Araştırma Makalesi
BibTex RIS Kaynak Göster

Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme

Yıl 2025, Cilt: 7 Sayı: Özel Sayı, 84 - 98, 17.12.2025
https://doi.org/10.46464/tdad.1762942

Öz

Bu çalışma, 2014M01–2025M06 dönemine ait Türkiye Zorunlu Deprem Sigortası prim üretimi verilerini kullanarak, 6 Şubat 2023 Kahramanmaraş depremleri sonrasında ortaya çıkan volatilite dinamiklerini koşullu varyans modelleri aracılığıyla incelemektedir. ARCH, GARCH, EGARCH, TARCH ve CGARCH modelleri karşılaştırmalı olarak uygulanmış; şokların ani ve kalıcı bileşenlerini ayrıştırmak amacıyla kukla değişkenler kullanılmıştır. Elde edilen bulgular, CGARCH modelinin kısa ve uzun vadeli oynaklık yapısını açıklamada göreli olarak daha dengeli sonuçlar sunduğunu göstermektedir. Bulgular, büyük ölçekli depremlerin sigorta prim dinamikleri üzerindeki kalıcı etkilerine işaret etmekte ve afet sonrası finansal oynaklığın modellenmesine yönelik metodolojik katkılar sağlamaktadır.

Kaynakça

  • Aftab H., Beg R.A., Sun S., Zhou Z., 2019. Testing and Predicting Volatility Spillover-A Multivariate, GJR-GARCH Approach, Theoretical Economics Letters, 9, 83-99.
  • Banafea W.A., 2014. Structural breaks and causality relationship between economic growth and energy consumption in Saudi Arabia, International Journal of Energy Economics and Policy, 4(4), 726-734.
  • Bjørnøy E., 2020. Markov-switching GARCH models with application to insurance data (Yüksek lisans tezi), University of Bergen.
  • Bollerslev T., 1986. Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31(3), 307-327.
  • Bouzouita R., Craioveanu M., 2019. Dynamic conditional correlations between the insurance sectors and the overall market: Evidence from the 2008 financial crisis, Research in Business and Economics Journal, 16, 1-14.
  • Box G.E.P., Jenkins G.M., Reinsel G.C., Ljung G.M., 2015. Time Series Analysis: Forecasting and Control. 5th ed. Hoboken, NJ: Wiley.
  • Breusch T.S., Pagan A.R., 1979. A Simple Test for Heteroscedasticity and Random Coefficient Variation, Econometrica, 47, 1287-1294.
  • Brewer E., Carson J.M., Elyasiani E., Mansur I., Scott W.L., 2007. Interest rate risk and equity values of life insurance companies: A GARCH–M model, Journal of Risk and Insurance, 74(2), 401-423.
  • Brooks C., 2019. Introductory econometrics for finance, Cambridge University Press, Cambridge.
  • Budiandru B., 2021. Dynamic volatility modeling of Indonesian insurance company stocks, Jurnal Ekonomi dan Studi Pembangunan, 14(1), 1-13.
  • Chaiyawat T., Guayjarernpanishk P., 2024. Effective forecasting of insurer capital requirements: ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH approaches, Emerging Science Journal, 8(6), 2173-2196.
  • Chalissery N., Anagreh S., Nishad T.M., Tabash M.I., 2022. Mapping the trend, application and forecasting performance of asymmetric garch models: A review based on bibliometric analysis, Journal of Risk and Financial Management, 15(9), 406.
  • Cheng J., Elyasiani E., Lin T.-T., 2010. Market reaction to regulatory action in the insurance industry: The case of contingent commission, Journal of Risk and Insurance, 77(2), 347-368.
  • Değirmenci N., Akay A., 2017. Finansal verilerin ARIMA ve ARCH modelleriyle öngörüsü: Türkiye örneği, Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 12(3), 15-36.
  • Dickey D.A., Fuller W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74(366), 427-431.
  • Elyasiani E., Staikouras S.K., Dontis-Charitos P., 2012. Cross-industry product diversification and contagion in risk and return: The case of bancassurance and ınsurance‐bank takeovers, The Journal of Risk and Insurance, 833), 681-718.
  • Enders W., 2014. Applied econometric time series (4th ed.), Wiley.
  • Engle R.F., 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50(4), 987-1007.
  • Engle R.F., Lee G., 1999. A long-run and short-run component model of stock return volatility (In: Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W.J. Granger, Oxford University Press, Oxford, Editors: Engle, R.F. and White, H.), 475-497.
  • Ertek Z., 2023. Zorunlu Deprem Sigortası/Dask Poliçesi, Legal Blok, Erişim adresi: https://legal.com.tr/blog/genel/zorunlu-deprem-sigortasi-dask-policesi/.
  • Fırat M., 2022. Deprem ve toplumsal etkileri, Tezkire, 80, 47-72.
  • Ghalanos A., 2013. Introduction to the rugarch package (Version 1.0-14).
