Systematic risk (in economics often called aggregate risk or undiversifiable risk) is vulnerability to events which affect aggregate outcomes such as broad market returns, total economy-wide resource holdings, or aggregate income. In many contexts, events like earthquakes and major weather catastrophes pose aggregate risks they affect not only the distribution but also the total amount of resources. In this paper we study systemic risks in the Iranian banking sector by using two famous systemic risk measures theMES (marginal expected shortfall) and CoVaR. To compute both measures we employ Engle's Dynamic conditional correlation model. Our empirical analysis shows, first, that although these two systemic risk measures differ in defining the contributions to systemic risk, both are qualitatively very similar in explaining the cross-sectional differences in systemic risk contributions across banks. Last, using a threshold VAR model, we suggest an overall systemic risk measure – the aggregateMES – and its associated threshold value for use as an early warning indicator. The paper is innovative in terms of the use of statistical models (Dynamic conditional correlation model) and available data Looking for the rating of commercial banks using by approaches MES and COVAR.

The current paper examines intertemporal capital asset pricing model in Iran’s Stock Market. Dynamic conditional correlation was used to estimate conditional variance and covariance portfolios with market returns. Time varying beta is estimated by Kalman Filter method. Based on the obtained results, risk aversion coefficients were between 0.013 and 0.28 and the average was 0.20.Significance of risk aversion and insignificance of intercepts revealed that there is ICAPM in Iran’s Stock Market. The result also showed that assets with high correlation with market conditional volatilities have low expected returns in the next transaction period. In addition, assets having high correlation with exchange rate growth are induced by additional risk premium in exchange rate risks and will have high expected returns in the next transaction period.

The issue of the optimal portfolio selection has always been one of the essential concerns of the investors. One of the famous models for this purpose is the Mean-Variance model of Markowitz. While Markowitz uses static correlation in his model, the recent studies have shown that correlation between the assets change over time. Accordingly, this research first investigates the consistency of the correlation matrices of return among the selected financial markets. In addition, developing the Markowitz's Mean-Variance model and using the Multiple Fitness Functions Genetic Algorithm, it tries to compare the behavior of the optimal portfolios based on two models of "Dynamic conditional correlation" and "Static correlation". Hence, market price index of three groups of the Middle Eastern developing, South East Emerging and some of the developing countries have been collected. The findings confirm inequality of the correlation matrices of return during the period of study and also show that there is a significant difference between the behavior of international optimal portfolios based on the static and Dynamic models.

To study price volatility and vertical market integration in the livestock and poultry vertical market levels, the multivariate volatility models, including constant (CCC) and Dynamic (DCC and VCC) conditional correlation models were estimated using monthly time series data of broiler feed, chicken, broiler, hay, sheep, calf, beef and mutton prices from April 1997 to March 2014. Estimating the conditional correlation models showed that the constant conditional correlation assumption is a major restriction for the variables under our investigation. Except for retail and wholesale price correlations in poultry market which is constant over time the Dynamic conditional correlations are significantly different from the constant conditional correlations, so that the Dynamic conditional correlation has experienced strong fluctuations in all cases. The results suggest that in the livestock and poultry markets price information's transmit more from the input level to the retail and wholesale levels than the reverse direction. Moreover, the correlation between the wholesale and retail market levels was larger which means stronger price relations between these two market levels. Estimated volatility models indicate that shocks and news rather than past volatility have greater impact on the current volatility in Iranian livestock and poultry markets. This indicates the necessity of news management in Iran.

The present study evaluates the symmetric and asymmetric Dynamic conditional correlations between volatilities of oil prices and stock markets in Persian Gulf region, in the event of financial crisis contagion. To do so, Dynamic conditional correlation (DCC) and Asymmetric Dynamic conditional correlation (ADCC) models are used during the 1st week of 2004 to 47th week of 2019. The results indicate asymmetric Dynamic conditional correlation between Iran and Dubai stock markets, and symmetric Dynamic conditional correlation between Saudi Arabia stock market and OPEC crude oil prices. Moreover, the results show symmetric Dynamic conditional correlation between Qatar and Dubai stock markets, and asymmetric Dynamic conditional correlation between Saudi stock market and Brent crude oil. Interpretation of these results, that there is symmetric and asymmetric correlations between oil return index and stock returns of Dubai, Qatar and Saudi Arabia implies that risk managers should be fully aware of the fact that these markets are not immune from external shocks. Furthermore, the results suggest that Dubai and Iran stock markets are vulnerable to internal shocks (OPEC oil), and that Dubai stock market is among the riskiest markets of Persian Gulf region.