Modeling Turning Points In Global Equity Market

Abstract

Turning points in financial markets are often characterized by changes in the direction and/or magnitude of market movements with short-to-long term impacts on investors' decisions. This paper develops a Bayesian technique to turning point detection in financial equity markets. We derive the interconnectedness among stock market returns from a piece-wise network vector autoregressive model. The empirical application examines turning points in global equity market over the past two decades. We also compare the COVID-19 induced interconnectedness with that of the global financial crisis in 2008 to identify similarities and the most central market for spillover propagation.

Keywords: Bayesian inference, Dynamic Programming, Financial Crisis, Turning points, Networks, VAR

JEL Classification: C11, C15, C51, C52, C55, C58, G01