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Selected Articles (Chronologically Listed, More-or-Less) Diebold, F.X. and Strasser, G.H. (2007, revised 2008), "On the Correlation Structure of Microstructure Noise in Theory and Practice," Manuscript, Department of Economics, University of Pennsylvania. We use market microstructure theory to derive the cross-correlation function between
latent returns and market microstructure noise. The cross-correlation at zero
displacement is typically negative, and cross-correlations at nonzero displacements are Christensen, J.H.E., Diebold, F.X. and Rudebusch, G.D. (2008), "An Arbitrage-Free Generalized Nelson-Siegel Term Structure Model," Manuscript, FRB San Francisco and University of Pennsylvania. If Christensen-Diebold-Rudebusch (2007) is arbitrage-free Nelson-Siegel, then this paper is effectively arbitrage-free Svensson. The fourth factor in the Svensson model helps with its long-maturity fit (relative to three-factor Nelson-Siegel), but making it arbitrage-free is significantly more involved than with Nelson-Siegel. Indeed we show that there does not exist an arbitrage-free model with Svensson factor loadings. We also show, however, that a simple five-factor generalization (where the fifth factor has a natural interpretation as a second slope factor) solves the problem, achieving both freedom from arbitrage and good long-maturity fit in a Svensson-style environment. Diebold, F.X. and Yilmaz, K. (2007), "Macroeconomic Volatility and Stock Market Volatility, Worldwide," Manuscript, University of Pennsylvania and Koc University. We study a broad international cross section of stock markets, and we find a clear link between macroeconomic fundamentals and stock market volatilities, with volatile fundamentals translating into volatile stock markets. Christensen, J.H.E., Diebold, F.X. and Rudebusch, G.D. (2007), "The Affine Arbitrage-Free Class of Nelson-Siegel Term Structure Models," NBER Working Paper No. 13611. Click here for revision of May 2008. Nelson-Siegel, arbitrage-free. The best of both worlds! Details: We derive the class of arbitrage-free affine dynamic term structure models that approximate the widely-used Nelson-Siegel yield-curve specification. Our theoretical analysis relates this new class of models to the canonical representation of the three-factor arbitrage-free affine model. Our empirical analysis shows that imposing the Nelson-Siegel structure on this canonical representation greatly improves its empirical tractability; furthermore, we find that improvements in predictive performance are achieved from the imposition of absence of arbitrage. Aruoba, S.B., Diebold, F.X. and Scotti, C. (2007, revised 2008), "Real-Time Measurement of Business Conditions," Manuscript, University of Maryland, University of Pennsylvania, and Federal Reserve Board. We construct a framework for measuring high-frequency economic activity using a variety of stock and flow data observed at mixed frequencies. Specifcally, we propose a dynamic factor model that permits exact filtering, and we explore the effcacy of our methods both in an empirical example and in a simulation study. Diebold, F.X. and Yilmaz, K. (2008), "Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets," NBER Working Paper No. 13811, forthcoming in The Economic Journal. 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. Click here for weekly updates of the Diebold-Yilmaz Spillover Index, reported weekly by the Turkish Economic Research Forum. Diebold, F.X., Li, C. and Yue, V. (Forthcoming), "Global Yield Curve Dynamics and Interactions: A Generalized Nelson-Siegel Approach," Journal of Econometrics. The popular Nelson-Siegel (1987) yield curve is routinely fit to cross sections of intra-country bond yields, and Diebold and Li (2006) have recently proposed a dynamized version. In this paper we extend Diebold-Li to a global context, modeling a potentially large set of country yield curves in a framework that allows for both global and country-specific factors. In an empirical analysis of term structures of government bond yields for the Germany, Japan, the U.K. and the U.S., we find that global yield factors do indeed exist and are economically important, generally explaining significant fractions of country yield curve dynamics, with interesting differences across countries. Diebold, F.X., Kilian, L. and Nerlove, M. (Forthcoming), "Time Series Analysis," in L. Blume and S. Durlauf (eds.), The New Palgrave Dictionary of Economics, Second Edition. London: Macmillan. We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading. Andersen, T.G., Bollerslev, T. and Diebold, F.X. (Forthcoming), "Parametric and Nonparametric Volatility Measurement," in L.P. Hansen and Y. Ait-Sahalia (eds.), Handbook of Financial Econometrics. Amsterdam: North-Holland. All the technical rigor you (or at least I) could ever want... Campbell, S.D and Diebold, F.X. (Forthcoming), "Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence," Journal of Business and Economic Statistics. 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. Andersen, T.G., Bollerslev, T. and Diebold, F.X. (2007), "Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," Review of Economics and Statistics, 89, 701-720. Separating jump from non-jump movements in asset return volatility fluctuations. This is potentially of great value for volatility forecasting, because jump movements are likely very quickly mean reverting, whereas non-jump movements are not. 