dynamic conditional correlation

This article introduces a designed model to elucidate the contagion effect of risk in the credit portfolio of a bank. The dynamic conditional correlation approach has been applied on a daily basis within the time span from March 26, 2011, to September 2, 2021, to model the dynamic patterns of changes in correlation among various types of risks. Initially, the risk for each contract is determined using the optimized ARMA-GARCH model. Subsequently, Conditional Value at Risk (CVaR) is extracted as the risk factor for each contract. The obtained risk values are further modeled using the multivariate BEKK and DCC-GARCH models to analyze the contagion levels based on their results and address the research hypotheses. The research results indicate that the contagion of oscillations among different contracts in the bank's loan portfolio is not unidirectional. Additionally, it cannot be definitively claimed that the spillover of risk shocks from one contract to another is symmetric. This study is anticipated to contribute to the enhancement of predictive model performance and the development of effective risk management strategies in banks.

Exchange rate market play an important role as a measure to deal with monetary and currency crises and control its effects on the economy. The purpose of this study is to investigate the relationship between exchange market pressure, inflation rate, oil price and economic uncertainty in Iran using the VAR-DCC-GARCH model during the period of the first quarter of 1991 to the third quarter of 2021. Based on the results of the research, there is a dynamic conditional correlation between the variables, and volatilities of the inflation rate, oil price and economic uncertainty index lead to volatilities of the exchange market pressure. Also, the spillover effect of inflation rate, oil price and economic uncertainty index on exchange market pressure is positive and significant, which shows the important influence of these variables on exchange market pressure. Therefore, according to the correlation structure between the exchange market pressure and the macroeconomic variables, it is possible to control the effects of macroeconomic variables on the currency market by implementing appropriate policies in line with economic stability.

Understanding the transmission of shocks and fluctuations among financial assets is crucial for effective risk management by investors. This study analyzes the dynamics of return spillovers and turbulence propagation across 15 industries in the Tehran Stock Exchange from October 12, 2009, to May 15, 2024. Employing the extended joint vector autoregression framework with time-varying parameters (TVP-VAR Extended Joint), we examine return spillovers, while turbulence spillovers are investigated using the dynamic conditional correlation (DCC-GARCH) approach. Our findings reveal several insights: Firstly, return shocks and turbulence in the network exhibit distinct dynamics. Secondly, the systemic risk resulting from turbulence transmission indicates tighter and more intricate interconnections among industries compared to return transmission. Thirdly, apart from the dominant investment industry, which serves as a major transmitter of shocks and fluctuations in both analyses, the pharmaceutical, construction, and food industries also emerge as significant net transmitters. Finally, the study confirms a strong dynamic synergistic relationship between basic metals and metal minerals throughout the study period.

The return of the stock market is affected by several factors; although some of which are not economic, they strongly affect the financial markets. The Covid-19 epidemic is also among these factors that has severely affected the global economy, empathetically the financial markets. Therefore, considering the importance of this epidemic in the stock market, the current study evaluates the effects of the Covid-19 epidemic crisis on the stock return index of the financial markets of China, America, and France; besides, it examines its spillover effects on Iran. To investigate the contagion of turbulence and the direction of spillover from the mentioned countries to Iran, the weekly data of the stock return index available on the websites of the Iranian Stock Exchange have been used. Moreover, the stock exchange of foreign countries during two periods: before the outbreak of the Covid-19 epidemic (January 2018 to December 2019) and the time of the outbreak of the Covid-19 epidemic (January 2020 to December 2021) have been examined. Then Oxmetrics software was used to check the conditional correlation, and SPSS software was used to measure the stationarity and unconditional correlation.

The present research evaluates the spillover effects of the covid-19 epidemic on the stock return index of the financial markets of China, America, and France and examines the mutual relationship between the aforementioned countries and Iran using the weekly stock return data of Iran and foreign countries. It has been analyzed using (DCC-GARCH) and (CCC-GARCH) models.

In this article, αii represents the effects of arch in each of the variables' past period turbulences, and αij represents the effects of the shock of variable i on the current shock of variable j. This spillover effect is calculated as the square of the residuals arising from the forecasted yield patterns. Garch effects are considered as βii. In other words, βii shows the stability of the shock in each of the series.
ρij also expresses the conditional correlation between two variables, which provides a representation of their simultaneous movement. Of course, both terms αij and β can indicate the overflow between indicators, because the shock overflow effect is determined by non-diagonal values. In the constant conditional correlation model, coefficients αii and βii are significant. In other words, they represent the amount of shock transmission in the conditional shocks of countries' returns.

The results indicate that in the post-epidemic period, the Iranian stock market experienced a decrease in stock returns, which can be caused by factors such as the imposition of sanctions and the stagnation of economic activities in addition to the spread of Covid-19. Also, the collapse of the Iranian stock market, which occurred in August 2019, led to the confusion and pessimism of more and more investors and finally led to the withdrawal of capital from the stock market. In such an uncertain and chaotic atmosphere, the spread of Covid-19 also aggravated the existing conditions due to the restrictions and also the implementation of government quarantines. Also, the results show that at the moment of the outbreak of the Covid-19 virus, all the sample countries have faced a decrease in stock returns. During the covid-19 epidemic, the impact of the Iranian stock market on China has been greater than that of other studied countries, which is important because China and Iran are each other's trading partners. It should be mentioned that during this period due to restrictions on the borders, the relationship between Iran and China became prominent. Also, Iran's stock market is not strong enough to influence global financial markets including China, America and France.
The growth of the stock return index has been increasing during the four-year period (2018-2021) in China, America and France, however the stock return index of Iran has been decreasing. The growth of China's stock returns during this period has been higher than that in the other studied countries. Also, the stock return index of all sample countries has faced a decrease in the stock returns during the outbreak of Covid-19.