  • Goldman E., Shen X., 2018. Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement, Bank of Canada Staff Working Paper, No. 2018-21.
  • Hacker R.S., Hatemi-J A., 2021. Model selection in time series analysis: Using information criteria as an alternative to hypothesis testing, Journal of Economic Studies, 49(6), 1055-1075.
  • Hamilton J.D., 1994. Time Series Analysis. Princeton, NJ: Princeton University Press.
  • Hansen B.E., 1992. Testing for parameter instability in linear models, Journal of Policy Modeling, 14(4), 517-533.
  • Hyndman R.J., 2014. Plotting the characteristic roots for ARIMA models, Hyndsight blog, Erişim adresi: https://robjhyndman.com/hyndsight/arma-roots/.
  • Jia Z., Jin Z., Marchandon M., Ulrich T., Gabriel A.-A., Fan W., Shearer P., Zou X., Rekoske J., Bulut F., Garagon A., Fialko Y., 2023. The complex dynamics of the 2023 Kahramanmaraş, Turkey, Mw 7.8-7.7 earthquake doublet, Science, 381(6661), 985-990.
  • Kingsley A., Uche P.I., 2019. Volatility modelling using ARCH and GARCH models: A case study of the Nigerian Stock Exchange, International Journal of Mathematics Trends and Technology, 65(4), 58-63.
  • Kurt F.E., Senal S., 2018. Sigorta Sektörü Hisse Senedi Piyasasında Volatilite Modellemesi: Arch-M Yöntemi ile Borsa İstanbul’da Bir Uygulama, Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(32), 314-332.
  • Lee S., 2025a. Mastering the Jarque-Bera Test: A Guide to Data Normality, Number Analytics, Erişim adresi: https://www.numberanalytics.com/blog/mastering-jarque-bera-test-data-normality-guide.
  • Lee S., 2025b. Ultimate Guide to ARCH models in time series, Number Analytics, Erişim adresi: https://www.numberanalytics.com/blog/ultimate-guide-arch-models.
  • Liu H., Li R., Yuan J., 2018. Deposit insurance pricing under GARCH. Finance Research Letters, 26, 242-249.
  • Liu T., Shi Y., 2022. Innovation of the Component GARCH Model: Simulation Evidence and Application on the Chinese Stock Market, Mathematics, 10(11), 1903.
  • MacKinnon J.G., 1996. Numerical distribution functions for unit root and cointegration tests, Journal of Applied Econometrics, 11(6), 601.618.
  • Nelson D.B., 1991. Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59(2), 347-370.
  • Palm F.C., 1996. 7 GARCH models of volatility (In: Handbook of Statistics, Editors: G.S. Maddala & C.R. Rao), 14, 209-240.
  • Peovski V., 2024. Comparative analysis of forecasting models in the nonlife insurance: Insights from the SARIMA and ETS approaches, Risk Management and Insurance Review, 27(4), 507-549.
  • Phillips P.C.B., Perron P., 1988. Testing for a unit root in time series regression, Biometrika, 75(2), 335-346.
  • Sezgin Alp Ö., Kırkbeşoğlu E., 2015. Sigorta endeksi getirisinin doğrusal olmayan yapısı, Finansal Araştırmalar Ve Çalışmalar Dergisi, 7(13), 245-260.
  • Susanti D., Maraya N.S., Sukono Saputra J., 2024. Prediction of motor vehicle insurance claims using ARIMA-GARCH models, Operations Research: International Conference Series, 5(3), 86-92.
  • Timur S., 2025. Doğal afetlerin sosyal yaşama, bütçeye ve ekonomiye etkileri, International Journal of Social and Humanities Sciences Research, 12(118), 650-662.
  • Tseng J.-J., Li S.-P., 2011. Asset returns and volatility clustering in financial time series, Physica A: Statistical Mechanics and its Applications, 390(7), 1300-1314.
  • Uğurlu E., 2019. Research data analysis using eviews:an empirical example of modeling volatility (In: Research Data Access and Management in Modern Libraries, Editors: R.K. Bhardwaj and P. Banks), 292- 324, USA: IGI.
  • Waheed M., Alam T., Ghauri S.P., 2006. Structural breaks and unit root: Evidence from Pakistani macroeconomic time series, (MPRA Paper No. 1797), University Library of Munich, Germany.
  • Wanat S., Denkowska A., 2018. Dependencies and systemic risk in the European insurance sector: New evidence based on copula-DCC-GARCH model and selected clustering methods, Entrepreneurial Business and Economics Review, 4, 7-27.
  • White H., 1980. A Heteroscedasticity Consistent Covariance Matrix and Direct Test for Heteroscedasticity, Econometrica, 48, 817-838.
  • Zivot E., Andrews D.W.K., 1992. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis, Journal of Business & Economic Statistics, 10(3), 251-270.
  • Zakoian J., 1994. Threshold Heteroskedastic Models, Journal of Economic Dynamics and Control, 18, 931-955.

