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Year 2025, Volume: 54 Issue: 1, 56 - 86, 15.05.2025
https://doi.org/10.26650/ibr.2025.54.1577152

Abstract

References

  • Amisano, G., & Giacomini, R. (2007). Comparing density forecasts via weighted likelihood ratio tests. Journal of Business & EconomicStatistics, 25(2), 177-190. https://doi.org/l0.1198/073500106000000332 google scholar
  • Andrews, D. W. K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59(3), 817-858. google scholar
  • Andrews, D. W. K., & Monahan, J. C. (1992). An improved heteroskedasticity and autocorrelation consistent covariance matrix estimator. Econometrica, 60(4), 953-966. google scholar
  • Ardia, D., Boudt, K., & Catania, L. (2019). Generalized autoregressive score models in R: The GAS package. Journal of Statistical Software, 88(6). https://doi.org/10.18637/jss.v088.i06 google scholar
  • Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203-228. google scholar
  • Bekar, E. (2019). Les Previsions Statiques et Dynamiques des Valeurs a Risque (VaR) des Actions de Banque: Le Modele de Depassements de Seuil (POT) et Les Modeles de Score Autoregressifs Generalises (GAS). The Journal of Academic Social Science, (97), 142-169. https://doi.org/10.29228/ASOS.36752 google scholar
  • Bekar, E. (2023). Döviz kuru volatilite modellemesinde Beta-t-EGARCH modelleri: Amerikan doları / Türk lirası döviz kuru üzerinden bir değerlendirme. Sosyoekonomi, 31(55), 371-395. https://doi.org/10.17233/sosyoekonomi.2023.01.19 google scholar
  • Bernardi, M., & Catania, L. (2016). Comparison of Value-at-Risk models using the MCS approach. Computational Statistics, 31(2), 579-608. https://doi.org/10.1007/s00180-016-0646-6 google scholar
  • Blasques, F., Hoogerkamp, M. H., Koopman, S. J., & van de Werve, I. (2021). Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data. International Journal of Forecasting, 37(4), 1426-1441. https://doi.org/10.1016/j. ijforecast.2021.01.026 google scholar
  • Blazsek, S., Escribano, A., & Licht, Adrian. (2021). Identification of seasonal effects in impulse responses using score-driven multivariate location models. Journal of Econometric Methods, 10(1), 53-66. google scholar
  • Bloomberg HT. (2013, June 3). BIST 100’den büyük düşüş. Retrieved October 27, 2024, from https://www.bloomberght.com/haberler/ haber/1368565-bist-100den-buyuk-dusus google scholar
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/ 10.1016/0304-4076(86)90063-1 google scholar
  • Brooks, C. (2019). Introductory econometrics for finance (4th ed.). Cambridge: Cambridge University Press. google scholar
  • Caner, M., & Kilian, L. (2001). Size distortions of tests of the null hypothesis of stationarity: evidence and implications for the PPP debate. google scholar
  • Journal of International Money and Finance, 20(5), 639-657. https://doi.org/10.1016/S0261-5606(01)00011-0 google scholar
  • Catania, L., & Nonejad, N. (2020). Density forecasts and the leverage effect: Evidence from observation and parameter-driven volatility models. European Journal of Finance, 26(2-3), 100-118. https://doi.org/10.1080/1351847X.2019.1586744 google scholar
  • Chevillon, G. (2007). Direct multi-step estimation and forecasting. Journal of Economic Surveys, 21(4), 746-785. https://doi.org/10.1111/j. 1467-6419.2007.00518.x google scholar
  • Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862. google scholar
  • Christoffersen, P. F., & Pelletier, D. (2004). Backtesting Value-at-Risk: A duration-based approach. Journal of Financial Econometrics, 2(1), 84-108. https://doi.org/10.1093/jjfinec/nbh004 google scholar
  • Creal, D., Koopman, S. J., & Lucas, A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28(5), 777-795. https://doi.org/l0.1002/jae.1279 google scholar
  • Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13 (3), 134-144. https:// doi.org/10.1198/073500102753410444 google scholar
  • Dowd, K. (2005). Measuring market risk (2nd ed.). Hoboken, NJ: John Wiley & Sons Ltd. google scholar
  • Elliott, G., & Timmermann, A. (2016). Economic Forecasting. New Jersey: Princeton University Press. google scholar
  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econo-metrica, 50(4), 987-1007. https://doi.org/10.2307/1912773 google scholar
  • Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business and Economic Statistics, 22(4), 367-381. https://doi.org/10.1198/073500104000000370 google scholar
