Papers Diebold, F.X., Schorfheide, F. and Shin, M. (2015), "RealTime Forecast Evaluation of DSGE Models with Stochastic Volatility," Manuscript, University of Pennsylvania.Demirer, M., Diebold, F.X., Liu, L. and Yilmaz, K. (2015), "Estimating Global Bank Network Connectedness," Manuscript, MIT, University of Pennsylvania, and Koc University. We characterize equity return volatility connectedness in the network of major American
and European financial institutions, 20042014. 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 bidirectional 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. 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: Essays in Honor of Andrew C. Harvey, Oxford University Press, forthcoming. Aruoba, S.B., Diebold, F.X., Nalewaik, J. Schorfheide, F. and Song, D. (2015), "Improving GDP Measurement: A MeasurementError Perspective," Journal of Econometrics, Annals Issue on Innovations in Econometric and Economic Measurement, forthcoming. We provide a new measure of U.S. GDP growth, obtained by applying optimal signalextraction techniques to the noisy expenditureside and incomeside GDP estimates. The quarterbyquarter values of our new measure often differ noticeably from those of the traditional measures. Its dynamic properties differ as well, indicating that the persistence of aggregate output dynamics is stronger than previously thought. Diebold, F.X. and Shin, M. (2015), "Assessing Point Forecast Accuracy by Stochastic Loss Distance," Economics Letters, 128, 3738. Diebold, F.X. (2015), "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of DieboldMariano Tests," (with discussion) Journal of Business and Economic Statistics, 33, 124. The DieboldMariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Unfortunately, however, much of the large subsequent literature uses DMtype tests for comparing models, in (pseudo) outofsample environments. In that case, much simpler yet more compelling fullsample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch that (pseudo) outofsample analysis is somehow the "only," or "best," or even a "good" way to provide insurance against insample overfitting in model comparisons proves largely false. On the other hand, (pseudo) outofsample analysis may be useful for learning about comparative historical predictive performance. Diebold, F.X. and Yilmaz, K. (2014), "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," Journal of Econometrics, 182, 119134. 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 intimatelyrelated to key measures of connectedness used in the network literature. Building on these insights, we track both average and daily timevarying connectedness of major U.S. financial institutions' stock return volatilities in recent years, including during the financial crisis of 20072008. Chen, F., Diebold, F.X. and Schorfheide, F. (2013), "A MarkovSwitching MultiFractal InterTrade Duration Model, with Application to U.S. Equities," Journal of Econometrics, 177, 320342. We propose and illustrate a Markovswitching multifractal duration (MSMD) model for analysis of intertrade durations in financial markets. MSMD is a parameterdriven longmemory model of conditional intensity dynamics, with long memory driven by structural Markovswitching components. The popular standard ACD duration model neglects all of those features. A few other notable duration models have featured them in isolation or in smaller assemblies, but none have featured them all. MSMD does so in a simple and parsimonious fashion, successfully capturing the key features of financial market intertrade durations: longmemory dynamics and overdispersed distributions. Empirical exploration suggests MSMD's superiority relative to the leading competitor. Diebold, F.X. and Strasser, G.H. (2013), "On the Correlation Structure of Microstructure Noise: A Financial Economic Approach," Review of Economic Studies, 80, 13041337. We bring financial economics to bear on the financial econometrics of volatility estimation in the presence of market microstructure noise, using microstructure theory to derive the crosscorrelation function between latent returns and market microstructure noise. The crosscorrelation at zero displacement is typically negative, and crosscorrelations at nonzero displacements are positive and decay geometrically. When market makers are very risk averse, the crosscorrelation pattern is inverted. The results may be useful for choosing among different market microstructure models and estimation of noiserobust measures of realized volatility. Andersen, T.G., Bollerslev, T., Christoffersen, P.F. and Diebold, F.X. (2013), "Financial Risk Measurement for Financial Risk Management," in G. Constantinedes, M. Harris and Rene Stulz (eds.), Handbook of the Economics of Finance, Volume 2, Part B, Elsevier, 11271220. We stress a conditional approach at both the portfolio and individualasset levels, at both high frequencies and business cycle frequencies, with special attention to dimensionalityreduction and regularization methods for "vast" covariance matrices. Aruoba, S.B., Diebold, F.X., Nalewaik, J. Schorfheide, F. and Song, D. (2012), "Improving GDP Measurement: A Forecast Combination Perspective," in X. Chen and N. Swanson (eds.), Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions, Essays in