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Methods for Estimating ETF Value at Risk and Expected Shortfall

Article Quant Q&A · Author: michael

Summary

The document addresses how to obtain value at risk and expected shortfall measures for exchange-traded funds when the figures are not readily available from common financial data websites. It recommends calculating the measures from the ETF’s return data, naming historical simulation, Monte Carlo simulation, and a parametric variance-covariance approach as possible methods.

As an alternative, it mentions a commercial risk analytics service where ETF identifiers can be entered to retrieve relevant metrics. The answer does not provide formulas, assumptions, implementation steps, or a comparison of the methods. It also gives no guidance on selecting a confidence level, estimation window, return distribution, or portfolio aggregation approach, so the suggested methods require further specification before their estimates can be compared or used for risk decisions.

Key ideas

  • ETF value at risk and expected shortfall can be estimated directly from return data.
  • Historical simulation, Monte Carlo simulation, and variance-covariance methods are identified as calculation approaches.
  • A commercial risk analytics service is presented as an alternative source of metrics.
  • The document does not explain method assumptions or specify confidence levels and estimation windows.

Tags

Full text
# Where can I find Value at Risk & Expected shortfall for ETF's?


# Where can I find Value at Risk & Expected shortfall for ETF's?












I'm struggling to find VaR & ES data for ETF's on websites such yahoo finance & Morningstar. Where can I find this data?

Thanks,

## Answer by owner (score 2, accepted)

https://quant.stackexchange.com/a/21974

Better to compute it by yourself either using `Historical simmulation`, `Monte Carlo`, or simple parametric method such as `variance-covariance`. Alternatively subscribe toBloomberg Risk Analytics, populate the ISIN(s) for your ETF(s) and get the relevant metrics.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.