and Finance, 54, E03141.
Koopman, S. J., Jungbacker, B., & Hol, E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurements. Journal of Empirical Finance, 12(3), 445–475.
Lock, B. Q., Chu, E. Y., Song, S. I., & Lee, L. Y. (2019). Exchange rate movements, earnings management and stock returns in Malaysia. Capital Markets Review, 27(1), 53–68.
Liu, L. Y., Patton, A. J. & Sheppard, K. (2015). Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. Journal of Econometrics, 187, 293–311.
Liu, M., Taylor, J. W., & Choo, W. C. (2020). Further empirical evidence on the forecasting of volatility with smooth transition exponential smoothing. Economic Modelling, 20, 651–659. https://doi.org/10.1016/j.econmod.2020.02.021.
Martens, M. (2001). Forecasting daily exchange rate volatility using intraday returns. Journal of International Money and Finance, 20, 1–23.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260
Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics, 160(1), 246 –256.
Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41, 478–539.
RiskMetrics. (1996). RiskMetricsTM technical document (4th ed.). J.P. Morgan/Reuters.
Sugiharti, L., Esquivias, M. A., & Setyorani, B. (2020). The impact of exchange rate volatility on Indonesia’s top exports to the five main export markets. Heliyon, 6(1), e03141. https://doi.org/10.1016/j.heliyon.2019.e03141
Sulliman, Z. S.A. (2012). Modelling exchange rate volatility using GARCH models: Empirical evidence from Arab countries. International Journal of Economics and Finance, 4(3), 216–229.
Taylor, J. W. (2004a). Volatility forecasting with smooth transition exponential smoothing. International Journal of Forecast, 20, 273–286. https://doi.org/10.1016/j.ijforecast.2003.09.010
Taylor, J. W. (2004b). Smooth transition exponential smoothing. Journal of Forecast, 23, 385–394.
Taylor, J. W. (2005). Generating volatility forecasts from value at risk estimates. Management Science, 51(5), 679–849.
Ung, S. N., Choo, W. C., Sambasivan, M., & Nassir, A. M., (2014). The persistency of international diversification benefits: the role of the asymmetry volatility model. Asian Academy of Management Journal of Accounting and Finance, 10(1), 151–165.
Wan, C. K., Choo, W. C., Annuar, M. N., Muzafar, S. H., & Zulkornain, Y. (2021). Volatility forecasting performance of Smooth Transition Exponential Smoothing method: Evidence from mutual fund indices in Malaysia. Asian Economic and Financial Review, 11(10), 829–859.
Zhu, X., Zhang, H., & Zhong, M. (2017). Volatility forecasting using high frequency data: The role of after-hours information and leverage effects. Resources Policy, 54(August), 58–70. https://doi.org/10.1016/j.resourpol.2017.09.006