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We present a simple new methodology to allow for time-variation in volatilities using a recursive updating scheme similar to the familiar RiskMetrics approach. It exploits the link between exponentially weighted moving average and integrated dynamics of score driven time varying parameter...
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This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and...
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We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...
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