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 entry of cryptocurrencies into the capital market and their increasing price has created a fundamental change in the international currency and monetary exchanges of the world. The present study uses data from 2015 to 2021 to investigate the relationship between causality and dynamic conditional correlation between the price variable of 9 cryptocurrencies with different creation mechanisms. The results of this study show that Bitcoin, Litecoin and Ethereum, which have a proof-of-work mechanism, have a dynamic conditional correlation relationship in the short and long run.On the other hand, the dynamic conditional correlation coefficient of Ripple, Nano and Monero that have proof of stake mechanism and EOS, Tron and Stellar that have new consensus mechanisms were not significant in the short run but in the long run, their relationship is statistically significant. Also, there is a two-way causality relationship between the price of Bitcoin, Litecoin, Ethereum and Monero. But among the prices of Tron, Stellar, Nano and Ripple with a newer consensus mechanism than other cryptocurrencies, there is mainly a one-way causality relationship.

The purpose of this article was to measure systemic risk and the impact of fundamental variables on it in the country's banking system. In this regard, information from the period of 2010-2019 was used. In the first part, systemic risk indicators were estimated, then the impact of the fundamental variables of the country's banking system and financial stability was evaluated. In order to estimate the model, the final expected deficit method (MES) and panel data were used. The systemic risk index in this research is obtained from the calculation of the degree of leverage (debt size), market size and expected final deficit (MES) in order to finally be able to model the factors affecting it.In this research, first of all, different types of systemic risk assessment models have been evaluated according to the forecast error, and then, with the better selected model, the relationship between systemic risk and the important ratios of the country's banking system has been evaluated. The results showed that there is a significant positive relationship between independent variables such as inflation rate, external debt, government debt, liquidity growth, non-current facility rate, debt ratio and the ratio of book value of equity to market value, and independent variables such as profitability There is a significant negative relationship between the total index of the stock exchange, the growth rate of GDP and return on assets with the systemic risk index among the country's banking system.

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.

The occurrence of shocks in monetary and financial markets causes turbulence and disrupt relations among markets in particular the relation between risk and return of capital assets. Either, the dynamic correlation that exists among markets makes the effect of shocks contagions from one market to other markets. In this study, we investigate the effect of contagion among the monetary and financial markets of Iran using analysis of multivariate dynamic correlation among “conditional dynamic” variance and averages of daily market returns that were obtained by FIAPARCH model during 2007-2018 and using the Kargolok algorithm to identify structural breaking point in time series of market returns. The results indicate that the domestic political events have no effect on the occurrence of market shocks and the existence of the effect of contagion among those market. Also, we found that investors show heard behavior during and after turbulence period and when the turmoil ends and stability returns to the market, according to learning and changes that occurred in their utilities due to events and their new attitudes towards risks and returns, will change their investment behavior.

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.

One of the features of a financial market, the stock market in particular, is its affectability from other financial and non-financial markets. regarding the importance of this issue, the present study aimed at investigating the dynamic conditional correlation (DCC) between the returns on the domestic and foreign assets in monthly data (oil, industry, exchange and base metals including total, copper, steel) and returns on the stock price index in Iran during March 2001 to April 2017 using the DCC-FIAPARCH approach. The obtained results indicated a statistically significant and positive DCC coefficient difference between the metals, industrial products, and copper returns with the stocks returns. Consequently, it is not possible to put each of these assets with the stocks in an identical situation (purchase or sale), but instead they should be always placed in opposite situations for the purpose of risk control. However, with regard to other assets, the DCC was not significant; accordingly, the assets can be placed in the investment portfolio together with the stocks although the presence of such assets in the given portfolio did not help to reduce the portfolio risk.

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.

One of the features of a financial market, the stock market in particular, is its affectability from other financial and non-financial markets; on the other hand, investors always seek to create a portfolio with minimum risk while maintaining the expected return level. Therefore, perceiving the relationship between the stock returns and other assets can be helpful for investors to create an optimal portfolio. On this basis, regarding the importance of this issue, the present study aimed at investigating the dynamic conditional correlation (DCC) between the returns on the domestic and foreign assets in monthly data (oil, industry, exchange and base metals including total, copper, steel) and returns on the stock price index in Iran during March 2001 to April 2017 using the DCC-FIAPARCH approach. The obtained results indicated a statistically significant and positive DCC coefficient difference between the metals, industrial products, and copper returns with the stocks returns. Consequently, it is not possible to put each of these assets with the stocks in an identical situation (purchase or sale), but instead they should be always placed in opposite situations for the purpose of risk control. However, with regard to other assets, the DCC was not significant; accordingly, the assets can be placed in the investment portfolio together with the stocks although the presence of such assets in the given portfolio did not help to reduce the portfolio risk.

The exchange rate volatility as an important factor will affect the behaviour of supply and demand of financial markets participants. The importance of the spillover explanation between the two markets comes from managed floating exchange rate system in Iran and the large portion of the foreign currency-dependent industries in the capital market. This paper examines the dynamic conditional correlation and volatility spillover of the exchange rate on the capital market using three multivariate GARCH models in a 12-year period ending in 1395S.H.The purpose of this study is to explain the effect of shocks and the spillover of the currency market on the Iranian capital market. This could play an important role in investors' decisions, fundamental analysts and also sovereign institutions. The results confirm the existence of negative short-term and positive long-term sustainability of exchange rate shocks on capital market returns. Also, the asymmetric and positive volatility spillover to the Iranian capital market was confirmed.