Honor of Halbert L. White Jr., 126. Two oftendivergent U.S. GDP estimates are available, a widelyused expenditure side version GDPE, and a much less widelyused incomeside version GDPI . We propose and explore a "forecast combination" approach to combining them. We then put the theory to work, producing a superior combined estimate of GDP growth for the U.S., GDPC. We compare GDPC to GDPE and GDPI, with particular attention to behavior over the business cycle. We discuss several variations and extensions. The bottom line: The U.S. should produce a similarlycombined headline GDP estimate, potentially using the methods introduced in this paper. Diebold, F.X. (2012), "100+ Years of Financial Risk Measurement and Management," in F.X. Diebold (ed.), Financial Risk Measurement and Management (ed.). Cheltenham, U.K. and Northampton, Mass.: Edward Elgar Publishing Ltd. (International Library of Critical Writings in Economics). I selectively survey several key strands of literature on financial risk measurement and management. I begin by showing why there's a need for financial risk measurement and management, and then I turn to relevant aspects of return distributions and volatility fluctuations, with implicit emphasis on market risk for equities. I then treat market risk for bonds, focusing on the yield curve, with its nuances and special structure. In addition to market risk measurement and management, I also discuss aspects of measuring credit risk, operational risk, systemic risk, and underlying businesscycle risk. I nevertheless also stress the limits of statistical analysis, and the associated importance of respecting the unknown and the unknowable. Diebold, F.X. (2012), "A Personal Perspective on the Origin(s) and Development of 'Big Data': The Phenomenon, the Term, and and the Discipline," Manuscript, Department of Economics, University of Pennsylvania. I investigate Big Data, the phenomenon, the term, and the discipline, with emphasis on origins of the term, in industry and academics, in computer science and statistics/econometrics. Big Data the phenomenon continues unabated, Big Data the term is now firmly entrenched, and Big Data the discipline is emerging. Diebold, F.X. and Yilmaz, K. (2012), "Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers (with discussion)," International Journal of Forecasting, 28, 5766. Using a generalized vector autoregressive framework in which forecasterror 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, crossmarket 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. Aruoba, S.B., Diebold, F.X., Kose, M.A. and Terrones, M.E. (2011), "Globalization, the Business Cycle, and Macroeconomic Monitoring," in R. Clarida and F.Giavazzi (eds.), NBER International Seminar on Macroeconomics. Chicago: University of Chicago Press, 245302. We propose and implement a framework for characterizing and monitoring the global business cycle. Our framework utilizes highfrequency data, allows us to account for a potentially large amount of missing observations, and is designed to facilitate the updating of global activity estimates as data are released and revisions become available. We apply the framework to the G7 countries and study various aspects of national and global business cycles, obtaining three main results. First, our measure of the global business cycle, the common G7 real activity factor, explains a significant amount of crosscountry variation and tracks the major global cyclical events of the past forty years. Second, the common G7 factor and the idiosyncratic country factors play different roles at different times in shaping national economic activity. Finally, the degree of G7 business cycle synchronization among country factors has changed over time. Christensen, J.H.E., Diebold, F.X. and Rudebusch, G.D. (2011), "The Affine ArbitrageFree Class of NelsonSiegel Term Structure Models," Journal of Econometrics, 164, 420. NelsonSiegel, arbitragefree. The best of both worlds! Details: We derive the class of arbitragefree affine dynamic term structure models that approximate the widelyused NelsonSiegel yieldcurve specification. Our theoretical analysis relates this new class of models to the canonical representation of the threefactor arbitragefree affine model. Our empirical analysis shows that imposing the NelsonSiegel 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. 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, 199214. 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, 5565.) We sketch a framework for monitoring macroeconomic activity in realtime and push it in new directions. In particular, we focus not only on real activity, which has received most attention to date, but also on inflation and its interaction with real activity. As for the recent recession, we find that (1) it likely ended around July 2009; (2) its most extreme aspects concern a real activity decline that was unusually long but less unusually deep, and an inflation decline that was unusually deep but brief; and (3) its real activity and inflation interactions were strongly positive, consistent with an adverse demand shock. Andersen, T.G., Bollerslev, T. and Diebold, F.X. (2010), "Parametric and Nonparametric Volatility Measurement," in L.P. Hansen and Y. AitSahalia (eds.), Handbook of Financial Econometrics. Amsterdam: NorthHolland, 67138. All the technical rigor you (or at least I) could ever want... Diebold, F.X. and Yilmaz, K. (2010), "Macroeconomic Volatility and Stock Market Volatility, Worldwide," in T. Bollerslev, J. Russell and M. Watson (eds.), Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle. Oxford: Oxford University Press, 97116. 