Identification and Inference for Multivariate Cointegrated and Ergodic Gaussian Diffusions
Inference is considered in the multivariate continuous time Gaussian Ornstein-Uhlenbeck (OU) model on the basis of observations in discrete time. Under the hypothesis of ergodicity as well as cointegration, the classical identification or ‘aliasing’ problem is re-addressed and new results given. Exact conditions are given for (i) identification of individual parameters, as well as results for, (ii) identification of rank and cointegration parameters, and, furthermore (iii) for the existence of a continuous time OU process which embeds a discrete time vector autoregression. Estimation and cointegration rank inference are discussed. An empirical illustration is given in which the ‘cost-of-carry’ hypothesis is investigated.
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- Bergstrom, A. R.: Continuous Time Econometric Modelling, Oxford University Press, Oxford, 1990.Google Scholar
- Hansen, E. and Rahbek, A.: Stationarity and asymptotics of multivariate ARCH processes, with an application to robustness of cointegration analysis, Preprint, Department of Statistics and Operations Research, University of Copenhagen (1998).Google Scholar
- Jacobsen, M.: Homogenous gaussian diffusions in finite dimensions, Preprint, Department of Statistics and Operations Research, University of Copenhagen (1991).Google Scholar
- Johansen, S.: Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, 2nd edition, Oxford University Press, Oxford, UK, 1996.Google Scholar
- Johansen, S.: The asymptotic variance of the estimated roots in a cointegrated vector autoregressive model, J. Time Series Anal. (in press).Google Scholar
- Merton, R. C.: Continuous-Time Finance, Blackwell, Oxford, UK, 1990.Google Scholar
- Musiela, M. and Rutkowski, M.: Martingale Methods in Financial Modelling, Applications of Mathematics, Stochastic Modelling and Applied Probability, Springer, Berlin, 1997.Google Scholar