Multivariate return decomposition: Theory and implications

Arbeitspapier

In this paper, we propose a model based on multivariate decomposition of multiplicative - absolute values and signs - components of several returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional copula. The approach is detailed in the case of a bivariate decomposition. We outline the construction of the likelihood function and the computation of different conditional measures. The finite-sample properties of the maximum likelihood estimator are assessed by simulation. An application to predicting bond returns illustrates the usefulness of the proposed method.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2015-7

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; Asset Pricing; Trading Volume; Bond Interest Rates
• Subject: multivariate decomposition ; multiplicative components ; volatility and direction models ; copula ; dependence

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Anatolyev, Stanislav ; Gospodinov, Nikolay
• Event: Veröffentlichung
• (who): Federal Reserve Bank of Atlanta
• (where): Atlanta, GA
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/130621