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Scenario analysis for derivative portfolios via dynamic factor models

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  • Martin B. Haugh
  • Octavio Ruiz Lacedelli
Abstract
A classic approach to financial risk management is the use of scenario analysis to stress test portfolios. In the case of an S&P 500 options portfolio, for example, a scenario analysis might report a P&L of $-{\$}1 $−$1m in the event the S&P 500 falls 5% and its implied volatility surface increases by 3 percentage points. But how accurate is this reported value of $-{\$}1 $−$1m? Such a number is typically computed under the (implicit) assumption that all other risk factors are set to zero. But this assumption is generally not justified as it ignores the often substantial statistical dependence among the risk factors. In particular, the expected values of the non-stressed factors conditional on the values of the stressed factors are generally non-zero. Moreover, even if the non-stressed factors were set to their conditional expected values rather than zero, the reported P&L might still be inaccurate due to convexity effects, particularly in the case of derivatives portfolios. A further weakness of this standard approach to scenario analysis is that the reported P&L numbers are generally not back-tested so their accuracy is not subjected to any statistical tests. There are many reasons for this but perhaps the main one is that scenario analysis for derivatives portfolios is typically conducted without having a probabilistic model for the underlying dynamics of the risk factors under the physical measure P. In this paper we address these weaknesses by embedding the scenario analysis within a dynamic factor model for the underlying risk factors. Such an approach typically requires multivariate state-space models that can model the real-world behavior of financial markets where risk factors are often latent, and that are sufficiently tractable so that we can compute (or simulate from) the conditional distribution of unstressed risk factors. We demonstrate how this can be done for observable as well as latent risk factors in examples drawn from options and fixed income markets. We show how the two forms of scenario analysis can lead to dramatically different results particularly in the case of portfolios that have been designed to be neutral to a subset of the risk factors.

Suggested Citation

  • Martin B. Haugh & Octavio Ruiz Lacedelli, 2020. "Scenario analysis for derivative portfolios via dynamic factor models," Quantitative Finance, Taylor & Francis Journals, vol. 20(4), pages 547-571, April.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:4:p:547-571
    DOI: 10.1080/14697688.2019.1698757
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    Cited by:

    1. Gloria González‐Rivera & C. Vladimir Rodríguez‐Caballero & Esther Ruiz, 2024. "Expecting the unexpected: Stressed scenarios for economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 926-942, August.
    2. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    3. Gonzalez Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2021. "Expecting the unexpected: economic growth under stress," DES - Working Papers. Statistics and Econometrics. WS 32148, Universidad Carlos III de Madrid. Departamento de Estadística.

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