dynamic conditional correlation(dcc)

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.

After financial crises in the world, vast amount of scientific literature created in financial fields of which must of them are based on theoretical model. Generally, financial institutes located in financial system where a part of the system does not work properly, the whole financial system affected and released to other financial sectors and in next step penetrated into real part of economy and final result is economy stagnations. This fact is a reason for measuring financial system crises to prevent crises occurrence in the societies. Systematic risk index considered as one of them. Systematic risk index obtained by leverage degree calculation (debt), market size, and marginal expected shortfall (MES) to model the factors affecting it. This study is looking for the best approach to measure the systemic risk with respect to different sort of approaches in the world. For choosing the best approach we must choose the minimum error of these approaches. The models that we used in this research are multivariant garch, a model proposed by Brownlees and Engel which called VCT, factor models and bivariant statistical models. The result clearly shows that VCT model has the minimum error between the others.

Considering that investing in the stock market is associated with risk, therefore, its measurement is one of the most important issues for investors. The focus of the current research is on the calculation of the value at risk of Dynamic Conditional Correlation with the Solvency Approach (DCC t-Cupola LVaR) based on the copula and also the minimization problems of the above model to choose the optimal portfolio. Therefore, the purpose of this research is to compare the performance of the Markowitz models and value-at-risk model with the Liquidity-t Cupola approach and DCC-t Cupola LVaR to optimize the portfolios in Tehran Stock Exchange. While presenting a composite model, the extracted model should be examined to select the most efficient model to optimize the investment portfolio considering the conditions of investment uncertainty and non-linearity of the correlation between asset returns. To estimate DCC-tCopula-LVaR, first, the time series of the disturbance distribution of asset returns were estimated and standardized from the ARIMA-GARCH 1,1) model. Then, the marginal distributions of assets were estimated using Student's t-copula function. In the following, DCC-tCopula-LVaR values were calculated using the parametric method. In the last step, using linear programming, the optimal combination of the portfolio and the efficient frontier of the two models were calculated at the confidence levels of 80, 85, 90, 95, and 99 percent for the above two models. The findings of this research indicate that the Markowitz model performs better as the risk level increases. As the risk level decreases and declines from 99% to 80% (risk level in a sell position), the DCC-LVaR model performs better. Also, as the value at risk increases, the Sharp value of the DCC-tCopula-LVaR model decreases compared to the Markowitz model. Numerical experiences in the presented empirical analysis, Sharpe ratio, and two loss measures mean absolute value of error (MAE), as well as the root mean square error (RMSE) show that for an arbitrary portfolio, using the DCC t-Cupola LVaR model at a low-risk level is more efficient than the Markowitz model. By reducing the increase and increasing the value at risk, the Markowitz model has better efficiency. Also, using the DCC-tCopula-LVaR model, when the value at risk-adjusted by liquidity is low, brings better performance for stock portfolio optimization compared to the Markowitz model, based on the Sharpe measurement criterion.

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 in the financial markets there are different factors that can help investors to allocate their assets satisfactorily. The main concern for asset allocation is that if any return and volatility from a stock market spillovers into, return and volatility of another market. Spillover expresses shock transfer to other markets or countries regardless of the basic links exist between them. This paper investigates the existence of spillover effect in Tehran Stock Exchange. Specifically, we study the return and volatility spillover effects between 6 indices from August 2011 to March 2016 and Dynamic conditional Correlation model (DCC) has been employed in our study. Base on the results of this research, we reveal that the return and volatility of selected industries are impacting on each other. Some results suggest that Pharma - Industry has the highest impact and Oil, /coke and nuclear fuel Industry has the lowest impact on other selected industries.