Bayesian nonparametric sparse VAR models

Abstract

High dimensional vector autoregressive (VAR) models require a large number of parameters to be estimated and may suffer of inferential problems. We propose a new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional VAR models that can improve estimation efficiency and prediction accuracy. Our hierarchical prior overcomes overparametrization and overfitting issues by clustering the VAR coefficients into groups and by shrinking the coefficients of each group toward a common location. Clustering and shrinking effects induced by the BNP-Lasso prior are well suited for the extraction of causal networks from time series, since they account for some stylized facts in realworld networks, which are sparsity, communities structures and heterogeneity in the edges intensity. In order to fully capture the richness of the data and to achieve a better understanding of financial and macroeconomic risk, it is therefore crucial that the model used to extract network accounts for these stylized facts.

A machine learning forecasting model for COVID-19 pandemic in India

“…Various information criteria that help in optimize autoregressive order are Akaike's information criterion (AIC), Bayesian information criterion (BIC), Hann-Quinn and Final prediction error (FPE). By adding and varying trends from constant, linear, and quadratic with forecast steps ahead and confidence intervals (Billio et al 2019;Portet 2020;Zhang and Krieger 1993). The formula for calculating AIC, BIC and HQ is as follows:…”

Interconnected Deviations from Covered Interest Parity

“…More importantly, modeling the network underlying the comovement in CCBS helps us to identify which basis is central in the widening club as well as the additional profit/loss that can be made from hedging against an adverse movement in the forex market using CCBS. Secondly, we contribute to the literature on financial networks via VAR approximated models to study interconnectedness within and across hybrids of asset classes in financial markets (see Ahelegbey et al, 2016a;Barigozzi and Hallin, 2017;Billio et al, 2019Billio et al, , 2012Diebold and Yilmaz, 2014). Thirdly, answering our research question (RQ-1) contributes to a common debate central to financial network studies on whether a densely interconnected market reduces or amplify financial risks caused by shock events.…”

Modeling Turning Points In Global Equity Market

“…A clear understanding of the nature of the linkages and interconnectedness among markets is critical to understand potential contagion. Modeling financial interconnectedness have received much attention, especially after the the global financial crisis of 2007-2009, and the European sovereign debt crisis of 2010-2013 (see Ahelegbey et al, 2016b;Battiston et al, 2012;Billio et al, 2019Billio et al, , 2012DasGupta and Kaligounder, 2014;Diebold and Yilmaz, 2014;Hautsch et al, 2015).…”

“…The VAR-RSEM specification is designed to account for the contemporaneous, serial, and cross-lagged dependencies beyond what simple stylized facts from historical data can provide. Closely related models have in recent times been applied to infer financial contagion networks (see Ahelegbey et al, 2016a,b;Barigozzi and Brownlees, 2019;Basu and Michailidis, 2015;Bianchi et al, 2019;Billio et al, 2019Billio et al, , 2012Diebold and Yilmaz, 2014). In a typical moderate to large VAR model, there are often too many parameters to estimate, compared to the available observations.…”