An Empirical Analysis of the Volatility Dynamics of Compulsory Earthquake Insurance Premiums in Türkiye

Yıl 2025, Cilt: 7 Sayı: Özel Sayı, 84 - 98, 17.12.2025
https://doi.org/10.46464/tdad.1762942

Öz

This study examines the volatility dynamics emerging after the February 6, 2023 Kahramanmaraş earthquakes using conditional variance models based on the compulsory earthquake insurance premium data for Türkiye covering the period 2014M01–2025M06. ARCH, GARCH, EGARCH, TARCH, and CGARCH models are applied comparatively, employing dummy variables to distinguish the immediate and persistent components of shocks. The findings reveal that the CGARCH model provides relatively more balanced results in explaining short- and long-term volatility structures. The results highlight the lasting impacts of large-scale earthquakes on insurance premium dynamics and offer methodological contributions to the modeling of post-disaster financial volatility.

Kaynakça

  • Aftab H., Beg R.A., Sun S., Zhou Z., 2019. Testing and Predicting Volatility Spillover-A Multivariate, GJR-GARCH Approach, Theoretical Economics Letters, 9, 83-99.
  • Banafea W.A., 2014. Structural breaks and causality relationship between economic growth and energy consumption in Saudi Arabia, International Journal of Energy Economics and Policy, 4(4), 726-734.
  • Bjørnøy E., 2020. Markov-switching GARCH models with application to insurance data (Yüksek lisans tezi), University of Bergen.
  • Bollerslev T., 1986. Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31(3), 307-327.
  • Bouzouita R., Craioveanu M., 2019. Dynamic conditional correlations between the insurance sectors and the overall market: Evidence from the 2008 financial crisis, Research in Business and Economics Journal, 16, 1-14.
  • Box G.E.P., Jenkins G.M., Reinsel G.C., Ljung G.M., 2015. Time Series Analysis: Forecasting and Control. 5th ed. Hoboken, NJ: Wiley.
  • Breusch T.S., Pagan A.R., 1979. A Simple Test for Heteroscedasticity and Random Coefficient Variation, Econometrica, 47, 1287-1294.
  • Brewer E., Carson J.M., Elyasiani E., Mansur I., Scott W.L., 2007. Interest rate risk and equity values of life insurance companies: A GARCH–M model, Journal of Risk and Insurance, 74(2), 401-423.
  • Brooks C., 2019. Introductory econometrics for finance, Cambridge University Press, Cambridge.
  • Budiandru B., 2021. Dynamic volatility modeling of Indonesian insurance company stocks, Jurnal Ekonomi dan Studi Pembangunan, 14(1), 1-13.
  • Chaiyawat T., Guayjarernpanishk P., 2024. Effective forecasting of insurer capital requirements: ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH approaches, Emerging Science Journal, 8(6), 2173-2196.
  • Chalissery N., Anagreh S., Nishad T.M., Tabash M.I., 2022. Mapping the trend, application and forecasting performance of asymmetric garch models: A review based on bibliometric analysis, Journal of Risk and Financial Management, 15(9), 406.
  • Cheng J., Elyasiani E., Lin T.-T., 2010. Market reaction to regulatory action in the insurance industry: The case of contingent commission, Journal of Risk and Insurance, 77(2), 347-368.
  • Değirmenci N., Akay A., 2017. Finansal verilerin ARIMA ve ARCH modelleriyle öngörüsü: Türkiye örneği, Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 12(3), 15-36.
  • Dickey D.A., Fuller W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74(366), 427-431.
  • Elyasiani E., Staikouras S.K., Dontis-Charitos P., 2012. Cross-industry product diversification and contagion in risk and return: The case of bancassurance and ınsurance‐bank takeovers, The Journal of Risk and Insurance, 833), 681-718.
  • Enders W., 2014. Applied econometric time series (4th ed.), Wiley.
  • Engle R.F., 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50(4), 987-1007.
  • Engle R.F., Lee G., 1999. A long-run and short-run component model of stock return volatility (In: Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W.J. Granger, Oxford University Press, Oxford, Editors: Engle, R.F. and White, H.), 475-497.
  • Ertek Z., 2023. Zorunlu Deprem Sigortası/Dask Poliçesi, Legal Blok, Erişim adresi: https://legal.com.tr/blog/genel/zorunlu-deprem-sigortasi-dask-policesi/.
  • Fırat M., 2022. Deprem ve toplumsal etkileri, Tezkire, 80, 47-72.
  • Ghalanos A., 2013. Introduction to the rugarch package (Version 1.0-14).
  • Goldman E., Shen X., 2018. Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement, Bank of Canada Staff Working Paper, No. 2018-21.