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) diretion-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. Andersen, T., Bollerslev, T., Diebold, F.X. and Vega, C. (2007), "Real-Time Price Discovery in Stock, Bond and Foreign Exchange Markets," Journal of International Economics, 73, 251-277. We progress relative to Andersen, Bollerslev, Diebold and Vega (2003, AER) by using a unique dataset to characterize news responses across several markets and countries. Among other things, we show that equity markets react differently to the same news depending on the state of the economy. In particular, good news has negative effects in expansions, and the traditionally-expected positive effects in recessions, which we explain by temporal variation in the competing "cash flow" and "discount rate" effects in equity valuation. We believe that our results, in conjunction with recent work by Boyd, Jagannathan and Hu, make a powerful advance toward answering Barsky's (1989, AER) key question, "Why Don't the Prices of Stocks and Bonds Move Together?": they do move together insofar as the correlation between stock and bond returns is sizeable and important, but it switches sign in expansions vs. recessions, and it therefore appears spuriously small when averaged over the business cycle. 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 Allan 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., Rudebusch, G.D. and Aruoba, B. (2006), “The Macroeconomy and the Yield Curve: A Dynamic Latent Factor Approach,” Journal of Econometrics, 131, 309-338. Do macroeconomic fundamentals help predict the yield curve? Does the yield curve help predict macroeconomic fundamentals? The answers are yes and yes, although the stronger direction of predictive causality seems to be from the macroeconomy to yields. Diebold, F.X. and Li, C. (2006), “Forecasting the Term Structure of Government Bond Yields,” Journal of Econometrics, 130, 337-364. Click here for data. The classic Nelson-Siegel curve, suitably dynamized and reinterpreted as a modern three-factor model of level, slope and curvature, forecasts bond yields surprisingly well, particularly at horizons between two and four quarters ahead. Andersen, T.G., Bollerslev, T., Diebold, F.X. and Wu, J. (2006), "Realized Beta: Persistence and Predictability," in T. Fomby and D. Terrell (eds.) Advances in Econometrics: Econometric Analysis of Economic and Financial Time Series in Honor of R.F. Engle and C.W.J. Granger , Volume B, 1-40. (Appendix here.) We move beyond analysis of more statistical objects like realized variances and covariances to a key financial economic object: systematic risk as captured by realized beta. Realized betas turn out to be noticeably more stable than the underlying realized variance and covariances, due to nonlinear fractional cointegration between the realized variance and covariances. Brandt, M.W. and Diebold, F.X. (2006), "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," Journal of Business, 79, 61-74. We generalize the Alizadeh-Brandt-Diebold (2002) range-based approach to volatility to the multivariate case by exploiting no-arb conditions. Absence of triangular arbitrage in foreign exchange, for example, implies that dollar rate covariances may be expressed in terms of dollar rate volatilities and cross rate volatility, all of which may be estimated by the range, and then plugged in. Diebold, F.X. (2006), "On Market Microstructure Noise and Realized Volatility," Journal of Business and Economic Statistics, 24,181-183. Incorporating jumps, time-varying expected returns, and intrinsic market microstructure nonlinearities when correcting realized volatility for microstructure noise. The wide-ranging predictions of economic theory for the correlation between microstructure noise and latent price. Diebold, F.X., Ji, L. and Li, C. (2006), "A Three-Factor Yield Curve Model: Non-Affine Structure, Systematic Risk Sources, and Generalized Duration," in L.R. Klein (ed.), Long-Run Growth and Short-Run Stabilization: Essays in Memory of Albert Ando. Cheltenham, U.K.: Edward Elgar, 240-274. More on the Diebold-Li approach to yield curve modeling and forecasting. The model is not affine, its risk factors appear to be priced, and bond portfolio risk management based on its generalized duration measure appears successful. Christoffersen, P.F. and Diebold, F.X. (2006), "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, 52, 1273-1287. 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), "Practical Volatility and Correlation Modeling for Financial Market Risk Management," in M. Carey and R. Stulz (eds.), Risks of Financial Institutions, University of Chicago Press for NBER, 513-548. What academics have to offer market financial institution risk management practitioners. Improvements to current industry practice that are nevertheless parsimonious and easily estimated. Practical approaches to high-dimensional covariance matrix modeling, and pitfalls to avoid... Andersen, T.G., Bollerslev, T., Diebold, F.X. and Wu, J. (2005), “A Framework for Exploring the Macroeconomic Determinants of Systematic Risk,” American Economic Review, 95, 398-404. We selectively survey, unify and extend the literature on realized volatility of financial asset returns. Rather than focusing exclusively on characterizing the properties of realized volatility, we progress by examining economically interesting functions of realized volatility, namely realized betas for equity portfolios, relating them both to their underlying realized variance and covariance parts and to underlying macroeconomic fundamentals. Diebold, F.X., Piazzesi, M. and Rudebusch, G.D. (2005), "Modeling