Scalable Regularized Vector Multiplicative Error Models for Positive-valued Financial Time Series

Key points

• This paper describes regularized estimation via hierarchical lag structures (Nicholson et al., 2020) for log-vMEM models with multivariate gamma error distribution of Tsionas (2004).1
• The parameter estimation is performed using a blockwise coordinate descent algorithm with a Gauss-Seidel-style update scheme (Wright, 2015).1
• The competing models are juxtaposed against each other by combining three hierarchical lag structures (componentwise, elementwise, own-other) and four penalties(group-lasso, adaptive group-lasso, group-mcp, and group-scad).1
• We apply the proposed methods to model the joint dynamics of robust intraday realized volatility measures for Microsoft (NASDAQ: MSFT) for the competing models.1

Sources (1)

• [1]Scalable Regularized Vector Multiplicative Error Models for Positive-valued Financial Time Series
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 02:35 PM

  The logarithmic multiplicative error model (log-vMEM) has been useful in modeling and forecasting multivariate positive-valued financial time series.

  This paper describes regularized estimation via hierarchical lag structures (Nicholson et al., 2020) for log-vMEM models with multivariate gamma error distribution of Tsionas (2004).

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