One-way analysis of variance with long memory errors and its application to stock return data

Jaechoul Lee, Kyungduk Ko

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Recent empirical results indicate that many financial time series, including stock volatilities, often have long-range dependencies. Comparing volatilities in stock returns is a crucial part of the risk management of stock investing. This paper proposes two test statistics for testing the equality of mean volatilities of stock returns using the analysis of variance (ANOVA) model with long memory errors. They are modified versions of the ordinary F statistic used in the ANOVA models with independently and identically distributed errors. One has a form of the ordinary F statistic multiplied by a correction factor, which reflects slowly decaying autocorrelations, that is, long-range dependence. The other is a test statistic such that the degrees of freedom of the denominator in the ordinary F test statistic is calibrated by the so-called effective sample size. Empirical sizes and powers of the proposed test statistics are examined via Monte Carlo simulation. An application to German stock returns is presented.

Original languageEnglish (US)
Pages (from-to)493-502
Number of pages10
JournalApplied Stochastic Models in Business and Industry
Volume23
Issue number6
DOIs
StatePublished - Nov 2007
Externally publishedYes

Keywords

  • ANOVA
  • Degrees of freedom
  • Effective sample size
  • F test
  • Long-range dependency

ASJC Scopus subject areas

  • Modeling and Simulation
  • General Business, Management and Accounting
  • Management Science and Operations Research

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