The two-component Beta-t-QVAR-M-lev: a new forecasting model

Michel Ferreira Cardia Haddad (), Szabolcs Blazsek, Philip Arestis, Franz Fuerst and Hsia Hua Sheng
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Michel Ferreira Cardia Haddad: Queen Mary University of London
Hsia Hua Sheng: Fundação Getulio Vargas

Financial Markets and Portfolio Management, 2023, vol. 37, issue 4, No 2, 379-401

Abstract: Abstract We introduce a new joint model of expected return and volatility forecasting, namely the two-component Beta-t-QVAR-M-lev (quasi-vector autoregression in-mean with leverage). The maximum likelihood estimator for the two-component Beta-t-QVAR-M-lev is an extension of theoretical results of the one-component Beta-t-QVAR-M. We compare the volatility forecasting performance of the two-component Beta-t-QVAR-M-lev and two-component GARCH-M (generalized autoregressive conditional heteroscedasticity), also considering their one-component frameworks. The results for G20 stock market indices indicate that the forecasting performance of the two-component Beta-t-QVAR-M-lev is superior compared with the two-component GARCH-M and their one-component versions.

Keywords: Dynamic conditional score (DCS); Generalized autoregressive score (GAS); Dynamic volatility models; Volatility forecasting; G20 (search for similar items in EconPapers)
JEL-codes: C32 C52 C58 F21 G15 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11408-023-00431-4

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