  • Fernândez, C., & Steel, M. F. J. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371. https://doi.org/10.1080/01621459.1998.10474117 google scholar
  • Fissler, T., & Ziegel, J. F. (2016). Higher order elicitability and Osband’s principle. The Annals of Statistics, 44(4), 1680-1707. https://doi. org/10.1214/16-AOS1439 google scholar
  • Francq, C., & Zakoian, J.-M. (2019). GARCH models: Structure, statistical inference, and financial applications (2nd ed.). Hoboken, NJ: John Wiley & Sons Ltd. google scholar
  • Galanos, A. (2023). rugarch: Univariate GARCH models. R package version 1.5-1. google scholar
  • GAS papers. (n.d.). Retrieved January 28, 2025, from https://www.gasmodel.com/gaspapers.htm google scholar
  • Gedik Yatırım Menkul Değerler A.Ş. (2021). Günlük Bülten. Retrieved from https://gedik-cdn.foreks.com/yatirim/reports/files/000/005/ 912/original/Gunluk_Bulten_22.03.2021.pdf?1616392483 google scholar
  • Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Associ-ation, 102(477), 359-378. https://doi.org/10.1198/016214506000001437 google scholar
  • Gneiting, T., & Ranjan, R. (2011). Comparing density forecasts using threshold and quantile-weighted scoring rules. Journal of Business and Economic Statistics, 29(3), 411-422. https://doi.org/10.1198/jbes.2010.08110 google scholar
  • Gonzâlez-Rivera, G., Lee, T. H., & Mishra, S. (2004). Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood. International Journal of Forecasting, 20(4), 629-645. https://doi.org/10.1016/j.ijforecast.2003. 10.003 google scholar
  • Hadi, D. M., Karim, S., Naeem, M. A., & Lucey, B. M. (2023). Turkish Lira crisis and its impact on sector returns. Finance Research Letters, 52. https://doi.org/10.1016/j.frl.2022.103479 google scholar
  • Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The Model Confidence Set. Econometrica, 79(2), 453-497. https://doi.org/10.3982/ECTA5771 google scholar
  • Harvey, A., Hurn, S., Palumbo, D., & Thiele, S. (2024). Modelling circular time series. Journal of Econometrics, 239(1). https://doi.org/10. 1016/j.jeconom.2023.02.016 google scholar
  • 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 google scholar
  • Hürriyet Newspaper. (2001, February 22). Kara Çarşamba. Retrieved July 13, 2024, from https://bigpara.hurriyet.com.tr/haberler/ ekonomi-haberleri/kara-carsamba_ID358956/ google scholar
  • Jorion, P. (2007). Value at risk: The new benchmark for managing financial risk (3rd ed.). New York: McGraw-Hill. google scholar
  • Kahyaoğlu Bozkuş, S. (2019). Generalized autoregressive score (Gas) model: An applied analysis on the BIST 100 index. In Ç. Başarır, B. Darıcı, & H. M. Ertuğrul (Eds.), New trends in banking and finance. Berlin: Peter Lang GmbH. google scholar
  • Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73-84. https:// doi.org/10.3905/jod.1995.407942 google scholar
  • Laporta, A. G., Merlo, L., & Petrella, L. (2018). Selection of Value at Risk Models for Energy Commodities. Energy Economics, 74, 628-643. https://doi.org/10.1016/j.eneco.2018.07.009 google scholar
  • Marcellino, M., Stock, J. H., & Watson, M. W. (2006). A comparison of direct and iterated multistep AR methods for forecasting macro-economic time series. Journal of Econometrics, 135(1-2), 499-526. https://doi.org/10.1016/j.jeconom.2005.07.020 google scholar
  • Owusu Junior, P., Tiwari, A. K., Tweneboah, G., & Asafo-Adjei, E. (2022). GAS and GARCH based value-at-risk modeling of precious metals. Resources Policy, 75. https://doi.org/10.1016/j.resourpol.2021.102456 google scholar
  • Pascual, L., Romo, J., & Ruiz, E. (2006). Bootstrap prediction for returns and volatilities in GARCH models. Computational Statistics and Data Analysis, 50(9), 2293-2312. https://doi.org/10.1016/j.csda.2004.12.008 google scholar
  • R Core Team. (2024). R: A Language and Environment for Statistical Computing. Vienna, Austria. Retrieved from https://www. R-project.org/ google scholar
  • Sucarrat, G. (2013). Betategarch: Simulation, estimation and forecasting of Beta-Skew-t-EGARCH models. R Journal, 5 (2), 137-147. https:// doi.org/10.32614/rj-2013-034 google scholar
  • Trucîos, C., & Taylor, J. W. (2023). A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies. Journal of Forecasting, 42(4), 989-1007. https://doi.org/10.1002/for.2929 google scholar
  • Xu, Y., & Lien, D. (2022). Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models. Journal of Forecasting, 41(2), 259-278. https://doi.org/10.1002/for.2812 google scholar

