Effective Forecasting of Insurer Capital Requirements: ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH Approaches

Thitivadee Chaiyawat, Pannarat Guayjarernpanishk

Abstract


This research paper presents a comprehensive analysis of three prominent volatility and dependence models for financial time series: ARMA-GARCH, GARCH-EVT, and DCC-GARCH. These models are employed to assess and forecast capital requirements for life and non-life insurer investments. This study evaluates the models' performance in forecasting Value-at-Risk, using daily data on key Thai financial indicators (representing permissible insurer investment assets) from March 2009 to March 2024. Specifically, 1-day and 10-day VaR forecasts are generated using the ARMA-GARCH and DCC-GARCH models, while the ARMA-GARCH-EVT model is employed for 1-day VaR forecasting. Our findings indicate that the ARMA-GARCH model effectively captures time-varying volatility, while the GARCH-EVT approach enhances tail risk estimation, particularly relevant for stress testing. Additionally, the DCC-GARCH model allows for the examination of dynamic conditional correlations between assets, providing insights into portfolio diversification benefits. Rigorous backtesting procedures, employing Kupiec and Christoffersen tests with a rolling window of 1,000 out-of-sample observations, confirm that the majority of models accurately forecast VaR at their respective horizons, with only a very small subset of 10-day VaR models exhibiting limitations. These results highlight that ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH models offer insurers robust tools for estimating minimum capital requirements, forecasting investment risk, and guiding strategic asset allocation decisions. This research underscores the effectiveness of these models for practical application in the insurance industry while also emphasizing the importance of continued model validation, particularly for extended forecasting horizons.

 

Doi: 10.28991/ESJ-2024-08-06-03

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Keywords


Volatility Forecasting; GARCH Models; Dynamic Conditional Correlation; Insurance Risk Management; Capital Requirements; Value-at-Risk.

References


Kajwang, B. (2022). Effect of government and regulatory framework on growth of insurance industry. International Journal of Law and Policy, 7(1), 17–25. doi:10.47604/ijlp.1604.

Schwarcz, D. & Schwarcz, S. L. (2014). Regulating systemic risk in insurance. The University of Chicago Law Review, 81(4), 1569–1640. doi:10.2139/ssrn.2404492.

Xie, Z. (2013). Risk and regulation–a broader view on their consistency. Annals of Actuarial Science, 7(2), 169–174. doi:10.1017/s1748499513000067.

Gaganis, C., Hasan, I., & Pasiouras, F. (2016). Regulations, institutions and income smoothing by managing technical reserves: international evidence from the insurance industry. Omega, 59, 113–129. doi:10.1016/j.omega.2015.05.010.

Zhou, M., Peng, L., & Zhang, R. (2021). Empirical likelihood test for the application of SWQMELE in fitting an ARMA-GARCH model. Journal of Time Series Analysis, 42(2), 222–239. doi:10.1111/jtsa.12563.

Huang, Y., Wang, H., Chen, Z., Feng, C., Zhu, K., Yang, X., & Yang, W. (2024). Evaluating cryptocurrency market risk on the blockchain: an empirical study using the ARMA-GARCH-VaR model. IEEE Open Journal of the Computer Society, 5, 83-94. doi.org/10.1109/ojcs.2024.3370603.

Petkov, P., Shopova, M., Varbanov, T., Ovchinnikov, E., & Lalev, A. (2024). Forecasting volatility of SOFIX index with GARCH models. doi.org/10.20944/preprints202406.1434.v1.

Chen, C. & Watanabe, T. (2018). Bayesian modeling and forecasting of value at risk via threshold realized volatility. Applied Stochastic Models in Business and Industry, 35(3), 747–765. doi:10.1002/asmb.2395.

Ali, F., Suri, P., Kaur, T., & Bisht, D. (2022). Modelling time-varying volatility using GARCH models: evidence from the Indian stock market. F1000Research, 11, 1098. doi:10.12688/f1000research.124998.2.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. doi:10.1016/0304-4076(86)90063-1.

Hossain, A. & Nasser, M. (2011). Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns. Journal of Applied Statistics, 38(3), 533–551. doi:10.1080/02664760903521435.

Makhwiting, M. R., Lesaoana, M., & Sigauke, C. (2012). Modelling volatility and financial market risk of shares on the Johannesburg stock exchange. African Journal of Business Management, 6(27), 8065-8070. doi:10.5897/ajbm11.2525.

Rastogi, V. R., Vishvakarma, N. K., & Dhar, J. (2012). An empirical study on tactical asset allocation and forecasting. International Journal of Economics and Business Research, 4(4), 393–411. doi:10.1504/IJEBR.2012.047419.

