General Financial Market Measurement, Modeling and Forecasting

Diebold, F.X. and Yilmaz, K. (2009), "Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers," Manuscript, University of Pennsylvania.

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 October 2008. We show that despite significant volatility fluctuations in all 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 volatility spillovers, with particularly important spillovers from the bond market to other markets.

Diebold, F.X. and Yilmaz, K. (2010), "Equity Market Spillovers in the Americas," in R. Alfaro and D. Gray (eds.) Financial Satbility, Monetary Policy, and Central Banking. Santiago: Bank of Chile, in press.

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.

(Published in Spanish as: Diebold, F.X. and Yilmaz, K. (2009), "Efectos Errame en Los Mercados de Valores del Continente Americano," Revista Economía Chilena, 12, 55-65.)

Diebold, F.X. and Yilmaz, K. (2009), "Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets," The Economic Journal, 119, 158-171.

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, and both turn out to be empirically important. In particular, in an analysis of nineteen global equity markets from the early 1990s to the present, we find striking evidence of divergent behavior 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.

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Christoffersen, P.F., Diebold, F.X., Mariano, R.S., Tay, A.S. and Tse, Y.K. (2007), "Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics: International Evidence," Journal of Financial Forecasting, 1(2), 3-24.

We generalize the Christoffersen-Diebold (2006) direction-of-change forecasting framework to incorporate conditional moments beyond the variance, and we examine its performance in the context of Asian equity markets. The results are mixed but encouraging.

Campbell, S.D. and Diebold, F.X. (2009), "Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence," Journal of Business and Economic Statistics, 27, 266-278.

Using half a century of Livingston expected business conditions data, we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwise-standard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R squared. Interestingly, one important and recently introduced non-financial predictor, the generalized consumption/wealth ratio ("CAY"), retains its predictive power even when controlling for expected business conditions, which accords with the view that the consumption/wealth ratio plays a role in asset pricing different from and complementary to that of expected business conditions. We argue that time-varying expected business conditions likely captures time-varying risk, while time-varying consumption/wealth captures time-varying risk aversion.

Christoffersen, P.F. and Diebold, F.X. (2006), "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, 52, 1273-1288.

We consider three sets of phenomena that feature prominently in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail.

Andersen, T.G., Bollerslev, T., Christoffersen, P.F., and Diebold, F.X. (2006), "Volatility and Correlation Forecasting," in G. Elliott, C.W.J. Granger, and A. Timmermann (eds.), Handbook of Economic Forecasting. Amsterdam: North-Holland, 778-878.

We survey the most important theoretical developments and empirical insights to emerge from the burgeoning volatility and correlation literature, with a focus on forecasting applications in financial risk management, asset management, and asset pricing.

Diebold, F.X. and Kilian, L. (2001), "Measuring Predictability: Theory and Macroeconomic Applications," Journal of Applied Econometrics, 16, 657-669.

Diebold, F.X. and Kilian, L. (2000), "Unit Root Tests are Useful for Selecting Forecasting Models," Journal of Business and Economic Statistics, 18, 265-273.

Diebold, F.X., Hahn, J. and Tay, A. (1999), "Multivariate Density Forecast Evaluation and Calibration in Financial Risk Management: High-Frequency Returns on Foreign Exchange," Review of Economics and Statistics, 81, 661-673.

Diebold, F.X., Tay, A. and Wallis, K. (1999), "Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters," in R. Engle and H. White (eds.), Festschrift in Honor of C.W.J. Granger, 76-90. Oxford: Oxford University Press.

Christoffersen, P. and Diebold, F.X. (1998), "Cointegration and Long-Horizon Forecasting," Journal of Business and Economic Statistics, 16, 450-458.

Diebold, F.X. Hickman, A., Inoue, A. and Schuermann, T. (1998), "Converting 1-Day Volatility to h-Day Volatility: Scaling by Root-h is Worse than You Think," Wharton Financial Institutions Center, Working Paper 97-34.

Published in condensed form as "Scale Models," Risk, 11, 104-107, 1998.

Diebold, F.X., Gunther, T. and Tay, A. (1998), "Evaluating Density Forecasts, with Applications to Financial Risk Management," International Economic Review, 39, 863-883.

Christoffersen, P. and Diebold, F.X. (1997), "Optimal Prediction Under Asymmetric Loss," Econometric Theory, 13, 808-817.

Diebold, F.X. and Mariano, R. (1995), “Comparing Predictive Accuracy,” Journal of Business and Economic Statistics, 13, 253-265.

Diebold, F.X., Lee, J.-H. and Weinbach, G. (1994), "Regime Switching with Time-Varying Transition Probabilities,” in C. Hargreaves (ed.), Nonstationary Time Series Analysis and Cointegration. (Advanced Texts in Econometrics, C.W.J. Granger and G. Mizon, eds.), 283-302. Oxford: Oxford University Press.

Diebold, F.X. (1989), "Random Walks vs. Fractional Integration: Power Comparisons of Scalar and Joint Tests of the Variance-Time Function,” in Baldev Raj (ed.), Advances in Econometrics and Modeling, 29-45. Advanced Studies in Theoretical and Applied Econometrics, Volume 15. Boston: Kluwer Academic Publishers.