We propose several connectedness measures built from pieces of variance decompositions, and we argue that they provide natural and insightful measures of connectedness. 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 daily time-varying connectedness of major US financial institutions’ stock return volatilities in recent years, with emphasis on the financial crisis of 2007–2008.
The Diebold-Mariano (
) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The
test was not intended for comparing models. Much of the large ensuing ...literature, however, uses
-type tests for comparing models, in pseudo-out-of-sample environments. In that case, simpler yet more compelling full-sample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch that pseudo-out-of-sample analysis is somehow the "only," or "best," or even necessarily a "good" way to provide insurance against in-sample overfitting in model comparisons proves largely false. On the other hand, pseudo-out-of-sample analysis remains useful for certain tasks, perhaps most notably for providing information about comparative predictive performance during particular historical episodes.
Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering, we propose measures of both the total and directional ...volatility spillovers. We use our methods to characterize daily volatility spillovers across US stock, bond, foreign exchange and commodities markets, from January 1999 to January 2010. 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, which began in 2007. As the crisis intensified, so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets taking place after the collapse of the Lehman Brothers in September 2008.
Estimating global bank network connectedness Demirer, Mert; Diebold, Francis X.; Liu, Laura ...
Journal of applied econometrics (Chichester, England),
January/February 2018, Letnik:
33, Številka:
1
Journal Article
Recenzirano
Odprti dostop
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.
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.
Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to the selection of the set of forecasts to ...combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality (“partially-egalitarian LASSO”). Ex post analysis reveals that the optimal solution has a very simple form: the vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures that are motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require the choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts; indeed, they perform approximately as well as the ex post best forecaster.
Yield curve modeling and forecasting Diebold, Francis X; Diebold, Francis X; Rudebusch, Glenn D
2013., 20130115, 2013, 2012-12-26, 20130101
eBook
Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, ...structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed.
Based on the Econometric and Tinbergen Institutes Lectures,Yield Curve Modeling and Forecastingcontains essential tools with enhanced utility for academics, central banks, governments, and industry.
Despite powerful advances in yield curve modeling in the last 20 years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do ...so. We use neither the no-arbitrage approach nor the equilibrium approach. Instead, we use variations on the Nelson–Siegel exponential components framework to model the entire yield curve, period-by-period, as a three-dimensional parameter evolving dynamically. We show that the three time-varying parameters may be interpreted as factors corresponding to level, slope and curvature, and that they may be estimated with high efficiency. We propose and estimate autoregressive models for the factors, and we show that our models are consistent with a variety of stylized facts regarding the yield curve. We use our models to produce term-structure forecasts at both short and long horizons, with encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts.
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 United States to Europe, but that connectedness became bidirectional starting in late 2008. Second, we find an unprecedented surge in directional connectedness from European to U.S. 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 U.S. and the European crises.
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