Khelifa, H., Abderrahmane, D., & Belgoum, F. (2024). Modelling the volatility of Frankfurt stock exchange (DAX) returns using hybrid models. Financial Markets, Institutions and Risks, 8(1), 31-42. doi:10.61093/fmir.8(1).31-42.2024.

Bagalkot, S. S., A, D. H., & Naik, N. (2024). Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction. PeerJ Computer Science, 10, e1735. doi:10.7717/peerj-cs.1735.

Gençay, R. & Selçuk, F. (2004). Extreme value theory and value-at-risk: relative performance in emerging markets. International Journal of Forecasting, 20(2), 287–303. doi:10.1016/j.ijforecast.2003.09.005.

Lei, B., Zhang, B., & Song, Y. (2021). Volatility forecasting for high-frequency financial data based on web search index and deep learning model. Mathematics, 9(4), 320. doi:10.3390/math9040320.

Bedoui, R., Braeik, S., Goutte, S., & Guesmi, K. (2018). On the study of conditional dependence structure between oil, gold and USD exchange rates. International Review of Financial Analysis, 59, 134–146. doi:10.1016/j.irfa.2018.07.001.

Chege, C. K., Mungat’u, J. K., & Ngesa, O. (2016). Estimating the extreme financial risk of the Kenyan Shilling Versus US Dollar exchange rates. Science Journal of Applied Mathematics and Statistics, 4(6), 249–255. doi:10.11648/j.sjams.20160406.11.

Emenogu, N. G., Adenomon, M. O., & Nwaze, N. O. (2019). Modeling and forecasting daily stock returns of Guaranty Trust Bank Nigeria Plc using ARMA-GARCH models, persistence, half-life volatility and backtesting. Science World Journal, 14(3), 1–22.

Hollstein, F., & Prokopczuk, M. (2018). How aggregate volatility-of-volatility affects stock returns. The Review of Asset Pricing Studies, 8(2), 253–292. doi:10.1093/rapstu/rax019.

Iqbal, M., Iqbal, M., Jaskani, F., Iqbal, K., & Hassan, A. (2021). Time-series prediction of cryptocurrency market using machine learning techniques. EAI Endorsed Transactions on Creative Technologies, 8(28). doi:10.4108/eai.7-7-2021.170286.

Ofori-Boateng, K., Ohemeng, W., Agyapong, E. K., & Bribinti, B. J. (2022). The impact of COVID-19 on stock returns of listed firms on the stock market: Ghana's experience. African Journal of Economic and Management Studies, 13(1), 136–146. doi:10.1108/ajems-02-2021-0074.

Salameh, H. M., & Alzubi, B. (2018). An investigation of stock market volatility: evidence from Dubai financial market. Journal of Economic and Administrative Sciences, 34(1), 21–35. doi:10.1108/JEAS-04-2017-0020.

Shen, Z., Wan, Q., & Leatham, D. J. (2021). Bitcoin return volatility forecasting: a comparative study between GARCH and RNN. Journal of Risk and Financial Management, 14(7), 337. doi:10.3390/jrfm14070337.

Qi, H., & Wang, Y. (2022). Prediction and analysis of stock logarithmic returns based on ARMA-GARCH model. The 12th International Conference on Electronics, Communications and Networks (CECNet 2022), 4-7 November 2022, Online Conference. doi:10.3233/faia220575.

Wang, Y., Xiang, Y., Lei, X., & Zhou, Y. (2022). Volatility analysis based on GARCH-type models: evidence from the Chinese stock market. Economic Research-Ekonomska Istraživanja, 35(1), 2530–2554. doi:10.1080/1331677X.2021.1967771.

Degiannakis, S., Floros, C., & Dent, P. (2013). Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: international evidence. International Review of Financial Analysis, 27, 21–33. doi:10.1016/j.irfa.2012.06.001.

Degiannakis, S., & Kiohos, A. (2014). Multivariate modelling of 10-day-ahead VaR and dynamic correlation for worldwide real estate and stock indices. Journal of Economic Studies, 41(2), 216–232. doi:10.1108/jes-06-2012-0082.

Rossignolo, A. F., Fethi, M. D., & Shaban, M. (2012). Value-at-risk models and Basel capital charges: evidence from emerging and frontier stock markets. Journal of Financial Stability, 8(4), 303–319. doi.org/10.1016/j.jfs.2011.11.003.

Crato, N. & Ruiz, E. (2012). Can we evaluate the predictability of financial markets?. International Journal of Forecasting, 28(1), 1–2. doi:10.1016/j.ijforecast.2011.02.002.

Fernandez, V. (2003). Extreme value theory and value at risk. Revista de Análisis Económico, 18(1), 57–85.

Harmantzis, F. C., Miao, L., & Chien, Y. (2006). Empirical study of value at risk and expected shortfall models with heavy tails. The Journal of Risk Finance, 7(2), 117–135. doi:10.2139/ssrn.788624.

