Estimating value-at-risk using crash-metrics
| dc.contributor.author | Otao, Calvin Mochoge | |
| dc.date.accessioned | 2019-05-09T07:36:19Z | |
| dc.date.available | 2019-05-09T07:36:19Z | |
| dc.date.issued | 2018 | |
| dc.description | A Research project Submitted in partial fulfillment of the requirements for the degree of Bachelor of Business Science in Financial Economics at Strathmore University | en_US |
| dc.description.abstract | The purpose of this study is to estimate Value-at-Risk using Crash-Metrics, a methodology proposed by (Wilmott, 2006) while applying them in the Kenyan context. We compare the results from the Beta Parametric Value-at-Risk, a methodology for estimating Value-at-Risk, proposed by (Sharpe, 1964). Crash-Metrics should according to (Wilmott, 2006), produce a higher loss scenario than Value-at-Risk. The dataset used includes daily returns of listed banks' stock and Nairobi All Share Index for the period beginning 2nd January 2015 to 31st December 2017. The results show that Crash-Metrics indeed outperforms Value-at-Risk during periods of markets stress by providing higher value for portfolio loss than the Beta Parametric Value-at-Risk. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11071/6495 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Strathmore University | en_US |
| dc.subject | cash-metrics | en_US |
| dc.subject | value-at-risk | en_US |
| dc.subject | Stock markets | en_US |
| dc.title | Estimating value-at-risk using crash-metrics | en_US |
| dc.type | Thesis | en_US |
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