Characterizing Heteroscedasticity

Gilles Zumbach
Additional contact information
Gilles Zumbach: Consulting in Financial Research

Chapter Chapter 12 in Discrete Time Series, Processes, and Applications in Finance, 2013, pp 181-196 from Springer

Abstract: Abstract The largest deviation from a simple random walk founds in the empirical time series is the volatility clustering, or heteroskedasticity. It is important to characterize at best the decay of the volatility lagged correlation because of its practical implications for the construction of processes. First, the volatility and correlation estimators most suited for this task are studied. Then, they are applied to a broad panel of financial time series. Finally, simple analytical shapes are estimated on the empirical lagged correlation. The best overall characterization is given by a logarithmic decay for increasing lags, whereas an exponential decay and power law decay can be eliminated. This shows that a multiscale structure should be used in the processes, capturing the observed long memory of the volatility clusters.

Keywords: Stock Index; Hurst Exponent; Robust Estimator; Asset Class; Correlation Estimator (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprfcp:978-3-642-31742-2_12

Ordering information: This item can be ordered from
http://www.springer.com/9783642317422

DOI: 10.1007/978-3-642-31742-2_12

Access Statistics for this chapter

More chapters in Springer Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().