Shirvani, A. (2020). Stock returns and roughness extreme variations: a new model for monitoring 2008 market crash and 2015 flash crash. Applied Economics and Finance, 7(3), 78–95. doi:10.11114/aef.v7i3.4824.

Gençay, R., Selçuk, F., & Ulugülyaǧci, A. (2003). High volatility, thick tails and extreme value theory in value-at-risk estimation. Insurance: Mathematics and Economics, 33(2), 337–356. doi:10.1016/j.insmatheco.2003.07.004.

Gençay, R., & Selçuk, F. (2006). Overnight borrowing, interest rates and extreme value theory. European Economic Review, 50(3), 547–563. doi:10.1016/j.euroecorev.2004.10.010.

Zivot, E., & Wang, J. (2006). Modeling financial time series with S-PLUS. Springer, New York, United States. doi:10.1007/978-0-387-32348-0.

Bhattacharyya, M., & Ritolia, G. (2008). Conditional VaR using EVT–Towards a planned margin scheme. International Review of Financial Analysis, 17(2), 382–395. doi:10.1016/j.irfa.2006.08.004.

Chan, K. F., & Gray, P. (2006). Using extreme value theory to measure value-at-risk for daily electricity spot prices. International Journal of Forecasting, 22(2), 283–300. doi:10.1016/j.ijforecast.2005.10.002.

Deng, L., Ma, C., & Yang, W. (2011). Portfolio optimization via pair copula-GARCH-EVT-CVaR model. Systems Engineering Procedia, 2, 171–181. doi:10.1016/j.sepro.2011.10.020.

McNeil, A. J., & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7(3-4), 271–300. doi:10.1016/S0927-5398(00)00012-8.

McAleer, M. & Da Veiga, B. (2008). Single index and portfolio models for forecasting value at risk thresholds. Journal of Forecasting, 27(3), 217–235. doi:10.1002/for.1054.

Brooks, C., & Persand, G. (2003). Volatility forecasting for risk management. Journal of Forecasting, 22(1), 1–2. doi:10.1002/for.841.

Ang, A., & Chen, J. (2002). Asymmetric correlations of equity portfolios. Journal of Financial Economics, 63(3), 443–494. doi:10.1016/S0304-405X(02)00068-5.

Chesnay, F., & Jondeau, E. (2001). Does correlation between stock returns really increase during turbulent periods?. Economic Notes, 30(1), 53–80. doi:10.1111/1468-0300.00047.

Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. The Journal of Finance, 56(2), 649–676. doi:10.1111/0022-1082.00340.

Loretan, M. & English, W. B. (2000). Evaluating correlation breakdowns during periods of market volatility. SSRN Electronic Journal, 1-33. doi:10.2139/ssrn.231857.

Yang, S. Y. (2005). A DCC analysis of international stock market correlations: the role of Japan on the Asian Four Tigers. Applied Financial Economics Letters, 1(2), 89–93. doi:10.1080/17446540500054250.

Engle, R. F., & Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. Working Paper, NYU Stern School of Business, New York, United States. doi:10.3386/w8554.

Engle, R. (2002). Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350. doi:10.1198/073500102288618487.

Abdelkefi, S. Z., & Khoufi, W. (2015). Stock markets linkages before, during and after subprimes crisis: bivariate BEKK GARCH (1, 1) and DCC Models. International Journal of Economics, Finance and Management Sciences, 3(3), 213–230. doi:10.11648/j.ijefm.20150303.18.

Ampountolas, A. (2022). Cryptocurrencies intraday high-frequency volatility spillover effects using univariate and multivariate GARCH models. International Journal of Financial Studies, 10(3), 51. doi:10.3390/ijfs10030051.

Choi, K., & Hammoudeh, S. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Energy Policy, 38(8), 4388–4399. doi:10.1016/j.enpol.2010.03.067.

Cretì, A., Joëts, M., & Mignon, V. (2013). On the links between stock and commodity markets' volatility. Energy Economics, 37, 16–28. doi:10.1016/j.eneco.2013.01.005.

Jhunjhunwala, S., & Suresh, S. (2020). Commodity and stock market interlinkages: opportunities and challenges for investors in Indian market. Global Business Review, 25(2_suppl), S42-S58. doi:10.1177/0972150920946413.

Joyo, A. S., & Lefen, L. (2019). Stock market integration of Pakistan with its trading partners: a multivariate DCC-GARCH model approach. Sustainability, 11(2), 303. doi:10.3390/su11020303.

Liu, L., Rafique, A., Abbas, N., Umer Quddoos, M., Ahmad, M. M., & Siddiqi, A. A. (2024). Systemic risk spillover between the stock market and banking deposits: evidence from a sustainability perspective in the South Asian countries. Plos One, 19(7), e0288310. doi.org/10.1371/journal.pone.0288310.

