A framework for measuring and predicting system risk with the conditional value at risk approach

Subject Areas : Financial Knowledge of Securities Analysis

Ja'far Baba Jani ¹ , M. Taghi Taghavi Fard ² , Amin Ghazali ³

1 - Faculty member of of Allameh Tabatabai University
2 - Faculty member of of Allameh Tabatabai University
3 - PhD student at Allameh Tabatabai University

Received: 2016-12-05 Accepted : 2017-06-22 Published : 2018-09-23

Keywords: systemic risk, systemic risk measurement, prediction of systemic risk, conditional value at risk (CoV, macro-prudential policies,

Abstract :

In recent years with the increasing integration and innovation in financial markets, concerns about the overall stability of the financial system has increased and the concept of systemic risk has become more important. systemic risk is the risk imposed by interlinkages and interdependencies in a system or market, where the failure of a single entity or cluster of entities can cause a crisis in the entire system or market. In this study, we presented a framework for measuring and predicting systemic risk in the capital market of Iran using conditional value at risk approach (CoVaR). On this basis, ΔCoVaR as a measure of systematic risk using quintile regression based on a set of state variables that indicates changes in the distribution of asset returns has been estimated. As well as to enhance the accuracy of the estimate, the research variables modeled after the conditional autoregressive value at risk model (CAViaR) has been developed and some Lagged firm specific characteristic has also been added. In order to test the validity of the model back testing methods have been used. On the other hand, The potential for systemic risk increases when volatility decreases (volatility paradox). In this study, we try to predict systemic risk by take advantage of the panel structure of the data and the relationship between ΔCoVaR and firm-specific variables that are available in certain sections.

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