Volatility (or risk) is a key variable in many areas of finance, and there are many applications that require an accurate estimate of volatility. One important application is in designing optimal dynamic hedging strategies. Engle (1982) proposed an autoregressive conditional heteroscedasticity (ARCH) model, which allows the conditional variance to change over time. This model has been extended ...

empirical researches have shown that in highly volatile market, conditional correlation between returns is stronger, so diversification cannot reduce risk. to test this claim in iran’s financial market, quintiles of stock return distribution have been estimated by kernel density and garch models. then, average conditional correlation, error variance and conditional capm has been calculated to t...

This paper provides an extension of the Dynamic Conditional Correlation model of Engle (2002) by allowing both the unconditional correlation and the parameters to be driven by an unobservable Markov chain. We provide the estimation algorithm and perform an empirical analysis of the contagion phenomenon in which our model is compared to the traditional CCC and DCC representations.

This paper proposes a class of parametric correlation models that apply a twolayer autoregressive-moving-average structure to the dynamics of correlation matrices. The proposed model contains the Dynamic Conditional Correlation model of Engle (2002) and the Varying Correlation model of Tse and Tsui (2002) as special cases and offers greater flexibility in a parsimonious way. Performance of the ...

Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In ...

This paper focuses on oil as a key determinant in US-GCC stock market interdependence. The analysis uses monthly data over the period from January 2003 to December 2019. interdependence between US and GCC is established using Asymmetric Dynamic Conditional Correlation model. We then investigate impact of both range macroeconomic variables nature correlation. Our results find that returns volati...

A small strand of recent literature is occupied with identifying simultaneity in multiple equation systems through autoregressive conditional heteroscedasticity. Since this approach assumes that the structural innovations are uncorrelated, any contemporaneous connection of the endogenous variables needs to be exclusively explained by mutual spillover effects. In contrast, this paper allows for ...

systemic risk is the risk of collapse in the financial system. due to the financial crisis that hit the world economy in 2008, the study of systemic risk in the banking sector became more attractive for researchers. in this research we study systemic risk in the iranian banking sector by using a famous systemic risk measure, the ∆covar. to compute the measure, we employ dynamic conditional corr...