Comparison methods for fitting data using Johnson translation distributions
WSC '88 Proceedings of the 20th conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Fitting Time-Series Input Processes for Simulation
Operations Research
Least-Squares Covariance Matrix Adjustment
SIAM Journal on Matrix Analysis and Applications
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When one models dependence solely via correlations, portfolio allocation models can perform poorly. This motivates considering dependence measures other than correlation. Cointegration is one such measure that captures long-term dependence. In this paper we present a new method to simulate cointegrated sample paths using the vector auto-regressive-to-anything (VARTA) algorithm. Our approach relies on new properties of cointegrated time series of financial asset prices and allows for marginal distributions from the Johnson system. The method is illustrated on two data sets, one real and one artificial.