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| Importance Sampling× | Value at Risk (VaR)× | |
|---|---|---|
| Fagområde≠ | Simulering | Finansiering |
| Familie≠ | Process / pipeline | Regression model |
| Oprindelsesår≠ | 1951 | 2007 |
| Ophavsperson≠ | Herman Kahn & Theodore Harris (RAND Corporation, 1951) | Jorion (textbook benchmark); popularised by RiskMetrics / J.P. Morgan |
| Type≠ | Monte Carlo variance-reduction technique | Financial risk measure |
| Oprindelig kilde≠ | Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley. DOI ↗ | Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). McGraw-Hill. ISBN: 978-0071464956 |
| Aliasser≠ | IS, weighted Monte Carlo, Önem Örneklemesi | VaR, value-at-risk, delta-normal VaR, historical simulation VaR |
| Relaterede | 5 | 5 |
| Resumé≠ | Importance sampling is a Monte Carlo variance-reduction technique that shifts the sampling distribution toward the region of interest — typically a rare or extreme event — so that informative samples are drawn far more often than under the original distribution. Developed at the RAND Corporation by Herman Kahn and Theodore Harris around 1951, it makes tail-probability estimation (such as Value-at-Risk or system-failure probability) tractable where standard Monte Carlo would require an astronomically large number of runs. | Value at Risk is a financial risk measure that estimates the maximum loss a position or portfolio could suffer over a fixed holding period at a given confidence level. It is the standard benchmark in risk management and regulatory capital calculations, developed in the textbook tradition of Jorion (2007) and the Basel market-risk framework. |
| ScholarGateDatasæt ↗ |
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