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A convergence-speed-dependent data quantity definition and its effect on risk estimation

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  • Jakob Krause

    (Martin-Luther University Halle-Wittenberg
    European Commodity Clearing)

Abstract

Data quantity plays a crucial role in the estimation of risk measures since ‘more data’ lead to a better estimator convergence and consequently to a better risk assessment. The objective of this paper is to use this relationship between data quantity and estimator convergence to formally derive a measure of data quantity for estimators based on weighted observations. For the case of a variance estimation and using exponentially weighted observations, this procedure leads to analytical formulas for the implied measure of data quantity. As such, this paper specifies the theoretical underpinnings of measures of data quantity which have been present in the literature (effective number of scenarios) and, as an application, demonstrates the effect of the specific measure of data quantity on risk assessment.

Suggested Citation

  • Jakob Krause, 2019. "A convergence-speed-dependent data quantity definition and its effect on risk estimation," Journal of Asset Management, Palgrave Macmillan, vol. 20(6), pages 469-475, October.
    • Handle: RePEc:pal:assmgt:v:20:y:2019:i:6:d:10.1057_s41260-019-00137-1
    DOI: 10.1057/s41260-019-00137-1
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    References listed on IDEAS

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    1. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
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    3. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    4. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
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