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Financial volatility: Issues and measuring techniques

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  • Daly, Kevin
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    Abstract

    This paper explains in non-technical terms various techniques used to measure volatility ranging from time invariant measures to time variant measures. It is shown that a weakness of the former measures arises from the underlying assumption that volatility is considered to be constant over time. This observation has led researchers to develop time variant measures based on the assumption that volatility changes over time. The introduction of the original ARCH model by Engle has spawned an ever increasing variety of models such as GARCH, EGARCH, NARCH, ARCH-M MARCH and the Taylor–Schwert model. The degree of sophistication employed in developing these models is discussed in detail as are the models characteristics used to capture the underlying economic and financial time series data including volatility clustering, leverage effects and the persistence of volatility itself. A feature of these more elaborate models is that they generally obtain a better fit to the data in-sample.

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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 387 (2008)
    Issue (Month): 11 ()
    Pages: 2377-2393

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    Handle: RePEc:eee:phsmap:v:387:y:2008:i:11:p:2377-2393

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: ARCH; GARCH; Taylor–Schwert; Multivariate; Implied volatility;

    References

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    Cited by:
    1. Bentes, Sonia R & Menezes, Rui, 2012. "On the predictive power of implied volatility indexes: A comparative analysis with GARCH forecasted volatility," MPRA Paper 42193, University Library of Munich, Germany.
    2. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2013. "Intraday volatility spillovers between spot and futures indices: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1795-1802.
    3. Irina Bilan, 2011. "Public Debt Developments In Eu Member States: Challenges And Solutions," Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi - Stiinte Economice, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 2011, pages 57-69, july.
    4. D’Urso, Pierpaolo & Cappelli, Carmela & Di Lallo, Dario & Massari, Riccardo, 2013. "Clustering of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2114-2129.

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