Examining the frequency-time dynamic relationship using the TVP-VAR-BK model for insurance, bank, and investment companies

Document Type : Research Paper

Authors

1 Assistant Professor, Faculty of Economic and Administrative Sciences, Qom University, Qom, Iran.

2 Associated Professor, Faculty of Economic and Administrative Sciences, Qom University, Qom, Iran.

3 MA student in Economics, Faculty of Economic and Administrative Sciences, Qom University, Qom, Iran.

10.22034/epj.2024.21037.2548

Abstract

Purpose: The flow of investment in various industries is affected by macro-economic and political conditions. By analyzing these factors and the internal factors of the stock market, investors buy and sell stocks. In such conditions, fluctuations are transferred to other markets. Following the increased volatility in an industry, it becomes difficult for investors to understand and analyze that industry. It increases the expectation of speculation, and, in such a situation, trust in a particular industry is lost. In the best case, capital outflow from the stock market does not occur. The continuous growing trend of extreme fluctuations in financial assets attracts the attention of many financial investors, policy makers and academic researchers. The transfer of returns and volatility between financial markets has been significantly strengthened and complicated due to globalization, technological development and financialization of commodity markets. It is widely accepted that the integration and financialization of the global market not only leads to increased liquidity and ease of trading in financial markets but also strengthens speculation and thus increases market volatility, which may act as a channel for temporal changes. There have been overflows of asymmetric fluctuations in financial markets as well as other markets over time. The small and large fluctuations there create different performance in the markets. The financialization of global commodities significantly contributes to stronger net contagion effects among financial markets and highlights the central role of financial markets in the transmission of volatility. Based on this, the occurrence of fluctuations in a financial industry can be transferred to other financial industries. Of course, the causality and intensity of transfer and receipt of fluctuations can be different over time and in different decimals of financial market returns, which is very important in the risk management of the investment portfolio. A review of the literature and the studies conducted in Iran's economy and financial markets show that, with the economic shocks brought to the country especially the recent sanctions, the capital market can play an important role to attract astray liquidity and finance the government. This can be done through the sale of shares of state-owned companies as well as the issuance of debt securities, which can show the importance of investigating the time-frequency relationship between the fluctuations of different stock market industries. Based on this, the current research deals with transmission, receipt, and the causal relationship between the fluctuations of the banking, insurance and investment industries during different years and in short-term, medium-term and long-term periods. This can be very important for investors as well as policy makers.
Methodology: The purpose of this study is to investigate the frequency-time dynamic relationship using the vector autoregression model with time-varying coefficients for insurance, bank, and investment companies in Iran's economy. In order to analyze the results, the TVP-VAR-BK pattern method was used in the period of 2011-2023 based on the frequency of the daily data. The variables used in this study included the index of banking, insurance and investment companies present in the stock exchange.
Findings and discussion: Based on the results, the banking and insurance industries were the receivers of returns, and the investment companies industry were the transmitters of returns in the short-term period. This shows that investment companies have been the main operators of the country's stock market financial network in the short term and have followed the movement and changes in the efficiency of the banking and insurance industries. Also, the average amount of communication between these industries was 17.53%. Investment companies were net contributors, and banks were net contributors. In the long-term period, insurance companies have received returns from other industries exactly equal to the returns that they have transferred. Accordingly, they have had a neutral role in the investigated network in this period of time. An important point is that investment companies have been net contributors, and the banking industry has been a net contributor in all the three time periods. This shows that any shock to investment companies' efficiency can cause a change in the efficiency of the banking and insurance industries. In general, the banking and insurance industries have been more passive in this network. This issue has been the case more in the short than the long term. Also, as the time period has passed on, the degree of connection between these three important industries of the capital market has decreased.
Conclusions and policy implications: The results of the research network analysis showed that, in general, the yield spillover from investment companies was transferred with a high intensity to the insurance companies and with a lower intensity to the banking industry. Also, the yield spillover was weakly transferred from the industry. The insurance has been transferred to the bank. In the short-term period, the yield overflow has been strongly transferred from investment to the bank and less intensively from investment to the insurance industry. In the medium-term period, there has been an overflow of returns from investments to insurance and weakly from banks to investments. Also, during this period, there has been a transfer of profits from the insurance industry to the bank. In the long-term period, the return has been strongly transferred from investment to insurance and more strongly from insurance to bank. According to the results of the research, the following suggestions can be considered by investors and policy makers. In the short-term period, if the investment companies that determine the network grow, the efficiency may be transferred to the banking and insurance industries, which is important to be considered by investors

