Identifying the Function of Systematic Risks on the Financial Prosperity of the Insurance Industry over Time

Authors
Islamic Azad University,North Tehran Branch
Abstract
This research aims to model the effective systematic risks of financial recovery in the insurance industry. This research is a type of applied research. The period of research is 11 years (1400-1390). For this purpose, the information on 14 systematic risks affecting the financial solvency of insurance companies was entered into dynamic, selective, and Bayesian averaging models. Based on the error rate, the Bayesian averaging model had the highest accuracy among the selected models. After estimating the model, 5 economic growth risks, inflation uncertainty, exchange rate, sanctions, and KOF index were selected; Also, based on the results of the TVPFAVAR model, it was assessed that the impact shock of the selected variables in the long-term period is stronger than the short-term period, which indicates that the elasticity of financial prosperity is greater than the changes in systematic risk variables compared to the short-term elasticity. Based on the results of economic growth and the KOF index, the positive effect and uncertainty variables of inflation, exchange rate, and sanctions hurt financial wealth in the general trend.
Keywords

Aye, G., Gupta, R., Hammoudeh, S., & Kim, W. J. (2015). Forecasting the price of gold using dynamic model averaging. International Review of Financial Analysis, 41, 257-266.
Aysan, A. F., Polat, A. Y., Tekin, H., & Tunalı, A. S. (2022). The Ascent of Geopolitics: Scientometric Analysis and Ramifications of Geopolitical Risk. Defence and Peace Economics, 1-19.
Belmonte, M., & Koop, G. (2014). Model switching and model averaging in time-varying parameter regression models. In Bayesian Model Comparison. Emerald Group Publishing Limited. 45-69.
BEN DHIAB, L. (2021). Determinants of Insurance firms' profitability: an empirical study of Saudi insurance market. The Journal of Asian Finance, Economics and Business, 8(6), 235-243.
Brokešová, Z., & Vachálková, I. (2016). Macroeconomic environment and insurance industry development: The case of Visegrad group countries.
Buncic, D., & Moretto, C. (2015). Forecasting copper prices with dynamic averaging and selection models. The North American Journal of Economics and Finance, 33, 1-38.
Caporale, G. M., Cerrato, M., & Zhang, X. (2017). Analysing the determinants of insolvency risk for general insurance firms in the UK. Journal of Banking & Finance, 84, 107-122.
Danieli, L., & Jakubik, P. (2022). Early warning system for the European insurance sector. Ekonomický časopis (Journal of Economics), 70(1), 3-21.
Di Filippo, G. (2015). Dynamic model averaging and CPI inflation forecasts: A comparison between the Euro area and the United States. Journal of Forecasting, 34(8), 619-648.
Drachal, K. (2016). Forecasting spot oil price in a dynamic model averaging framework—Have the determinants changed over time?. Energy Economics, 60, 35-46.
Ferreira, D., & Palma, A. A. (2015). Forecasting inflation with the Phillips curve: A dynamic model averaging approach for Brazil. Revista Brasileira de Economia, 69, 451-465.
Fytros, C. (2021). The aporetic financialisation of insurance liabilities: Reserving under Solvency II. Finance and Society, 7(1), 20-39.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the econometric society, 357-384.
Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of econometrics, 45(1-2), 39-70.
Koop, G. (2012). Using VARs and TVP-VARs with many macroeconomic variables. Central European Journal of Economic Modelling and Econometrics, (3),143-153.
Koop, G., & Korobilis, D. (2009). Manual to accompany MATLAB package for Bayesian VAR models. Retrieved, 10, 2012.
Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends® in Econometrics, 3(4), 267-358.
Koop, G., & Korobilis, D. (2011). UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?. Economic Modelling, 28(5), 2307-2318.
Koop, G., & Korobilis, D. (2012). Forecasting inflation using dynamic model averaging. International Economic Review, 53(3), 867-886.
Koop, G., & Korobilis, D. (2013). A new index of financial conditions. Available at SSRN 2374980.
Koop, G., McIntyre, S., Mitchell, J., & Poon, A. (2020). Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970. Journal of Applied Econometrics, 35(2), 176-197.
Korobilis, D. (2013). Assessing the transmission of monetary policy using time‐varying parameter dynamic factor models. Oxford Bulletin of Economics and Statistics, 75(2), 157-179.
Li, Ting, & Li, Menggang (2020). An Empirical Analysis of the Factors Influencing the Development of Insurance Industry in China. SAGE open October -December 2020: 1 –10.
Moreira, R. R., Chaiboonsri, C., & Chaitip, P. (2014). Analysing monetary policy's transmission mechanisms through effective and expected interest rates: an application of MS-models, Bayesian VAR and cointegration approaches for Brazil. International Journal of Monetary Economics and Finance, 7(1), 1-12.
Naser, H. (2014). An Econometric Investigation of Forecasting GDP, Oil Prices, and Relationships among GDP and Energy Sources (Doctoral dissertation, University of Sheffield).
Naser, H., & Alaali, F. (2018). Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach. Empirical Economics, 55, 1757-1777.
Ndaru, A. P. H., & Soesetio, Y. (2021). Early Warning System Analysis of General Insurance Companies. KnE Social Sciences, 72-86.
Rauch, J., & Wende, S. (2015). Solvency prediction for property-liability insurance companies: Evidence from the financial crisis. The Geneva Papers on Risk and Insurance-Issues and Practice, 40, 47-65.
Siddik, M. N. A., Hosen, M. E., Miah, M. F., Kabiraj, S., Joghee, S., & Ramakrishnan, S. (2022). Impacts of Insurers’ Financial Insolvency on Non-Life Insurance Companies’ Profitability: Evidence from Bangladesh. International Journal of Financial Studies, 10(3), 80.
Stock, J. H., & Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American statistical association, 97(460), 1167-1179.
Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.
Stock, J. H., & Watson, M. W. (2005). An empirical comparison of methods for forecasting using many predictors. Manuscript, Princeton University, 46.
Stock, J. H., & Watson, M. W. (2006). Forecasting with many predictors. Handbook of economic forecasting, 1, 515-554.
Stock, J., & Watson, M. (1998). Diffusion indexes. NBER Working Paper No.w6702.
Ul Din, S. M., Abu-Bakar, A., & Regupathi, A. (2017). Does insurance promote economic growth: A comparative study of developed and emerging/developing economies. Cogent Economics & Finance, 5(1), 1390029.