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Are the log-returns of Italian open-end mutual funds normally distributed? A risk assessment perspective

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  • Michele Leonardo Bianchi

    (Bank of Italy)

Abstract

In this paper we conduct an empirical analysis of daily log-returns of Italian open-end mutual funds and their respective benchmarks in the period from February 2007 to June 2013. First, we estimate the classical normal-based model on the log-returns of a large set of funds. Then we compare it with three models allowing for asymmetry and heavy tails. We empirically assess that both the value at risk and the average value at risk are model-dependent and we show that the difference between models should be taken into consideration in the evaluation of risk measures.

Suggested Citation

  • Michele Leonardo Bianchi, 2014. "Are the log-returns of Italian open-end mutual funds normally distributed? A risk assessment perspective," Temi di discussione (Economic working papers) 957, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_957_14
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    References listed on IDEAS

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    2. Cesari, Riccardo & Panetta, Fabio, 2002. "The performance of Italian equity funds," Journal of Banking & Finance, Elsevier, vol. 26(1), pages 99-126, January.
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    4. Marco Corazza & Florence Legros & Cira Perna & Marilena Sibillo, 2017. "Mathematical and Statistical Methods for Actuarial Sciences and Finance," Post-Print hal-01776135, HAL.
    5. Kim, Young Shin & Rachev, Svetlozar T. & Bianchi, Michele Leonardo & Mitov, Ivan & Fabozzi, Frank J., 2011. "Time series analysis for financial market meltdowns," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1879-1891, August.
    6. Young Kim & Rosella Giacometti & Svetlozar Rachev & Frank Fabozzi & Domenico Mignacca, 2012. "Measuring financial risk and portfolio optimization with a non-Gaussian multivariate model," Annals of Operations Research, Springer, vol. 201(1), pages 325-343, December.
    7. Esposito, Lucia & Nobili, Andrea & Ropele, Tiziano, 2015. "The management of interest rate risk during the crisis: Evidence from Italian banks," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 486-504.
    8. Matthias Scherer & Svetlozar T. Rachev & Young Shin Kim & Frank J. Fabozzi, 2012. "Approximation of skewed and leptokurtic return distributions," Applied Financial Economics, Taylor & Francis Journals, vol. 22(16), pages 1305-1316, August.
    9. Takashi Isogai, 2014. "Benchmarking of Unconditional VaR and ES Calculation Methods: A Comparative Simulation Analysis with Truncated Stable Distribution," Bank of Japan Working Paper Series 14-E-1, Bank of Japan.
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    Cited by:

    1. Jimmie Goode & Kim & Fabozzi, 2015. "Full versus quasi MLE for ARMA-GARCH models with infinitely divisible innovations," Applied Economics, Taylor & Francis Journals, vol. 47(48), pages 5147-5158, October.
    2. Chou-Wen Wang & Sharon S. Yang & Jr-Wei Huang, 2017. "Analytic option pricing and risk measures under a regime-switching generalized hyperbolic model with an application to equity-linked insurance," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1567-1581, October.
    3. Choi, Jaehyung & Kim, Young Shin & Mitov, Ivan, 2015. "Reward-risk momentum strategies using classical tempered stable distribution," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 194-213.

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    More about this item

    Keywords

    open-end mutual funds; normal distribution; tempered stable distributions; value at risk; average value at risk;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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