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Showing 2 results for Systematic Risk
Measurement of Communication and Systematic Risk in Tehran Stock Exchange Index (with Linear and Nonlinear Granger Causality Approach and Regression Switching)
Abed Abbasidarkhaneh, Farid Askari, Abdolrahim Hashemi Dizaj,
Volume 11, Issue 42 (12-2020)
Abstract
In this study, using linear and nonlinear Granger causality methods and regression switching, the relationships between the returns of important industry indices in the period 2008 to 2019 in order to invest in economic growth and development were examined. Based on the results obtained in the two periods of 2008 to 2013 and 2018 to 2019: 6, the relationship between the returns of the studied industry index has reached the highest value. In the linear Granger causality approach based on centrality criteria, the returns of metals index, machinery and investment are the most important and the returns of communication and banking index are the least important. It can also be said that the degree of effectiveness and efficiency of industry index returns is well affected by the amount of stock market fluctuations and this importance is asymmetric. In the nonlinear Granger causality approach based on the centrality criterion, the communication sector is the least important and the basic metals, chemical and machinery industries are the most important. In the period 2018 to 2019, the banking sector, automotive and communications industries are the most important and oil and metal products are the least important for investment.
Identifying the Function of Systematic Risks on the Financial Prosperity of the Insurance Industry over Time
English Habib Habib Shirafken Lamso, English Amir Gholami, English Seyyed Mehdi Ahmadi,
Volume 14, Issue 52 (9-2023)
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.

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