Value at risk calculation and diversification benefits
dc.contributor.advisor | Pandey, Ajay | |
dc.contributor.author | Khnadelia, Ritu | |
dc.contributor.author | Sarin, Vikram | |
dc.date.accessioned | 2016-08-19T10:41:18Z | |
dc.date.available | 2016-08-19T10:41:18Z | |
dc.date.copyright | 2002 | |
dc.date.issued | 2002 | |
dc.identifier.uri | http://hdl.handle.net/11718/18342 | |
dc.description.abstract | Absrtact Value at risk provides a single measure in dollars of a firm's potential losses. It makes a statement of the kind: "We are X percent certain that we will not lose more than V doilars in the nest N days". Here n represents the time most commonly used approach to VaR calculation is the JP Morgan Risk Metrics approach. However this method assumes constant volatility and lack of dependence between volatility and correlation. The paper proves that in fact when volatilities of indices increase the correlation between them also increases. This has the effect of increasing the VaR values and decreasing diversification benefits. As such the JP Morgan approach underestimates the VaR valur. An alternative solution could be the use of GARCH (1,1) model that incorporates the effects of time varying volatility thereby yielding more accurate result. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Indian Institute of Management Ahmedabad | en_US |
dc.relation.ispartofseries | SP;000978 | |
dc.subject | Risk calculation | en_US |
dc.subject | Diversification benefits | en_US |
dc.subject | Linear model | en_US |
dc.subject | Risk metrics | en_US |
dc.subject | (EWMA) | en_US |
dc.subject | GARCH (1,1) model | en_US |
dc.subject | Circulation of VaR | en_US |
dc.title | Value at risk calculation and diversification benefits | en_US |
dc.type | Student Project | en_US |
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