Determining non-fragile risks on financial solvency in insurance industry: A new approach to averaging models

Shirafkan Lamso, H.; Gholami, A.; Ahmadi, S.M.M.

doi:10.22056/ijir.2023.04.04

Document Type : Original Research Paper

Authors

Department of Economics, Faculty of Management and Social Sciences North Tehran Branch, Islamic Azad University, Tehran, Iran

https://doi.org/10.22056/ijir.2023.04.04

Abstract

BACKGROUND AND OBJECTIVES: This research aims to develop a new approach to modeling systematic and unsystematic risks as well as geopolitical risks, in financial solvency within the insurance industry in Iran. The objective is to improve the accuracy of prediction models used in the industry..
METHODS: The research follows developmental-practical approach and unilizes a descriptive-survey method. Data from 2011 to 2021, covering an 11-year period, were collected and analyzed. A total of 33 risk indicators affecting the financial solvency of insurance companies were examined using BMA, TVP-DMA, TVP-DMS, and BVAR models.
FINDINGS: The BMA model demonstrated the highest accuracy based on error rate. Through the analysis, 11 main variables were identified as significant factors influencing financial solvency including economic growth, inflation uncertainty, exchange rate, sanctions, KOF index, return on working capital, cash adequacy ratio, total debt-to-equity ratio, loss factor, Herfindahl-Hirschman index, and geopolitical risk. The results The results highlight the complex nature of financial solvency prediction in the insurance industry, emphasizing the need for a comprehensive and systematic approach.
CONCLUSION: This study emphasizes the limitations of relying on a single conceptual model in financial solvency modeling and decision-making. The multiplicity of factors influencing financial solvency requires a systemic perspective in managing insurance companies. Additionally, it is important to consider a wide range of variables rather than relying on a specific model or set of variables to ensure a comprehensive understanding of financial solvency in the industry.

Keywords

Main Subjects

financial markets

References

Abakah, E.J.A.; Tiwari, A.K.; Alagidede, I.P.; Gil-Alana, L.A., (2022). Re-examination of risk-return dynamics in international equity markets and the role of policy uncertainty, geopolitical risk and VIX: Evidence using Markov-switching copulas. Finance. Res. Lett., 47(7): 102-135 (34 Pages).

Abdel Jawad, Y.A.L.; Ayyash, I., (2019). Determinants of the solvency of insurance companies in Palestine. Int. J. Financ. Res., 10(6): 188-195 (8 Pages).

Ahmadi Quchan Atiq, M.; Sehat, S.; Nikumram, H.; Khalili Iraqi, M., (2022). The effects of efficiency and financial risk (Credit risk, operational risk, liquidity risk and financial wealth) on the performance of insurance companies admitted to the Tehran stock exchange: A case study in Iran. Financ. Econ. Policy. Q., 10(37): 167-219 (53 Pages). [In Persian]

Alizadeh, E.; Vakilifard, H.; Hamidian, M., (2022). Investigation of micro and macro economic factors affecting corporate financial performance: A fuzzy dimensional approach. Investment. Knowl., 11(41): 405-428 (24 Pages). [In Persian]

Aye, G.; Gupta, R.; Hammoudeh, S.; Kim, W., (2015). Forecasting the price of gold using dynamic model averaging. Int. Rev. Financ. Anal., 41(5): 257-266 (10 Pages).

Belmonte, M.; Koop, G., (2014). Model switching and model averaging in time-varying parameter regression models. Adv. Econom., 34(3): 45-69 (25 Pages). [In Persian]

Bloomberg, (2012). SWOT, PESTEL, Porterâs 5 forces and value chain. Available online.

Caldara, D. & Iacoviello, M., (2022). Measuring geopolitical risk. Am. Econ. Rev., 112(4): 1194-1225 (32 Pages).

Charitou, A., Neophytou, E., & Charalambous, C., (2007). Predicting corporate failure: Empirical evidence for the UK. Eur. Accounting. Rev., 13(3): 465-497 (33 Pages).

Dhiab, B., (2021). Determinants of insurance firms’ profitability: An empirical study of Saudi insurance market. J. Asia. Finance. Econ. Bus., 8(6): 235-243 (9 Pages).

Fytros, C., (2021). The aporetic financialisation of insurance liabilities: Reserving under solvency II. Finance. Soc., 7(1): 20-39 (20 Pages).

Haghverdilou, M.; Peykarjou, K.; Zomorodians, G.R., (2022). Introducing early warning system for solvency of Iranian insurance companies, using pane data method. Investment. Knowl., 11(44): 417-452 (36 Pages). [In Persian]

Hamilton, J.D., (1990). Analysis of time series subject to changes in regime. J. Econom., 45(1/2): 39-70 (32 Pages).

