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Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation

Yongdeng Xu

Journal of Time Series Econometrics2024https://doi.org/10.1515/jtse-2022-0018article
AJG 2ABDC B
Weight
0.51

What the paper says

Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.

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https://doi.org/https://doi.org/10.1515/jtse-2022-0018

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@article{yongdeng2024,
  title        = {{Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation}},
  author       = {Yongdeng Xu},
  journal      = {Journal of Time Series Econometrics},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1515/jtse-2022-0018},
}

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Evidence weight

0.51

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.51 × 0.4 = 0.20
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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