GAS or GARCH: A comparison of density and VaR forecasts in Turkish FX and stock markets

Year 2025, Volume: 54 Issue: 1, 56 - 86, 15.05.2025
https://doi.org/10.26650/ibr.2025.54.1577152

Abstract

This paper compares the renowned GARCH model with a novel one, the Generalized Autoregressive Score (GAS) model in terms of forecasting performance. Considering the gap in the literature, this study focuses on the Turkish stock and FX markets. The analysis covers 25 years (1999-2023), of which the last 12 constitute the out-of-sample period. The selected indexes largely represent the finance (XBANK) and industry (XUSIN) sectors and the entire (XUTUM) economy, while the fourth (XU100) is the market benchmark. Likewise, FX rates are the leading factors that dominate Turkish foreign trade. Rolling density forecasts from the standard versions of the models are compared via Diebold-Mariano (DM) test with the two popular scoring rules. The GARCH model generally outperforms GAS when the conditional distribution is the Normal or its skewed version. We find some evidence for the reverse with Student-t and skewed version, but this lacks statistical support, except for the definite superiority of GAS in USD returns coupled with skewed Student-t. A deeper analysis attributed GAS’s underperformance to its treatment of shocks that are more likely to occur in developing markets. We also report similar findings with DM tests using two loss functions for VaR forecasts, whereas the results of the backtesting procedures are inconsistent across risk levels.

