Bond Market Connectedness and Monetary Policy in the New Normal
We use the Diebold-Yilmaz Connectedness Index (DYCI) methodology to analyze the transmission of daily sovereign bond return shocks across countries and maturities since the global financial crisis. After declining from 2009 to mid-2012, bond return connectedness followed an upward trend from mid-2012 as the ECB gradually joined the Federal Reserve and other major central banks in the conduct of unconventional monetary policies to reignite growth. The analysis of cross-maturity connectedness shows that return shocks are transmitted from short-term to medium- and long-term maturity bonds. The analysis of cross-country connectedness, on the other hand, shows that connectedness was higher for medium-to-long-term (3 to 10 years) bonds than short-term bonds (3 to 6 months). Finally, we use panel regressions to analyze the impact of monetary policy interventions on pairwise return connectedness along with other factors, such as the distance, trade, and portfolio investment flows between pairs of countries. We find that changes in conventional (policy interest rates) and unconventional (relative size of central bank assets) monetary policy tools led to an increase in pairwise return connectedness across countries.
This study extends the Diebold-Yilmaz Connectedness Index (DYCI) methodology and, based on forecast error covariance decompositions, derives a network risk model for a portfolio of assets. As a normalized measure of the sum of variance contributions, system-wide connectedness averages out the information embedded in the covariance matrix in aggregating pairwise directional measures. This actually does matter, especially when there are large differences in asset variances. As a first step towards deriving the network risk model, the portfolio covariance matrix is decomposed to obtain the network-driven component of the portfolio variance using covariance decompositions. A second step shows that a common factor model can be estimated to obtain both the variance and covariance decompositions. In a third step, using quantile regressions, the proposed network risk model is estimated for different shock sizes. It is shown, in contrast to the DYCI model, the dynamic quantile estimation of the network risk model can differentiate even small shocks at both tails. This result is obtained because the network risk model makes full use of information embedded in the covariance matrix. Estimation results show that in two recent episodes of financial market turmoil, the proposed network risk model captures the responses to systemic events better than the system-wide index. (Latest version: Link)
This paper investigates the state-level differences in government and community responses to the Covid-19 pandemic, leading to different growth trajectories of Covid-19 cases and their connectedness across the U.S. states. Our regression analysis shows that higher growth trajectories are observed in the states that implemented the lax government and community response to the pandemic. Moving to the analysis of spillovers/connectedness of Covid-19 cases across the states, we apply the Diebold-Yilmaz connectedness methodology to the growth rates of Covid-19 cases. Using the total directional connectedness measures, we find that the states with lax government and community response generated connectedness of Covid-19 cases to others. These findings are also supported by the secondary regression analysis of pairwise connectedness measures over time. Finally, the travel intensity between the pairs of states, indirectly measured by the data on smartphone location exposure index, contributes significantly to the pairwise directional connectedness of Covid-19 across the states. (link) (VoxEU.org Article)
Measuring Connectedness of Turkish Banks Through Local & Global Crises
This paper analyzes how the stock return and volatility shocks spread within the Turkish banking sector. In particular, it presents dynamic stock return and volatility connectedness measures for Turkish banks over the two-and-a-half decades from 1993 to 2020. The results show that, for the most part, return connectedness originated from big banks towards their smaller counterparts. Unlike return connectedness, it is not possible to discern a dominant pattern in directional volatility connectedness. While big banks still generate strong volatility connectedness towards others, smaller and yet more vulnerable banks, at times, generated sizeable directional volatility connectedness to others. Some of the banks that went bankrupt or were taken to the government conservatorship in the late 1990s and early 2000s generated significant volatility connectedness before their demise, showing that under stress, even small/medium-sized banks can generate substantial financial systemic risk. After the 2001 economic crisis, the return and notably the volatility connectedness of the banking sector stocks experienced jumps due to domestic and international shocks. Among these, we can count the changes in the U.S. central bank monetary policy stance (May 2006 and June 2013), the international financial crises (2008-09 global financial crisis, 2011 Eurozone debt crisis), increased domestic political tensions (corruption investigations of December 2013 and coup attempt of July 2016), domestic financial shocks (August-September 2018 currency crisis) and the COVID-19 shock of 2020.