Filtered Historical Simulation Value-at-Risk Models and Their Competitors
SSRN Electronic Journal
2015
- 17Citations
- 3,287Usage
- 41Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Financial institutions have for many years sought measures which cogently summarise the diverse market risks in portfolios of financial instruments. This quest led institutions to develop Value-at-Risk (VaR) models for their trading portfolios in the 1990s. Subsequently, so-called filtered historical simulation VaR models have become popular tools due to their ability to incorporate information on recent market returns and thus produce risk estimates conditional on them. These estimates are often superior to the unconditional ones produced by the first generation of VaR models. This paper explores the properties of various filtered historical simulation models. We explain how these models are constructed and illustrate their performance, examining in particular how filtering transforms various properties of return distribution. The procyclicality of filtered historical simulation models is also discussed and compared to that of unfiltered VaR. A key consideration in the design of risk management models is whether the model’s purpose is simply to estimate some percentile of the return distribution, or whether its aims are broader. We discuss this question and relate it to the design of the model testing framework. Finally, we discuss some recent developments in the filtered historical simulation paradigm and draw some conclusions about the use of models in this tradition for the estimation of initial margin requirements.
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