Bond Yields in Finance and Macroeconomics," American Economic Review, 95, 415-420. New aspects of the macro/finance interface as embodied in yield curve modeling. The tension between current finance approaches that have the theoretically appealing property of freedom from arbitrage but forecast poorly, and traditional macroeconomic approaches that admit arbitrage but forecast well. A step toward resolving the tension: making Nelson-Siegel arbitrage-free. Campbell, S. and Diebold, F.X. (2005), "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, 100, 6-16. We take a simple yet sophisticated time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. In particular, we argue that the long-horizon density forecasts of crucial relevance may be produced cheaply and effectively by stochastic simulation of standard models. Seasonality in weather shock volatility dynamics turns out to play a crucial role. Diebold, F.X. (2005), “On Robust Monetary Policy with Structural Uncertainty,” in J. Faust, A. Orphanedes and D. Reifschneider (eds.), Models and Monetary Policy: Research in the Tradition of Dale Henderson, Richard Porter, and Peter Tinsley. Washington, DC: Board of Governors of the Federal Reserve System, 82-86. Pitfalls and opportunities associated with the new robust control. Local vs. global robustness, and the dangers of complacency. Diebold, F.X. (2004), "The
Nobel Prize for Robert F. Engle," Scandinavian Journal of
Economics, 106, 165-185, 2004. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2003), "Modeling and Forecasting Realized Volatility," Econometrica, 71, 529-626. Andersen, T., Bollerslev, T., Diebold, F.X. and Vega, C. (2003), "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," American Economic Review, 93, 38-62. Diebold, F.X. (2003), "'Big Data' Dynamic Factor Models for Macroeconomic Measurement and Forecasting" (Discussion of Reichlin and Watson papers), in M. Dewatripont, L.P. Hansen and S. Turnovsky (Eds.), Advances in Economics and Econometrics, Eighth World Congress of the Econometric Society. Cambridge: Cambridge University Press, 115-122. Diebold, F.X. (2003), "The ET Interview: Professor Robert F. Engle," Econometric Theory, 19, 1159-1193. Alizadeh, S., Brandt, M. and Diebold, F.X. (2002), "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, 57, 1047-1092. Bangia, A. Diebold, F.X., Kronimus, A., Schagen, C., and Schuermann, T. (2002), "Ratings Migration and the Business Cycle, with Application to Credit Portfolio Stress Testing," Journal of Banking and Finance, 26, 445- 474. Andersen, T., Bollerslev, T., Diebold, F.X. and Ebens, H. (2001), "The Distribution of Realized Stock Return Volatility," Journal of Financial Economics, 61, 43-76. (Appendix here.) Andersen, T. Bollerslev, T., Diebold, F.X. and Labys, P. (2001), "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, 96, 42-55. Bangia, A., Diebold, F.X., Schuermann, T, and Stroughair, J. (2001), "Modeling Liquidity Risk, With Implications for Traditional Market Risk Measurement and Management," in S. Figlewski and R. Levich (eds.), Risk Management: The State of the Art . Amsterdam: Kluwer Academic Publishers, 2002, 1-13. Published in abridged form as "Liquidity on the Outside," Risk, 12, 68-73, 1999. Diebold, F.X. and Kilian, L. (2001), "Measuring Predictability: Theory and Macroeconomic Applications," Journal of Applied Econometrics, 16, 657-669. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2000), "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," Multinational Finance Journal, 4, 159-179. 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. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (1999), "(Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation," Manuscript, Northwestern University, Duke University and University of Pennsylvania. Published in revised form as "Great Realizations," Risk, March 2000, 105-108. 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. Christoffersen, P., Diebold, F.X., and Schuermann, T. (1998), "Horizon Problems and Extreme Events in Financial Risk Management," Economic Policy Review, Federal Reserve Bank of New York, October, 109-118. Diebold, F.X. (1998), "The Past, Present and Future of Macroeconomic Forecasting," Journal of Economic Perspectives, 12, 175-192. Diebold, F.X., Gunther, T. and Tay, A. (1998), "Evaluating Density Forecasts, with Applications to Financial Risk Management," International Economic Review, 39, 863-883. 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., Ohanian, L. and Berkowitz, J. (1998), "Dynamic Equilibrium Economies: A Framework for Comparing Models and Data," Review of Economic Studies, 65, 433-452. Diebold, F.X., Schuermann, T. and Stroughair, J. (1998), "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," in A.-P. N. Refenes, J.D. Moody and A.N. Burgess (eds.), Advances in Computational Finance, 3-12. Amsterdam: Kluwer Academic Publishers. Reprinted in Journal of Risk Finance, 1 (Winter 2000), 30-36. Christoffersen, P. and Diebold, F.X. (1997), "Optimal Prediction Under Asymmetric Loss," Econometric Theory, 13, 808-817. Diebold, F.X. and Rudebusch, G. (1996), "Measuring Business Cycles: A Modern Perspective," Review of Economics and Statistics, 78, 67-77. DIebold, F.X. and Chen, C. (1996), "Testing Structural Stability With Endogenous Break Point: A Size Comparison of Analytic and Bootstrap Procedures,” Journal of Econometrics, 70, 221-241. 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. (1986), "Modeling the Persistence of Conditional Variances:
A comment," Econometric Reviews, 5, 51-56. |