**Diebold, F. X. and Yilmaz, K. (2015)**,*Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring*, Oxford University Press, April 2015.**Bilgin, N. M. and Yilmaz, K.(2018)**, " Producer Price Inflation Connectedness and Input-Output Networks", Koc University-TUSIAD Economic Research Forum, Working Paper No. 1813, August.**Uluceviz, E. and Yilmaz, K.(2018)**, "Measuring Real–Financial Connectedness in the U.S. Economy", Koc University-TUSIAD Economic Research Forum, Working Paper No. 1812, August.**Demirer, M., Diebold, F. X., Liu, L. and Yilmaz, K. (2018)**,"Estimating Global Bank Network Connectedness,*Journal of Applied Econometrics, 2018, 33(1):1-15.***Demirer, M., Gokcen U. and Yilmaz, K.(2018)**, "Financial Sector Volatility Connectedness and Equity Returns", Koc University-TUSIAD Economic Research Forum, Working Paper No. 1803, January.**Korobilis, D. and Yilmaz, K.(2018)**, "Measuring Dynamic Connectedness with Large Bayesian VAR Models", Koc University-TUSIAD Economic Research Forum, Working Paper No. 1802, January.**Cotter, J., Hallam, M. and Yilmaz, K. (2017)**, "Mixed-Frequency Macro-Financial Spillovers", Koc University-TUSIAD Economic Research Forum, Working Paper No. 1704,**Diebold, F.X. and Yilmaz, K. (2016)**, "Trans-Atlantic Equity Volatility Connectedness: U.S. and European Financial Institutions, 2004-2014",*Journal of Financial Econometrics,*14(1): 81-127**Bostanci, G., and Yilmaz, K. (2015)**,"How Connected is the Global Sovereign Credit Risk Network?,*Koç University-TUSIAD Economic Research Forum, Working Paper No: 1515, August.***Diebold, F. X. and Yilmaz, K. (2015)**,"Measuring the Dynamics of Global Business Cycle Connectedness," in S.J. Koopman and N. Shephard (eds.),*Unobserved Components and Time Series Econometrics*, Oxford University Press, 45-70, December.**Diebold, F.X. and Yilmaz, K. (2014)**, "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms"*Journal of Econometrics*, 182(1), 119-134.**Diebold, F.X. and Yilmaz, K. (2012)**, "Better to Give than to Receive: Forecast-Based Measurement of Volatility Spillovers"*International Journal of Forecasting*, 28(1), 57-66**Diebold, F.X. and Yilmaz, K. (2011)**, "Equity Market Spillovers in the Americas," in R. Alfaro (ed.)**Financial Stability, Monetary Policy, and Central Banking**. Santiago: Bank of Chile Central Banking Series, Volume 15, 199-214, 2011. (Published in Spanish as: "Efectos Errame en Los Mercados de Valores del Continente Americano,"*Revista Economía Chilena*, 12, 55-65, 2009.)**Yilmaz, K. (2010)**, "Return and Volatility Spillovers among the East Asian Equity Markets"*Journal of Asian Economics*, 21(3), 304-313.**Diebold, F.X. and Yilmaz, K. (2009)**, "Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets,"*Economic Journal*, 119, 158-171.

We analyze the transmission of producer price inflation shocks across the U.S. manufacturing industries from 1947 to 2018 using the Diebold-Yilmaz Connectedness Index framework, which fully utilizes the information in generalized variance decompositions from vector autoregressions. The results show that the system-wide connectedness of the input-output network Granger-causes the producer price inflation connectedness across industries. The input-output network and the inflation connectedness nexus is stronger during periods of major supply-side shocks, such as the global oil and metal price hikes, and weaker during periods of aggregate demand shocks, such as the Volcker disinflation of 1981-84 and the Great Recession of 2008. These findings are consistent with Acemoglu et al. (2016)’s conjecture that supply shocks are transmitted downstream, whereas demand shocks are transmitted upstream. Finally, preliminary results show that Trump tariffs caused an increase in the system-wide inflation connectedness in the first half of 2018, due to shocks mostly transmitted from tariff-targeted industries, namely, basic metals, fabricated metals and machinery.

We analyze connectedness between the real and financial sectors of the U.S. economy. Using the weekly ADS index of the Philadelphia FED (the widely used business conditions indicator) to represent the real side, we find that during times of financial distress and/or business cycle turning points the direction of connectedness runs from the real sector to financial markets. The ADS index is derived from a model containing a measure of term structure along with real variables, therefore, it might not be the best representative of the real activity to be used in the connectedness analysis. As an alternative, we derive a real activity index (RAI) from a dynamic factor model of the real sector variables only. The behavior of RAI over time is quite similar to that of the ADS index. When we include RAI to represent the real side of the economy in the connectedness analysis, the direction of net connectedness reverses: financial markets generate positive net connectedness to the real side of the economy.

We use LASSO methods to shrink, select, and estimate the high-dimensional network linking the publicly traded subset of the world's top 150 banks, 2003–2014. We characterize static network connectedness using full sample estimation and dynamic network connectedness using rolling window estimation. Statically, we find that global bank equity connectedness has a strong geographic component, whereas country sovereign bond connectedness does not. Dynamically, we find that equity connectedness increases during crises, with clear peaks during the Great Financial Crisis and each wave of the subsequent European Debt Crisis, and with movements coming mostly from changes in cross-country as opposed to within-country bank linkages.