Koop, G., (2012). Using VARS and TVP-VARs with many macroeconomic variables. Center. Eur. J. Econ. Model. Econom., 4(1): 143-167 (25 Pages).

Koop, G.; Korobilis, D., (2010). Bayesian multivariate time series methods for empirical macroeconomics. Found. Trends. Econom., 3(4): 267-358 (92 Pages).

Litterman, R.B., (1986). Forecasting with bayesian vector autoregressions—five years of experience. J. Bus. Econ. Stat., 4(1): 25-37 (13 Pages).

Mazloomi, N.; Nateghi, A.A., (2020). A model of existing risks in Iran's insurance industry. Sci. J. Bus. Strategies., 16(13): 39-58 (20 Pages). [In Persian]

Mei, D.; Ma, F.; Liao, Y.; Wang, L., (2020). Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models. Energy. Econ., 86: 104-124 (21 Pages).

Moreira, R.R.; Chaiboonsri, C.; Chaitip, P., (2014). Analyzing monetary policy’s transmission mechanisms through effective and expected interest rates: An application of ms models: Bayesian VAR and co-integration approach for Brazil. Int. J. Monetary. Econ. Finance., 7(1): 1-12 (12 Pages).

Motiei, A.; Ismailzadeh, A.; Jahanshad, A., (2017). The relationship between financial solvency and financial variables of insurance companies. Iran. J. Insur. Res., 32(1): 23-42 (20 Pages). [In Persian]

Naser, H.; Alaali, F., (2018). Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach. Empirical. Econ., 55: 1757-1777 (21 Pages).

Peykarjou, K.; Haghverdilou, M.; Zomorodian, G., (2022). Introducing early warning system for solvency of Iranian insurance companies, using logit panel data method. Financ. Manage. Strategy., 10(3): 187-202 (16 Pages). [In Persian]

Rajabian, M.A.; Sabahi, A.; Lotfalipour, M.R.; Behnameh, M., (2019). The Effect of Macroeconomic Stability Indices Shocks on “TEPIX” by Bayesian VAR Model Approach. Econ. Growth. Dev. Res., 9(33): 79-90 (12 Pages). [In Persian]

Rauch, J.; Wende, S., (2015). Solvency prediction for property-liability insurance companies: Evidence from the financial crisis. Geneva. Pap. Risk. Insur. Issues. Pract., 40: 47-65 (19 Pages).

Sahebhonar, H.; Nadri, K., (2014). The economic analysis of the oil revenues increase impact on income distribution with a BVAR approach: Case study of Iran. Iran. Energy. Econ., 3(9): 115-149 (35 Pages). [In Persian]

Sala-i-Martin, X., Doppelhofer, G. and Miller, R. I. (2004). Determinants of long-term growth: A Bayesian averaging of classical estimates (BACE) approach. American economic review, 94: 813-835.

Shahbazadeh Zaferani, S.F., Abbasi, E., Didekhani, H.; Khozin, A., (2020). The behavior of nonlinear models in predicting the financial strength of stock exchange companies. Iran. J. Insur. Res., 9(1): 103-129 (27 Pages). [In Persian]

Siddik, N.A.; Hosen, E.; Miah, F.; Kabiraj, S.; Joghee, S.; Ramakrishnan, S., (2022). Impacts of insurers’ financial insolvency on non-life insurance companies’ profitability: Evidence from Bangladesh. Int. J. Financ. Stud., 10(3): 80-98 (19 Pages).

Sims, C., (1980). Macroeconomics and reality. Econometrica., 48(1): 1-48 (48 Pages).

Stock, J.H.; Watson, M.W., (1998). Diffusion indexes. NBER. Work. Pap.

Stock, J.H.; Watson, M.W., (2005). An empirical comparison of methods for forecasting using many predictors. Princeton. Univ., 1-43 (43 Pages).

Zellner, A., (1996). An introduction to Bayesian inference in econometrics. General & introductory statistics.

Letters to Editor

IJIR Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in IJIR should be sent to the editorial office of IJIR within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Iranian Journal of Insurance Research

Determining non-fragile risks on financial solvency in insurance industry: A new approach to averaging models

References

References

Letters to Editor

Send comment about this article

Volume 12, Issue 4 - Serial Number 46
August 2023
Pages 299-316

Determining non-fragile risks on financial solvency in insurance industry: A new approach to averaging models

References

References

Letters to Editor

Send comment about this article

Volume 12, Issue 4 - Serial Number 46August 2023Pages 299-316

Volume 12, Issue 4 - Serial Number 46
August 2023
Pages 299-316