  • Hacker R.S., Hatemi-J A., 2021. Model selection in time series analysis: Using information criteria as an alternative to hypothesis testing, Journal of Economic Studies, 49(6), 1055-1075.
  • Hamilton J.D., 1994. Time Series Analysis. Princeton, NJ: Princeton University Press.
  • Hansen B.E., 1992. Testing for parameter instability in linear models, Journal of Policy Modeling, 14(4), 517-533.
  • Hyndman R.J., 2014. Plotting the characteristic roots for ARIMA models, Hyndsight blog, Erişim adresi: https://robjhyndman.com/hyndsight/arma-roots/.
  • Jia Z., Jin Z., Marchandon M., Ulrich T., Gabriel A.-A., Fan W., Shearer P., Zou X., Rekoske J., Bulut F., Garagon A., Fialko Y., 2023. The complex dynamics of the 2023 Kahramanmaraş, Turkey, Mw 7.8-7.7 earthquake doublet, Science, 381(6661), 985-990.
  • Kingsley A., Uche P.I., 2019. Volatility modelling using ARCH and GARCH models: A case study of the Nigerian Stock Exchange, International Journal of Mathematics Trends and Technology, 65(4), 58-63.
  • Kurt F.E., Senal S., 2018. Sigorta Sektörü Hisse Senedi Piyasasında Volatilite Modellemesi: Arch-M Yöntemi ile Borsa İstanbul’da Bir Uygulama, Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(32), 314-332.
  • Lee S., 2025a. Mastering the Jarque-Bera Test: A Guide to Data Normality, Number Analytics, Erişim adresi: https://www.numberanalytics.com/blog/mastering-jarque-bera-test-data-normality-guide.
  • Lee S., 2025b. Ultimate Guide to ARCH models in time series, Number Analytics, Erişim adresi: https://www.numberanalytics.com/blog/ultimate-guide-arch-models.
  • Liu H., Li R., Yuan J., 2018. Deposit insurance pricing under GARCH. Finance Research Letters, 26, 242-249.
  • Liu T., Shi Y., 2022. Innovation of the Component GARCH Model: Simulation Evidence and Application on the Chinese Stock Market, Mathematics, 10(11), 1903.
  • MacKinnon J.G., 1996. Numerical distribution functions for unit root and cointegration tests, Journal of Applied Econometrics, 11(6), 601.618.
  • Nelson D.B., 1991. Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59(2), 347-370.
  • Palm F.C., 1996. 7 GARCH models of volatility (In: Handbook of Statistics, Editors: G.S. Maddala & C.R. Rao), 14, 209-240.
  • Peovski V., 2024. Comparative analysis of forecasting models in the nonlife insurance: Insights from the SARIMA and ETS approaches, Risk Management and Insurance Review, 27(4), 507-549.
  • Phillips P.C.B., Perron P., 1988. Testing for a unit root in time series regression, Biometrika, 75(2), 335-346.
  • Sezgin Alp Ö., Kırkbeşoğlu E., 2015. Sigorta endeksi getirisinin doğrusal olmayan yapısı, Finansal Araştırmalar Ve Çalışmalar Dergisi, 7(13), 245-260.
  • Susanti D., Maraya N.S., Sukono Saputra J., 2024. Prediction of motor vehicle insurance claims using ARIMA-GARCH models, Operations Research: International Conference Series, 5(3), 86-92.
  • Timur S., 2025. Doğal afetlerin sosyal yaşama, bütçeye ve ekonomiye etkileri, International Journal of Social and Humanities Sciences Research, 12(118), 650-662.
  • Tseng J.-J., Li S.-P., 2011. Asset returns and volatility clustering in financial time series, Physica A: Statistical Mechanics and its Applications, 390(7), 1300-1314.
  • Uğurlu E., 2019. Research data analysis using eviews:an empirical example of modeling volatility (In: Research Data Access and Management in Modern Libraries, Editors: R.K. Bhardwaj and P. Banks), 292- 324, USA: IGI.
  • Waheed M., Alam T., Ghauri S.P., 2006. Structural breaks and unit root: Evidence from Pakistani macroeconomic time series, (MPRA Paper No. 1797), University Library of Munich, Germany.
  • Wanat S., Denkowska A., 2018. Dependencies and systemic risk in the European insurance sector: New evidence based on copula-DCC-GARCH model and selected clustering methods, Entrepreneurial Business and Economics Review, 4, 7-27.
  • White H., 1980. A Heteroscedasticity Consistent Covariance Matrix and Direct Test for Heteroscedasticity, Econometrica, 48, 817-838.
  • Zivot E., Andrews D.W.K., 1992. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis, Journal of Business & Economic Statistics, 10(3), 251-270.
  • Zakoian J., 1994. Threshold Heteroskedastic Models, Journal of Economic Dynamics and Control, 18, 931-955.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Afet Ekonomisi
Bölüm Araştırma Makalesi
Yazarlar

