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Conditional Autoregressive Value at Risk by Regression Quantiles: Estimating Market Risk for Major S
Year Of Publication: 2005
Month Of Publication: November
Pages: 46
Download Count: 659
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Comment Num: 0
Language: EN
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Who Can Read: Free
Date: 5-13-2006
Publisher: Administrator
Summary
This paper employs a new approach due to Engle and Manganelli (2004) in order toexamine market risk in several major equity markets, as well as for major companieslisted in New York Stock Exchange and Athens Stock Exchange. By interpreting theVaR as the quantile of future portfolio values conditional on current information,Engle and Manganelli propose a new approach to quantile estimation that does notrequire any of the extreme assumptions of the existing methodologies, mainlynormality and i.i.d. returns. The CAViaR model shifts the focus of attention from thedistribution of returns directly to the behaviour of the quantile. We provide acomparative evaluation of the predictive performance of four alternative CAViaRspecifications, namely Adaptive, Symmetric Absolute Value, Asymmetric Slope andIndirect GARCH(1,1) models. The main findings of the present analysis is that we areable to confirm some stylized facts of financial data such as volatility clustering whilethe Dynamic Quantile criterion selects different models for different confidenceintervals for the case of the five general indices, the US companies and the Greekcompanies respectively.
Author(s)
Kouretas, Georgios Sign in to follow this author
Zarangas, Leonidas Sign in to follow this author
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