Session 5: Financial Econometrics

Chair:  Tamas Kiss

Luc Bauwens: We modeled long memory with just one lag!

Abstract: A large dimensional network or system can generate long memory in its components, as shown by Chevillon, Hecq and Laurent (2018, CHL) and Schennach (2018). These authors derive conditions under which the variables generated by an infinite dimensional vector autoregressive model of order 1, a VAR(1), exhibit long memory. We go one step further and show how these asymptotic results can be put to practice for finite sample modeling and inference regarding series with long range dependence that belong a network or a large system. We propose to use a VAR(1), or an AR(1)-X when the VAR(1) model is estimated equation by equation, whose parameters we shrink to generic conditions matching those of CHL and Schennach. Our proposal significantly outperforms ARFIMA and HAR models when forecasting a non-parametric estimate of the log of the integrated variance (i.e., log(MedRV)) of 250 assets, the annual productivity growth recorded in 100 industrial sectors in the U.S., as well as seasonally adjusted historic monthly streamflow series recorded in 97 localizations of the Columbia river basin.

Timo Teräsvirta: Four Australian Banks and the Multivariate Time-Varying Smooth Transition Correlation GARCH model

Abstract: This paper looks at changes in the correlations of daily returns between the four major banks in Australia. Revelations from the analysis are of importance to investors, but also to government involvement, due to the large proportion of the highly concentrated financial sector relying on the stability of the Big Four. For this purpose, a methodology for building Multivariate Time-Varying STCC–GARCH models is developed. The novel contributions in this area are the specification tests related to the correlation component, the extension of the general model to allow for additional correlation regimes, and a detailed exposition of the systematic, improved modelling cycle required for such nonlinear models. There is an R-package that includes the steps in the modelling cycle. Simulations evidence the robustness of the recommended model building approach. The empirical analysis reveals an increase in correlations of the Australia’s four largest banks that coincides with the stagnation of the home loan market, technology changes, the mining boom, and Basel II alignment, increasing the exposure of the Australian financial sector to shocks.