Multivariate modelling of the autoregressive random variance process

Mike K. P. SO, Wai Keung LI, K. LAM

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

The autoregressive random variance (ARV) model proposed by Taylor (Financial returns modelled by the product of two stochastic processes, a study of daily sugar prices 1961–79. In Time Series Analysis: Theory and Practice 1 (ed. O. D. Anderson). Amsterdam: North‐Holland, 1982, pp. 203–26) is useful in modelling stochastic changes in the variance structure of a time series. In this paper we focus on a general multivariate ARV model. A traditional EM algorithm is derived as the estimation method. The proposed EM approach is simple to program, computationally efficient and numerically well behaved. The asymptotic variance‐‐covariance matrix can be easily computed as a by‐product using a well‐known asymptotic result for extremum estimators. A result that is of interest in itself is that the dimension of the augmented state space form used in computing the variance–covariance matrix can be shown to be greatly reduced, resulting in greater computational efficiency . The multivariate ARV model considered here is useful in studying the lead–lag (causality) relationship of the variance structure across different time series. As an example, the leading effect of Thailand on Malaysia in terms of variance changes in the stock indices is demonstrated. Copyright © 1997 Blackwell Publishers Ltd.
Original languageEnglish
Pages (from-to)429-446
JournalJournal of Time Series Analysis
Volume18
Issue number4
DOIs
Publication statusPublished - Jul 1997

Fingerprint

Time series
Modeling
Variance-covariance Matrix
Time series analysis
Computational efficiency
Random processes
Sugars
Byproducts
Stock Index
Malaysia
Stochastic Modeling
Time Series Analysis
Space Form
EM Algorithm
Extremum
Causality
Computational Efficiency
Stochastic Processes
State Space
Model

Citation

So, M. K. P., Li, W. K., & Lam, K. (1997). Multivariate modelling of the autoregressive random variance process. Journal of Time Series Analysis, 18(4), 429-446. doi: 10.1111/1467-9892.00060

Keywords

  • Autoregressive random variance process
  • EM algorithm
  • Observed information matrix
  • Stochastic volatility