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A Multivariate Random Walk Model with Slowly Changing Drift and Cross-correlation Applied to Finance

Author

Listed:
  • Yuanhua Feng

    (University of Paderborn)

  • David Hand

    (Imperial College)

  • Yuanhua Feng

    (Brunel University)

Abstract

A new multivariate random walk model with slowly changing drift and cross-correlations for multivariate processes is introduced and investigated in detail. In the model, not only the drifts and the cross-covariances but also the cross-correlations between single series are allowed to change slowly over time. The model can accompany any number of components such as many number of assets. The model is particularly useful for modelling and forecasting the value of financial portfolios under very complex market conditions. Kernel estimation of local covariance matrix is used. The integrated effect of the estimation errors involved in estimating the integrated processes is derived. Practical relevance of the model and estimation is illustrated by application to several foreign exchange rates.

Suggested Citation

  • Yuanhua Feng & David Hand & Yuanhua Feng, 2012. "A Multivariate Random Walk Model with Slowly Changing Drift and Cross-correlation Applied to Finance," Working Papers CIE 50, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:50
    as

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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP50.pdf
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    References listed on IDEAS

    as
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    2. Härdle, Wolfgang & Tsybakov, A. & Yang, L., 1996. "Nonparametric Vector Autoregression," SFB 373 Discussion Papers 1996,61, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

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