Muhammet Kotan 0000-0003-4456-9381

Gönderilme Tarihi 12 Ağustos 2025
Kabul Tarihi 24 Ekim 2025
Erken Görünüm Tarihi 12 Aralık 2025
Yayımlanma Tarihi 17 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 7 Sayı: Özel Sayı

Kaynak Göster

APA Kotan, M. (2025). Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme. Türk Deprem Araştırma Dergisi, 7(Özel Sayı), 84-98. https://doi.org/10.46464/tdad.1762942
AMA Kotan M. Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme. TDAD. Aralık 2025;7(Özel Sayı):84-98. doi:10.46464/tdad.1762942
Chicago Kotan, Muhammet. “Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme”. Türk Deprem Araştırma Dergisi 7, sy. Özel Sayı (Aralık 2025): 84-98. https://doi.org/10.46464/tdad.1762942.
EndNote Kotan M (01 Aralık 2025) Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme. Türk Deprem Araştırma Dergisi 7 Özel Sayı 84–98.
IEEE M. Kotan, “Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme”, TDAD, c. 7, sy. Özel Sayı, ss. 84–98, 2025, doi: 10.46464/tdad.1762942.
ISNAD Kotan, Muhammet. “Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme”. Türk Deprem Araştırma Dergisi 7/Özel Sayı (Aralık2025), 84-98. https://doi.org/10.46464/tdad.1762942.
JAMA Kotan M. Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme. TDAD. 2025;7:84–98.
MLA Kotan, Muhammet. “Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme”. Türk Deprem Araştırma Dergisi, c. 7, sy. Özel Sayı, 2025, ss. 84-98, doi:10.46464/tdad.1762942.
Vancouver Kotan M. Türkiye’de Zorunlu Deprem Sigortası Primlerinin Volatilite Dinamikleri Üzerine Ampirik Bir İnceleme. TDAD. 2025;7(Özel Sayı):84-98.

AÇIK ERİŞİM ve LİSANS


Bu derginin içeriği Creative Commons Attribution 4.0 International Non-Commercial License'a tabidir.




Flag Counter