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Histogram-valued data on value at risk measures: a symbolic approach for risk attribution

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  • Carole Toque
  • Virginie Terraza

Abstract

In this article, we develop the concept of histogram-valued data on value at risk for the classification of hedge fund risk. By using recent developments in data mining, it is a question of the classification of heterogeneous data in order to sort hedge funds by risk class. In practical terms, risk levels relative to measures of histogram-valued data on VaR are calculated as an aid to decision-making. The empirical study was carried out on 1023 HFR-based hedge funds, where we had estimated monthly ARMA-GARCH or asymmetric GARCH VaR and CVaR measures between 01 January 2003 and 31 December 2008. We identify two sub-periods: from 2003 to 2005, and from 2006 to 2008 in order to identify a recovery period after the 2001-2002 crisis and the impact of the 2007-2008 crisis. First, the symbolic approach allows us to construct the measures of histogram-valued data on VaR by optimizing the definition of categories. A symbolic principal component analysis shows that the indices coming from the VaR of the GARCH and asymmetrical GARCH are the most pertinent. Second, we apply a criterion of inter-class inertia and retain a partitioning of hedge funds into three classes by dynamic K -means cluster analysis. For each of our sub-periods and for each class, a risk level is defined on the basis of the categories of the most discriminating variable.

Suggested Citation

  • Carole Toque & Virginie Terraza, 2014. "Histogram-valued data on value at risk measures: a symbolic approach for risk attribution," Applied Economics Letters, Taylor & Francis Journals, vol. 21(17), pages 1243-1251, November.
    • Handle: RePEc:taf:apeclt:v:21:y:2014:i:17:p:1243-1251
    DOI: 10.1080/13504851.2014.920467
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    References listed on IDEAS

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    1. Turan G. Bali & Panayiotis Theodossiou, 2008. "Risk Measurement Performance of Alternative Distribution Functions," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(2), pages 411-437, June.
    2. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
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    Cited by:

    1. Ngoc Phu Tran & Thang Cong Nguyen & Duc Hong Vo & Michael McAleer, 2019. "Market Risk Analysis of Energy in Vietnam," Risks, MDPI, vol. 7(4), pages 1-13, November.
    2. Virginie Terraza & Aslı Boru İpek & Mohammad Mahdi Rounaghi, 2024. "The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    3. Duc Hong Vo & Ngoc Phu Tran & Tam Nguyen-Thanh Duong & Michael McAleer, 2019. "Risk analysis of energy in Vietnam," Documentos de Trabajo del ICAE 2019-14, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    4. Robert J. Powell & Duc H. Vo & Thach N. Pham, 2018. "Economic cycles and downside commodities risk," Applied Economics Letters, Taylor & Francis Journals, vol. 25(4), pages 258-263, February.
    5. Paravee Maneejuk & Nootchanat Pirabun & Suphawit Singjai & Woraphon Yamaka, 2021. "Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models," Mathematics, MDPI, vol. 9(21), pages 1-20, November.

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