Keywords

Main Subjects


Ahmed, A. & Huo, R. (2021). Volatility Transmissions across International Oil Market, Commodity Futures and Stock Markets: Empirical Evidence from China. Energy Economics, 93(2): 1-14.
Amiri, F. Derakhshani Darabi, K. & Asayesh, H. (2022). Application of the TV-GARCH Model in Estimating the Exchange Rate Volatility in Iran. The Journal of Economic Policy, 13(26): 61-87 (In Persian).
Aroury, M. E. H. Lahiani, A. & Khuong Nguyan, D. (2015). World Gold Prices and Stock Returns in China: Insights for Hedging and Diversification Strategies. Economic Modeling, 44(3): 273-282.
Ashena, M. & La’l Khezri, H. (2020). The Dynamic Correlation of Global Economic Policy Uncertainty Index with Stock, Exchange Rate and Gold Markets in Iran: Application of M-GARRCH and DCC Approach. Journal of Econometric Modelling, 5(2): 147-172 (In Persian).
Da, R. & Xiu, D. (2021). When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility. Econometrica, 89(6): 2787–2825.
Dadmehr, M. Rahnama Roodposhti, F. Nikoumaram, H. & Fallah Shams, M. F. (2021). Investigating the Effects of Contagion between Monetary and Financial Markets of Iran. Journal of Economics and Modelling, 12(2): 123-166 (In Persian).
Ebrahimi, M. (2018). Investigating the Impact of Macroeconomic Variables on the Iranian Stock Market using Data Mining Algorithms. Financial Economics, 13(49): 283-309 (In Persian).
Fallahi, F. Hghighat, J. Sanoubar, N. & Jahangiri, K. (2014). Study of Correlation between Volatility of Stock, Exchange and Gold Coin Markets in Iran with DCC-GARCH Model. Economics Research, 14(52): 147-123 (In Persian).
Farid, S. Naeem, M. A. Paltrinieri, A. & Nepal, R. (2022). Impact of COVID-19 on the Quantile Connectedness between Energy, Metals and Agriculture Commodities. Energy Economics, 109(2): 59-72.
Gong, X. & Xu, J. (2022). Geopolitical Risk and Dynamic Connectedness between Commodity Markets. Energy Economics, 110(2): 67-85.
Gong, X. Liu, Y. & Wang, X. (2021). Dynamic Volatility Spillovers across Oil and Natural Gas Futures Markets Based on a Time-Varying Spillover Method. Internationa Review Finance, 76(3): 101-130.
Hematy, M. & Ebrahimi, I. (2022). Exchange Rate Pass-Through to Transportation Sector in Iran: An Autoregressive Distributed Lags (ARDL) Model. Journal of Transportation Research, 19(3): 179-194 (In Persian).
Hoseini Ebrahimabad, S. A. Heidari, H. Jahangiri, K. & Ghaemi Asl, M. (2019). Using Bayesian Approach to Study the Time Varying Correlation among Selected Indices of Tehran Stock Exchange. Financial Research Journal, 21(1): 59-78 (In Persian).
Huang, J. Chen, B. Xu, Y. & Xia, X. (2023). Time-Frequency Volatility Transmission among Energy Commodities and Financial Markets during the COVID-19 Pandemic: A Novel TVP-VAR Frequency Connectedness Approach. Finance Research Letters, 53(3): 103-143.
Karami, S. & Rastegar, M. (2017). Return and Volatilities Spillover between Different Industries of Tehran Stocks’ Exchange. Journal of Risk Modeling and Financial Engineering, 14(2): 13-28 (In Persian).
Koop, G. Pesaran, M. H. & Potter, S. M. (1996). Impulse Response Analysis in Nonlinear Multivariate Models. Journal of Econometrics, 74(1): 119-147.
Li, X. Li, B. Wei, G. Bai, L. Wei, Y. & Liang, C. (2021). Return Connectedness among Commodity and Financial Assets during the COVID-19 Pandemic: Evidence from China and the US. Resources Policy, 73(2): 102-166.
Liow, K. H. Song, J. & Zhou, X. (2021). Volatility Connectedness and Market Dependence across Major Financial Markets in China Economy. Quantitative Finance and Economics, 5(3): 397-420.
Mensi, W. Yousaf, I. Vo, X. V. & Kang, S. H. (2022). Asymmetric Spillover and Network Connectedness between Gold, Oil and EU Subsector Markets. Journal of International Financial Market, 76(2): 89-97.
Mohseni, H. & Botshekan, M. H. (2020). Investigating Conditional Correlation among Industries in the Capital Market. Journal of Budget and Finance Strategic Research, 1(1): 75-91 (In Persian).
Mudiangombe, B. M. & Muteba, J. W. (2023). Impacts of U.S. Stock Market Crash on South African Top Sector Indices, Volatility, and Market Linkages: Evidence of Copula-Based BEKK-GARCH Models. International Journal of Financial Studies, 11(1): 77-92.
Naeem, M. A. Hasan, M. Arif, M. Balli, F. & Shahzad, S.J.H. (2020). Time and Frequency Domain Quantile Coherence of Emerging Stock Markets with Gold and Oil Prices. Physics, 55(3): 124-135.
Permeh, Z. (2019). Evaluation the Impacts of Covid19 Outbreaking on Iran's Manufacturing Sector: Application of Social Accounting Matrix. Industrial Economic Research, 3(8): 79-93 (In Persian).
Sezavar, M. R. Khazaei, A. & Eslamian, M. (2019). Conditional Correlation between Foreign Exchange Markets, Gold, Housing, Stock and Oil in the Iranian Economy. Economic Strategy, 8(29): 37-60 (In Persian).
Shah, A. A. & Dar, A. B. (2021). Exploring Diversification Opportunities across Commodities and Financial Markets: Evidence from Time-Frequency Based Spillovers. Resource Policy, 74(3): 102-138.
Shirafkan, M. Izadi, H. & Sistani Bandoee, Y. (2023). The Relationship between the Selected Industries Index of Iran Stock Exchange in a Quantile Time: Investigation of High, Low and Medium Efficiency States (TVP-Quantile VAR Approach). Financial Economics, 17(65): 121-152 (In Persian).
Taheri Bazkhaneh, S. (2023). An Investigation into the Effect of Liquidity and Exchange Rate on Inflation in Time-Frequency Domain. The Journal of Economic Policy, 15(29): 111-148 (In Persian).
Yadav, M. P. Sharma, S. & Bhardwaj, I. (2023). Volatility Spillover between Chinese Stock Market and Selected Emerging Economies: A Dynamic Conditional Correlation and Portfolio Optimization Perspective. Asia-Pac Financ Markets, 30(2): 427-444.
Yunus, N. (2020). Time-Varying Linkages among Gold, Stocks, Bonds and Real Estate. The Quarterly Review of Economics and Finance, 77(2): 165-185.