References

  • Amisano, G., & Giacomini, R. (2007). Comparing density forecasts via weighted likelihood ratio tests. Journal of Business & EconomicStatistics, 25(2), 177-190. https://doi.org/l0.1198/073500106000000332 google scholar
  • Andrews, D. W. K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59(3), 817-858. google scholar
  • Andrews, D. W. K., & Monahan, J. C. (1992). An improved heteroskedasticity and autocorrelation consistent covariance matrix estimator. Econometrica, 60(4), 953-966. google scholar
  • Ardia, D., Boudt, K., & Catania, L. (2019). Generalized autoregressive score models in R: The GAS package. Journal of Statistical Software, 88(6). https://doi.org/10.18637/jss.v088.i06 google scholar
  • Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203-228. google scholar
  • Bekar, E. (2019). Les Previsions Statiques et Dynamiques des Valeurs a Risque (VaR) des Actions de Banque: Le Modele de Depassements de Seuil (POT) et Les Modeles de Score Autoregressifs Generalises (GAS). The Journal of Academic Social Science, (97), 142-169. https://doi.org/10.29228/ASOS.36752 google scholar
  • Bekar, E. (2023). Döviz kuru volatilite modellemesinde Beta-t-EGARCH modelleri: Amerikan doları / Türk lirası döviz kuru üzerinden bir değerlendirme. Sosyoekonomi, 31(55), 371-395. https://doi.org/10.17233/sosyoekonomi.2023.01.19 google scholar
  • Bernardi, M., & Catania, L. (2016). Comparison of Value-at-Risk models using the MCS approach. Computational Statistics, 31(2), 579-608. https://doi.org/10.1007/s00180-016-0646-6 google scholar
  • Blasques, F., Hoogerkamp, M. H., Koopman, S. J., & van de Werve, I. (2021). Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data. International Journal of Forecasting, 37(4), 1426-1441. https://doi.org/10.1016/j. ijforecast.2021.01.026 google scholar
  • Blazsek, S., Escribano, A., & Licht, Adrian. (2021). Identification of seasonal effects in impulse responses using score-driven multivariate location models. Journal of Econometric Methods, 10(1), 53-66. google scholar
  • Bloomberg HT. (2013, June 3). BIST 100’den büyük düşüş. Retrieved October 27, 2024, from https://www.bloomberght.com/haberler/ haber/1368565-bist-100den-buyuk-dusus google scholar
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/ 10.1016/0304-4076(86)90063-1 google scholar
  • Brooks, C. (2019). Introductory econometrics for finance (4th ed.). Cambridge: Cambridge University Press. google scholar
  • Caner, M., & Kilian, L. (2001). Size distortions of tests of the null hypothesis of stationarity: evidence and implications for the PPP debate. google scholar
  • Journal of International Money and Finance, 20(5), 639-657. https://doi.org/10.1016/S0261-5606(01)00011-0 google scholar
  • Catania, L., & Nonejad, N. (2020). Density forecasts and the leverage effect: Evidence from observation and parameter-driven volatility models. European Journal of Finance, 26(2-3), 100-118. https://doi.org/10.1080/1351847X.2019.1586744 google scholar
  • Chevillon, G. (2007). Direct multi-step estimation and forecasting. Journal of Economic Surveys, 21(4), 746-785. https://doi.org/10.1111/j. 1467-6419.2007.00518.x google scholar
  • Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862. google scholar
  • Christoffersen, P. F., & Pelletier, D. (2004). Backtesting Value-at-Risk: A duration-based approach. Journal of Financial Econometrics, 2(1), 84-108. https://doi.org/10.1093/jjfinec/nbh004 google scholar
  • Creal, D., Koopman, S. J., & Lucas, A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28(5), 777-795. https://doi.org/l0.1002/jae.1279 google scholar
  • Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13 (3), 134-144. https:// doi.org/10.1198/073500102753410444 google scholar
  • Dowd, K. (2005). Measuring market risk (2nd ed.). Hoboken, NJ: John Wiley & Sons Ltd. google scholar
  • Elliott, G., & Timmermann, A. (2016). Economic Forecasting. New Jersey: Princeton University Press. google scholar
  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econo-metrica, 50(4), 987-1007. https://doi.org/10.2307/1912773 google scholar
  • Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business and Economic Statistics, 22(4), 367-381. https://doi.org/10.1198/073500104000000370 google scholar
  • Fernândez, C., & Steel, M. F. J. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371. https://doi.org/10.1080/01621459.1998.10474117 google scholar
  • Fissler, T., & Ziegel, J. F. (2016). Higher order elicitability and Osband’s principle. The Annals of Statistics, 44(4), 1680-1707. https://doi. org/10.1214/16-AOS1439 google scholar
