Estimation of VaR in insurance companies listed on the Tehran Stock Exchange with RBC and EC approaches

Document Type : Research Paper

Authors

1 Department of Accountancy and Management, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

2 Department of Economics, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

Abstract

The capital level of insurance companies to meet shareholders' and the regulator's expectations is very important. This study was to estimate the VaR (Value at Risk) of insurance companies on the TSE  (Tehran Stock Exchange) with RBC (Risk-Based-Capital)  and EC (Economic Capital) approaches, nine insurance companies in TSE during 2011-2019 were selected. Historical simulation and MCS (Monte Carlo Simulation) methods were used to calculate VaR. In historical simulation, the probability of an insurance company's loss of more than 2.02\% of the asset value is 1\%. Meanwhile, the VaR obtained from MCS, at the confidence level of 99\%, VaR is 3.28\%. In CBA (Cost-Benefit-Analysis), the amount of capital desired by the shareholders is 1.25-1.3 of the existing capital. In SMR (Solvency Margin Ratio) the amount of capital desired by the regulator is 1.3 -1.4 of the existing capital. Finally, the target capital is 1.25-1.4 of the existing capital.

Keywords

[1] S. Bagherzade, The effective factor on the stock retune of the Tehran exchange, Financ. Res. 19 (2004), 35–64.
[2] S.A. Basher and P. Sadorsky, Hedging emerging market stock prices with oil gold, VIX, and bonds: A comparison
between DCC, ADCC, and GO-GARCH, Energy Econ. 54 (2016), 235–247.
[3] E.B. Burger and M.P. Starbird, The heart of mathematics: An invitation to effective thinking, Springer-Verlag,
New York, 2005.
[4] Central Insurance of I. R. Iran, Investment Regulations of Insurance Institutions, 2009.
[5] Central Insurance of I. R. Iran, Regulations on calculating and monitoring insurance companies’ solvency margin,
2011.
[6] Central Insurance of I. R. Iran, The statistical report on the performance of the insurance industry, 2011, 2003–
2011.
[7] Central Insurance of I. R. Iran, The insurance industry in Islamic Republic of Iran’s 2025 vision plan, Including
Strategies, Policies, and Projects, 2012.
[8] P. Chung, H. Johnson and M. Schill, Asset pricing when returns are nonnormal: Fama-French factors vs. higherorder systematic co-moments, J. Bus. 2004 (2004), 321–358.
[9] J. Core, W. Guay and R. Verdi, Is accruals quality a priced risk factor?, J. Account. Econ. 46 (2008), 2–22.[10] S. L. Dragos, C. Mare, I. M. Dragota, C. M. Dragos and G. M. Muresan, The nexus between the demand for life
insurance and institutional factors in Europe: new evidence from a panel data approach, J. Econ. Res.-Ekonomska
Istra. 30 (2017), no. 1, 1477-149.
[11] C.J. Exley and A.D. Smith, The cost of capital for financial firms, Br. Actuar. J. 12 (2006), 229–301.
[12] E.F. Fama and K.R. French, The capital asset pricing model: Theory and evidence, J. Econ. Perspect. 18 (2004),
no. 3, 25–46.
[13] M. Gharakhani and Z. Majedi, Calculation of asset risk coefficients in insurance companies’ solvency margin
using VaR method, Insurance Res. J. 112 (2013), 127.
[14] A.N. Hitchcox, I.A. Hinder, A.M. Kaufman, T.J. Maynard, A.D. Smith and M.G. White, Assessment of target
capital for general insurance firms’, Br. Actuar. J. 13 (2007), 81–168.
[15] P. L’Ecuyer and R. Simard, TestU01: A C library for empirical testing of random number generators, ACM Trans.
Math. Softw. 33 (2007), no. 4, Article Number 22.
[16] J. Liew and M. Vassalou, Can book-to-market, size and momentum be risk factors that predict economic growth?,
J. Finan. Econ. 57 (2000), 221–45.
[17] WH. Press, et al. Numerical recipes in C: The art of scientific computing, 2nd. Cambridge: Cambridge University
Press, 1992.
[18] R. Raeei and A. Saeedi, Fundamentals of financial engineering and risk management, Samat Publications, 2015.
[19] P. Ralitsa, Do the Fama-French factors proxy for innovations in predictive variables?, J. Finance 61 (2006),
581–612.
[20] B. Shahriar and S.M.M. Ahmadi, Calculating the amount and share of optimal reliance maintenance in insurance
companies with VaR approach, Econ. Res. J. 8 (2009), no. 28, 223–243.
[21] S.M. Stigler, Statistics on the table: the history of statistical concepts and methods, Cambridge, Massachusetts:
Harvard University Press, 2002.
[22] Ch. Tarun and Sh. Lakshmanan, Earnings and price momentum, J. Finan. Econ. 80 (2016), 627–656.
[23] M. Youssef, L. Belkacem and Kh. Mokni, Value-at-Risk estimation of energy commodities: A long-memory
GARCH–EVT approach, Energy Econ. 51 (2015), 99–110.
Volume 13, Issue 2
July 2022
Pages 1207-1218
  • Receive Date: 04 November 2021
  • Revise Date: 19 December 2022
  • Accept Date: 09 February 2022