Mishra, A. & Dash, A. K. (2024). Return volatility of Asian stock exchanges; a GARCH DCC analysis with reference of bitcoin and global crude oil price movement. Journal of Chinese Economic and Foreign Trade Studies, 17(1), 29-48. doi.org/10.1108/jcefts-01-2024-0009.

Adailah, R. M., Al-Damour, S. B., & Al-Majali, A. (2024). The impact of global energy price volatility on oil derivative and local price in Jordan: using DCC-GARCH model. International Journal of Energy Economics and Policy, 14(1), 336-348. doi.org/10.32479/ijeep.15158.

Ardia, D. (2008). Financial risk management with Bayesian estimation of GARCH models theory and applications. Springer Berlin, Germany. doi:10.1007/978-3-540-78657-3.

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (2008). Forecasting methods and applications (3rd Edi.). John Wiley & Sons, New York, United States. doi:10.2307/2287014.

Akaike, H. (1981). Likelihood of a model and information criteria. Journal of Econometrics, 16(1), 3–-14. doi:10.1016/0304-4076(81)90071-3.

Beirlant, J., Goegebeur, Y., Segers, J., & Teugels, J. L. (2006). Statistics of extremes: theory and applications. John Wiley & Sons, New York, United States. doi:10.1002/0470012382.

Balkema, A. A., & De Haan, L. (1974). Residual life time at great age. The Annals of Probability, 2(5), 792–804. doi:10.1214/aop/1176996548.

Pickands, J. (1975). Statistical inference using extreme order statistics. The Annals of Statistics, 3(1), 119–131. doi:10.1214/aos/1176343003.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91. doi.org/10.2307/2975974.

Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. Washington, DC: Division of Research and Statistics, Division of Monetary Affairs, Federal Reserve Board, United States.

Christoffersen, P. F. (1998). Evaluating interval forecasts. International Economic Review, 39(4), 841–862. doi:10.2307/2527341.

Floros, C. (2007). The use of GARCH models for the calculation of minimum capital risk requirements: international evidence. International Journal of Managerial Finance, 3(4), 360–371. doi:10.1108/17439130710824361.

Coles, S. (2001). An introduction to statistical modeling of extreme values. Springer Series in Statistics, Springer London, United Kingdom. doi:10.1007/978-1-4471-3675-0.

Bee, M., Dupuis, D. J., & Trapin, L. (2016). Realizing the extremes: estimation of tail-risk measures from a high-frequency perspective. Journal of Empirical Finance, 36, 86-99. doi:10.1016/j.jempfin.2016.01.006.

Huang, C. K., North, D., & Zewotir, T. (2017). Exchangeability, extreme returns and Value-at-Risk forecasts. Physica A: Statistical Mechanics and its Applications, 477, 204–216. doi:10.1016/j.physa.2017.02.080.

Karmakar, M., & Shukla, G. K. (2015). Managing extreme risk in some major stock markets: an extreme value approach. International Review of Economics & Finance, 35, 1–25. doi:10.1016/j.iref.2014.09.001.

Li, L. (2017). A comparative study of GARCH and EVT model in modeling value-at-risk. Journal of Applied Business and Economics, 19(7), 27-48.

Totić, S., & Božović, M. (2016). Tail risk in emerging markets of Southeastern Europe. Applied Economics, 48(19), 1785–1798. doi:10.1080/00036846.2015.1109037.

Degiannakis, S., Dent, P., & Floros, C. (2014). A Monte Carlo simulation approach to forecasting multi-period value at risk and expected shortfall using the FIGARCH-SKT specification. The Manchester School, 82(1), 71–102. doi:10.1111/manc.12001.

Echaust, K., & Just, M. (2020). Value at risk estimation using the GARCH-EVT approach with optimal tail selection. Mathematics, 8(1), 114. doi:10.3390/math8010114.

Gründl, H., Dong, M., & Gal, J. (2016). The evolution of insurer portfolio investment strategies for long-term investing. OECD Journal: Financial Market Trends, 2016(2), 1–55. doi:10.1787/fmt-2016-5jln3rh7qf46.

Christoffersen, P., Errunza, V., Jacobs, K., & Jin, X. (2014). Correlation dynamics and international diversification benefits. International Journal of Forecasting, 30(3), 807–824. doi:10.1016/j.ijforecast.2014.01.001.

Olgun, S. & Polat, M. (2024). Volatility and international interactions in financial markets: an analysis of the Turkish stock exchange and G7 countries. Trends in Business and Economics, 38(2), 102-112. doi:10.16951/trendbusecon.1468689.


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DOI: 10.28991/ESJ-2024-08-06-03

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