2024-03-29T15:51:34Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1603082018-11-07T14:50:21Zcom_10234_8643com_10234_9col_10234_8644
Repositori UJI
author
Liu, Ruipeng
author
Lux, Thomas
2016-06-03T12:17:20Z
2016-06-03T12:17:20Z
2015
LIU, Ruipeng; LUX, Thomas. Non-homogeneous volatility correlations in the bivariate multifractal model. The European Journal of Finance, 2015, vol. 21, no 12, p. 971-991.
1351-847X1466-4364
http://hdl.handle.net/10234/160308
http://dx.doi.org/10.1080/1351847X.2014.897960
In this paper, we consider an extension of the recently proposed bivariate Markov-switching multifractal model of Calvet, Fisher, and Thompson [2006. “Volatility Comovement: A Multifrequency Approach.” Journal of Econometrics 131: 179–215]. In particular, we allow correlations between volatility components to be non-homogeneous with two different parameters governing the volatility correlations at high and low frequencies. Specification tests confirm the added explanatory value of this specification. In order to explore its practical performance, we apply the model for computing value-at-risk statistics for different classes of financial assets and compare the results with the baseline, homogeneous bivariate multifractal model and the bivariate DCC-GARCH of Engle [2002. “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.” Journal of Business & Economic Statistics 20 (3): 339–350]. As it turns out, the multifractal model with heterogeneous volatility correlations provides more reliable results than both the homogeneous benchmark and the DCC-GARCH model.
eng
Long memory
Multifractal models
Simulation-based inference
Value-at-risk
C11
C13
G15
Non-homogeneous volatility correlations in the bivariate multifractal model
info:eu-repo/semantics/article
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