DEPARTMENT OF ECONOMICS SEMINAR SERIES (2018-19) 

Dr. Anindya S Chakrabarti, Assistant Professor of Economics at the Indian Institute of Management in Ahmedabad, India, is going to deliver a talk on

“Ripples on Financial Networks.”

Profile

Dr. Anindya S Chakrabarti works as an Assistant Professor of Economics at IIM Ahmedabad. He works in the area of characterization and modeling of large scale economic networks, learning in multi-agent systems, and macroeconomic dynamics. After completing his MSQE from ISI Kolkata, he moved to Boston University to complete his PhD. He is a very active researcher and has published more than 20 articles in journals of repute. A few among them are Journal of Economic Behavior and Organization, Journal of Economic Interaction and Coordination, Dynamic Games and Application, Journal of Economic Dynamics and Control, Physica among others. He has refereed for Physica A, Journal of Economic Interaction & Coordination, Europhysics Letters, Economic Modeling, Palgrave Communications, European Physical Journal- Special Topics, Scientific Reports, Journal of the Royal Society Interface, Journal of Economic Dynamics and Control, European Physical Journal B, Vikalpa, Economics, Journal of Quantitative Economics, Journal of Macroeconomics, Macroeconomics and Finance in Emerging Market Economies. He has reviewed book proposals for Cambridge University Press, Oxford University Press.

Abstract

In the financial markets, asset returns exhibit collective dynamics masking individual impacts on the rest of the market. Hence, it is still an open problem to identify how shocks originating from one particular asset would create spill-over effects across other assets. The problem is more acute when there is a large number of simultaneously traded assets, making the identification of which asset affects which other assets even more difficult. Recently, Diebold and Yilmaz (2015) [Financial and Macroeconomic Connectedness, OUP] proposed a method based on vector auto-regression (VAR) methodology to model dynamic relationship between multiple return series. In this paper, we propose a many-dimensional VAR model with unique identification criteria based on network topology of the assets traded in the market. Because of the interlinkages across stocks, volatility shock to a particular node propagates through the network creating a ripple effect. Our method allows us to find the exact path the ripple effect follows on the whole network.