  • Francq, C., & Zakoian, J.-M. (2019). GARCH models: Structure, statistical inference, and financial applications (2nd ed.). Hoboken, NJ: John Wiley & Sons Ltd. google scholar
  • Galanos, A. (2023). rugarch: Univariate GARCH models. R package version 1.5-1. google scholar
  • GAS papers. (n.d.). Retrieved January 28, 2025, from https://www.gasmodel.com/gaspapers.htm google scholar
  • Gedik Yatırım Menkul Değerler A.Ş. (2021). Günlük Bülten. Retrieved from https://gedik-cdn.foreks.com/yatirim/reports/files/000/005/ 912/original/Gunluk_Bulten_22.03.2021.pdf?1616392483 google scholar
  • Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Associ-ation, 102(477), 359-378. https://doi.org/10.1198/016214506000001437 google scholar
  • Gneiting, T., & Ranjan, R. (2011). Comparing density forecasts using threshold and quantile-weighted scoring rules. Journal of Business and Economic Statistics, 29(3), 411-422. https://doi.org/10.1198/jbes.2010.08110 google scholar
  • Gonzâlez-Rivera, G., Lee, T. H., & Mishra, S. (2004). Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood. International Journal of Forecasting, 20(4), 629-645. https://doi.org/10.1016/j.ijforecast.2003. 10.003 google scholar
  • Hadi, D. M., Karim, S., Naeem, M. A., & Lucey, B. M. (2023). Turkish Lira crisis and its impact on sector returns. Finance Research Letters, 52. https://doi.org/10.1016/j.frl.2022.103479 google scholar
  • Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The Model Confidence Set. Econometrica, 79(2), 453-497. https://doi.org/10.3982/ECTA5771 google scholar
  • Harvey, A., Hurn, S., Palumbo, D., & Thiele, S. (2024). Modelling circular time series. Journal of Econometrics, 239(1). https://doi.org/10. 1016/j.jeconom.2023.02.016 google scholar
  • 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 google scholar
  • Hürriyet Newspaper. (2001, February 22). Kara Çarşamba. Retrieved July 13, 2024, from https://bigpara.hurriyet.com.tr/haberler/ ekonomi-haberleri/kara-carsamba_ID358956/ google scholar
  • Jorion, P. (2007). Value at risk: The new benchmark for managing financial risk (3rd ed.). New York: McGraw-Hill. google scholar
  • Kahyaoğlu Bozkuş, S. (2019). Generalized autoregressive score (Gas) model: An applied analysis on the BIST 100 index. In Ç. Başarır, B. Darıcı, & H. M. Ertuğrul (Eds.), New trends in banking and finance. Berlin: Peter Lang GmbH. google scholar
  • Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73-84. https:// doi.org/10.3905/jod.1995.407942 google scholar
  • Laporta, A. G., Merlo, L., & Petrella, L. (2018). Selection of Value at Risk Models for Energy Commodities. Energy Economics, 74, 628-643. https://doi.org/10.1016/j.eneco.2018.07.009 google scholar
  • Marcellino, M., Stock, J. H., & Watson, M. W. (2006). A comparison of direct and iterated multistep AR methods for forecasting macro-economic time series. Journal of Econometrics, 135(1-2), 499-526. https://doi.org/10.1016/j.jeconom.2005.07.020 google scholar
  • Owusu Junior, P., Tiwari, A. K., Tweneboah, G., & Asafo-Adjei, E. (2022). GAS and GARCH based value-at-risk modeling of precious metals. Resources Policy, 75. https://doi.org/10.1016/j.resourpol.2021.102456 google scholar
  • Pascual, L., Romo, J., & Ruiz, E. (2006). Bootstrap prediction for returns and volatilities in GARCH models. Computational Statistics and Data Analysis, 50(9), 2293-2312. https://doi.org/10.1016/j.csda.2004.12.008 google scholar
  • R Core Team. (2024). R: A Language and Environment for Statistical Computing. Vienna, Austria. Retrieved from https://www. R-project.org/ google scholar
  • Sucarrat, G. (2013). Betategarch: Simulation, estimation and forecasting of Beta-Skew-t-EGARCH models. R Journal, 5 (2), 137-147. https:// doi.org/10.32614/rj-2013-034 google scholar
  • Trucîos, C., & Taylor, J. W. (2023). A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies. Journal of Forecasting, 42(4), 989-1007. https://doi.org/10.1002/for.2929 google scholar
  • Xu, Y., & Lien, D. (2022). Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models. Journal of Forecasting, 41(2), 259-278. https://doi.org/10.1002/for.2812 google scholar
There are 50 citations in total.

Details

Primary Language English
Subjects Financial Econometrics, Financial Forecast and Modelling
Journal Section Articles
Authors

Ali Ulvi Özgül 0000-0002-1082-2652

Publication Date May 15, 2025
Submission Date October 31, 2024
Acceptance Date March 7, 2025
Published in Issue Year 2025 Volume: 54 Issue: 1

Cite

APA Özgül, A. U. (2025). GAS or GARCH: A comparison of density and VaR forecasts in Turkish FX and stock markets. Istanbul Business Research, 54(1), 56-86. https://doi.org/10.26650/ibr.2025.54.1577152

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