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We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...
Persistent link: https://www.econbiz.de/10008797745
-d.In this work we consider two identification procedures: the first one follows the classical estimation for SETAR models, the …
Persistent link: https://www.econbiz.de/10013111893
estimation properties of the method and test its predictive power on S&P 500 option data, comparing it as well with other recent …
Persistent link: https://www.econbiz.de/10013108080
Persistent link: https://www.econbiz.de/10015073818
In this article, we derive a set of necessary and sufficient conditions for positivity of the vector conditional variance equation in multivariate GARCH models with explicit modelling of conditional correlation. These models include the constant conditional correlation GARCH model of Bollerslev...
Persistent link: https://www.econbiz.de/10003576679
This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for...
Persistent link: https://www.econbiz.de/10014107616
One of the most important factors to control for the achievements of investment portfolio returns is risk. If we only think that a 100% positive return is needed to recover a portfolio loss of 50%, we can understand why. With the advent of the exponential growth of technology usage in markets,...
Persistent link: https://www.econbiz.de/10014254526
Persistent link: https://www.econbiz.de/10012697180
The paper examines the performance of four multivariate volatility models, namely CCC, VARMA-GARCH, DCC and BEKK, for the crude oil spot and futures returns of two major benchmark international crude oil markets, Brent and WTI, to calculate optimal portfolio weights and optimal hedge ratios, and...
Persistent link: https://www.econbiz.de/10013149486
In this paper we examine feed-forward neural networks using genetic algorithms in the training process instead of error backpropagation algorithm. Additionally real encoding is preferred to binary encoding as it is more appropriate to find the optimum weights. We use learning and momentum rates...
Persistent link: https://www.econbiz.de/10013138757