Abstract: We apply the Diebold and Yilmaz (2014) methodology to daily stock prices of the largest 40 U.S. financial institutions to construct a volatility connectedness index. We then estimate the contemporaneous return sensitivity of every non-financial U.S. company to this index. We find that there is a large statistically significant difference between the returns of firms with positive and negative exposures to financial connectedness. The four-factor alpha of a strategy that goes long in the bottom decile and short in the top decile of stocks sorted on their connectedness betas is roughly 15% per annum. Bivariate portfolio tests reveal that abnormal returns are robust to market beta, size, book-to-market ratio, momentum, debt, illiquidity, and idiosyncratic volatility. Abnormal returns are asymmetric; they are primarily driven by firms whose returns covary negatively with the index. These firms tend to be young and small, with poor past performance and low credit quality.

We estimate a large Bayesian time-varying parameter vector autoregressive (TVP-VAR) model of daily stock return volatilities for 35 U.S. and European financial institutions. Based on that model we extract a connectedness index in the spirit of Diebold and Yilmaz (2014) (DYCI). We show that the connectedness index from the TVP-VAR model captures abrupt turning points better than the one obtained from rolling-windows VAR estimates. As the TVP-VAR based DYCI shows more pronounced jumps during important crisis moments, it captures the intensification of tensions in financial markets more accurately and timely than the rolling-windows based DYCI. Finally, we show that the TVPVAR-based index performs better in forecasting systemic events in the American and European financial sectors as well.

We develop a new methodology to analyse spillovers between the real and financial sides of the economy that employs a mixed-frequency modelling approach. This enables high-frequency financial and low-frequency macroeconomic data series to be employed directly, avoiding the data aggregation and information loss incurred when using common-frequency methods. In a detailed analysis of macro-financial spillovers for the US economy, we find that the additional high-frequency information preserved by our mixed-frequency approach results in estimated spillovers that are typically substantially higher than those from an analogous common-frequency approach and are more consistent with known in-sample events. We also show that financial markets are typically net transmitters of shocks to the real side of the economy, particularly during turbulent market conditions, but that the bond and equity markets act heterogeneously in both transmitting and receiving shocks to the non-financial sector. We observe substantial short and medium-run variation in macro-financial spillovers that is statistically associated with key variables related to financial and macroeconomic fundamentals; the values of the term spread, VIX and unemployment rate in particular appear to be important determinants of macrofinancial spillovers.

We characterize equity return volatility connectedness in the network of major American and European financial institutions, 2004-2014. Our methods enable precise characterization of the timing and evolution of key aspects of the financial crisis. First, we find that during 2007- 2008 the direction of connectedness was clearly from the US to Europe, but that connectedness became bi-directional starting in late 2008. Second, we find an unprecedented surge in directional connectedness from European to US financial institutions in June 2011, consistent with massive deterioration in the health of EU financial institutions. Third, we identify particular institutions that played disproportionately important roles in generating connectedness during the US and the European crises.

We apply the Diebold-Yilmaz connectedness index methodology on sovereign credit default swaps (SCDSs) to estimate the network structure of global sovereign credit risk. In particular, using the elastic net estimation method, we separately estimate networks of daily SCDS returns and volatilities for 38 countries between 2009 and 2014. Our results reveal striking differences between the network structures of returns and volatilities. In SCDS return networks, developing and developed countries stand apart in two big clusters. In the case of the SCDS volatility networks, however, we observe regional clusters among emerging market countries along with the developed-country cluster. We also show that global factors are more important than domestic factors in the determination of SCDS returns and volatilities. Finally, emerging market countries are the key generators of connectedness of sovereign credit risk shocks while severely problematic countries as well as developed countries play relatively smaller roles.

Using a connectedness-measurement technology fundamentally grounded in modern network theory, we measure real output connectedness for a set of six developed countries, 1962-2010. We show that global connectedness is sizable and varies over the business cycle, and we study the nature of the variation relative to ongoing discussion of the changing nature of the global business cycle. We also show that connectedness corresponding to transmissions to others from the United States and Japan is disproportionately important.

We propose several connectedness measures built from pieces of variance decompositions, and we argue that they provide natural and insightful measures of connectedness among financial asset returns and volatilities. We also show that variance decompositions define weighted, directed networks, so that our connectedness measures are intimately-related to key measures of connectedness used in the network literature. Building on these insights, we track both average and daily time-varying connectedness of major U.S. financial institutions' stock return volatilities in recent years, including during the financial crisis of 2007-2008.

Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to variable ordering, we propose measures of both total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across U.S. stock, bond, foreign exchange and commodities markets, from January 1999 through September 2009. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis that began in 2007. As the crisis intensified so too did the volatility spillovers, with particularly important spillovers from the bond market to other markets taking place after the collapse of Lehman Brothers in September 2008.

We provide an empirical analysis of return and volatility spillovers among five equity markets in the Americas: Argentina, Brazil, Chile, Mexico and the U.S. The results indicate that both return and volatility spillovers vary widely. Return spillovers, however, tend to evolve gradually, whereas volatility spillovers display clear bursts that often correspond closely to economic events.

This article examines the extent of contagion and interdependence across the East Asian equity markets since early 1990s and compares the ongoing crisis with earlier episodes. Using the forecast error variance decomposition from a vector autoregression, we derive return and volatility spillover indices over the rolling sub-sample windows. We show that there is substantial difference between the behavior of the East Asian return and volatility spillover indices over time. While the return spillover index reveals increased integration among the East Asian equity markets, the volatility spillover index experiences significant bursts during major market crises, including the East Asian crisis. The fact that both return and volatility spillover indices reached their respective peaks during the current global financial crisis attests to the severity of the current episode.

We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of return spillovers and volatility spillovers. Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers; both turn out to be empirically important. In particular, in an analysis of 19 global equity markets from the early 1990s to the present, we find striking evidence of divergent behaviour in the dynamics of return spillovers vs. volatility spillovers: return spillovers display a gently increasing trend but no bursts, whereas volatility spillovers display no trend but clear bursts.