Federal Reserve Bank of Atlanta
FRB Atlanta Working Paper
Multivariate return decomposition: theory and implications
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.
Cite this item
Stanislav Anatolyev & Nikolay Gospodinov, Multivariate return decomposition: theory and implications, Federal Reserve Bank of Atlanta, FRB Atlanta Working Paper 2015-7, 01 Aug 2015.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
Keywords: multivariate decomposition; multiplicative components; volatility and direction models; copula; dependence
This item with handle RePEc:fip:fedawp:2015-07
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