جستجوی مقالات مرتبط با کلیدواژه "همبستگی شرطی پویا" در نشریات گروه "مالی"

The purpose of this paper was to assess the overall risk in various financial sectors, including banking, insurance and investment companies. Pervasive risk in general indicates the possibility of collapse of the entire financial system in a crisis. While in most cases, investors in different markets worry about losing the value of a stock or commodity and measure the risks involved, the risk is pervasive, focused on the market as a whole and likely to fall. In this study, the method of measuring changes in value at risk based on the returns of financial institutions has been used. In this study, statistical information of banks, investment companies and commercial insurances during the years 1380-1399 has been used. The results show that all three sectors during this time period are significantly involved in hedging risk in Iran and investment companies have the largest share in hedging risk, followed by segments respectively. Banking and insurance are included.

The aim of this research is to compare the performance of the value-at-risk model with the liquidity-t-copula approach with dynamic conditional correlation (t-copula-GARCH-LVaR) with the value-at-risk (VaR) model to optimize the portfolio in the Tehran Stock Exchange. In the current research, in order to test the desired hypotheses, the period is between 2001 and 2021. All the variables used in this research on a quantitative scale and observations in the form of time series are the daily logarithmic returns of 40 stock market indices, including 39 industry indices and one index of fixed-income bonds from the beginning of September 2011 to the end of July 2021. In this research, to perform the final analysis, all the calculations required for this research were done using the open-source software R 4.2.1. Our results showed that the t-copula-GARCH-LVaR optimization model performs better according to the Sharpe criterion based on Mann–Whitney U test at the 95% test level.

In this paper, the effect of banks network topology on banks systemic risk in Tehran Stock Exchange have been investigated by using banks daily stock return from 2013 to 2021. First, systemic risk index have been measured and decomposed by using EVT approach and then this index divided into two dimensions, bank tail risk and systemic linkage. Then the network between banks listed in the banking industry of Tehran Stock Exchange have been created based on dynamic conditional correlations (DCC) by using the spanning minimum tree (MST) approach and the banks network topology have been measured. Finally, the relationship between banks systemic risk and its dimensions with banks network topology have been investigated by using regression of panel data. According to the results, Post-Bank, Tejarat and Saderat banks have the highest and Karafarin and Eghtesad-Novin banks have the lowest systemic risk, respectively. Also, the regression results showed that there is a positive and significant relationship between the variables of node strength, betweenness centrality and size with the banks systemic risk and a negative and significant relationship between the variables of node degree and liquidity with banks systemic risk.

With the recent extension of markets and increasing financial interactions, institutions are affected by their systemic risk and the systemic risk of other institutions and markets. Also, by changing the structure and characteristics of institutions in their complex network, the Systemic risk contribution of these institutions will be different. It is important for lawmakers, investors, and others to control, manage and reduce systemic relationship between local topology structure and systemic risk contribution by panel data regression analysis, it found that there is a significant relationship between the change of Conditional Value-at-Risk (∆CoVaR) and the local topology structure such as node closeness centrality, node strength, and node degree. So, there is a significant relationship between systemic risk contribution and the local topology structure. The results show that there is a positive relationship between systemic risk contribution and node closeness centrality, so financial institutions with larger node closeness centrality have higher systemic risk contributions. Also, there is a negative relationship between systemic risk contribution and node strength and node degree. Therefore, financial institutions with greater node strength and larger node degrees have lower systemic risk contributions. But with the data analyzed in this study, no significant relationship is found between node betweenness centrality and systemic risk contributions.risk. The purpose of this paper is to analyze the structure of the financial institutions' local topology on their systemic risk contribution. The purpose of this study is to investigate the contribution of systemic risk, using Tehran stock exchange data (on twenty stock companies from March 2014 to March 2019) with the change of Conditional Value-at-Risk (∆CoVaR). Initially, a dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH) is used to calculate the conditional correlation matrix and the minimum spanning tree (MST) is constructed. Then, the topology structure of the financial institutions' network and relationships between these characteristics and systemic risk is estimated. By quantifying the

In this study, we examined Dynamic Conditional Correlation (DCC) between important monetary and financial markets of Iran using multivariate DCC-FIAPARCH model and daily market returns during eleven years, (from 2007(1386) to 2018(1396)), and The existence of hidden characteristics in financial data, i.e. the ability to record long-term memory in data, power or conversion power (unconditional variance to conditional variance due to the addition of observation to the time series) and asymmetry of market reaction to good and bad news have been studied. The results show that OPEC oil market fluctuations haven't effect on the domestic markets of Iran, very high and importance dynamic conditional correlation between the coin market (gold) and exchange rate, the existence of leverage, power and long-term memory with strong ARCH/GARCH characteristics. We also found that market data have clustering characteristics and the assumption of t-student distribution is more appropriate than normal distribution for market return distributions.

Measuring the dynamics of the relationship between financial and non-financial industries with a systemic importance in the economy has attracted in many attention after the recent financial crisis. This paper examines conditional correlations by used models of multivariate GARCH in the ten years period among the fourteen industries with the highest market value in the capital market. The purpose of this study is to understand and identify the volatility transfer pattern among industries in order to predict financial fluctuations, as well as policy decisions and risk management to completion fundamental analysis. The results show that the "banking" industry has a positive relationship with the "pharmaceutical", "telecommunications" and "investment" industries and but also the "electricity, gas, steam and hot water" and "communications equipment" Has a negative conditional correlation.

In this research, it is attempted to present a framework for estimating and predicting systemic risk in Iran capital market using the marginal expected shortfall approach (MES), which has recently been considered in systemic risk literature. On this basis, MES as a systemic risk measure, will be analyzed in terms of assumptions for market and firm returns as a function of mean, volatility, correlation, and tail expectations and its components will be measured using an ARMA-GJR-GARCH-DCC framework and a nonparametric tail expectation estimator. In this way, a weekly panel will be created from the company's MES. On the other hand, the systemic risk is built up in a period that looks calm and low fluctuations, and is accumulated until activation. In other words, systemic risk potential increases as fluctuations decrease. In this study, it was attempted to predict systemic risk by taking advantage of the panel structure of the data and the relationship between MES and firm-specific variables that are available in certain sections.

Today, addressing the issue of volatility spillovers in different markets and their relationship with each other, in terms of its use in the prediction of shocks and crises, is an important issue in empirical finance research. Volatility Spillover indicate data transfer process and the flow of capital between markets.This paper investigate the dynamic conditional correlation and Volatility Spillover among returns of crude oil price and stock index by using Baba, engel, kroner and Kraft (BEKK), constant conditional correlation (CCC), dynamic conditional correlation (DCC) and multivariate GARCH model (VARMA-AGARCH) for 144 monthly data up to 2016.The aim of this study is identifing the effect of important external shocks such as oil price to stock index fluctuations in order to used for financial flactuations management, investment decisions and risk management. The results indicate a conditional correlation of volatility in the short term and the spillover effects of the oil price on the stock index.