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. Diebold, F.X. (2010), "Discussion of Jeremy J. Nalewaik: The Income and ExpenditureSide Estimates of U.S. Output Growth," Brookings Papers on Economic Activity (spring), 107112. Diebold, F.X., Kilian, L. and Nerlove, M. (2009), "Time Series Analysis," in L. Blume and S. Durlauf (eds.), The New Palgrave Dictionary of Economics, Second Edition. London: Macmillan, 284298. We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading. 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, 266278. 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 countercyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwisestandard 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 nonfinancial 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 timevarying expected business conditions likely captures timevarying risk, while timevarying consumption/wealth captures timevarying risk aversion. Christensen, J.H.E., Diebold, F.X. and Rudebusch, G.D. (2009), "An ArbitrageFree Generalized NelsonSiegel Term Structure Model," The Econometrics Journal, 12, 3364. If ChristensenDieboldRudebusch (2007) is arbitragefree NelsonSiegel, then this paper is effectively arbitragefree Svensson. The fourth factor in the Svensson model helps with its longmaturity fit (relative to threefactor NelsonSiegel), but making it arbitragefree is significantly more involved than with NelsonSiegel. Indeed we show that there does not exist an arbitragefree model with Svensson factor loadings. We also show, however, that a simple fivefactor generalization (where the fifth factor has a natural interpretation as a second slope factor) solves the problem, achieving both freedom from arbitrage and good longmaturity fit in a Svenssonstyle environment. Aruoba, S.B., Diebold, F.X. and Scotti, C. (2009), "RealTime Measurement of Business Conditions," Journal of Business and Economic Statistics, 27, 417427 (lead article). We construct a framework for measuring highfrequency economic activity using a variety of stock and flow data observed at mixed frequencies. Specifically, we propose a dynamic factor model that permits exact filtering, and we explore the efficacy of our methods both in an empirical example and in a simulation study.
Click here for realtime updates of the AruobaDieboldScotti Business Conditions Index, reported by the Federal Reserve Bank of Philadelphia. 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 noncrisis 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 DieboldYilmaz Spillover Index, reported by the Turkish Economic Research Forum. The popular NelsonSiegel (1987) yield curve is routinely fit to cross sections of intracountry bond yields, and Diebold and Li (2006) have recently proposed a dynamized version. In this paper we extend DieboldLi to a global context, modeling a potentially large set of country yield curves in a framework that allows for both global and countryspecific 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. 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, 701720. Separating jump from nonjump 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 nonjump movements are not. Christoffersen, P.F., Diebold, F.X., Mariano, R.S., Tay, A.S. and Tse, Y.K. (2007), "DirectionofChange Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics: International Evidence," Journal of Financial Forecasting, 1, 324. We generalize the ChristoffersenDiebold (2006) diretionofchange 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), "RealTime Price Discovery in Stock, Bond and Foreign Exchange Markets," Journal of International Economics, 73, 251277. 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 traditionallyexpected 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 A. Timmermann (eds.), Handbook of Economic Forecasting. Amsterdam: NorthHolland, 778878. 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, 309338. 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, 337364. Click here for data. The classic NelsonSiegel curve, suitably dynamized and reinterpreted as a modern threefactor 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, 140. (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 NoArbitrage Approach to RangeBased Estimation of Return Covariances and Correlations," Journal of Business, 79, 6174. We generalize the AlizadehBrandtDiebold (2002) rangebased approach to volatility to the multivariate case by exploiting noarb 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, 24,181183. Incorporating jumps, timevarying expected returns, and intrinsic market microstructure nonlinearities when correcting realized volatility for microstructure noise. The wideranging predictions of economic theory for the correlation between microstructure noise and latent price. Diebold, F.X., Ji, L. and Li, C. (2006), "A ThreeFactor Yield Curve Model: NonAffine Structure, Systematic Risk Sources, and Generalized Duration," in L.R. Klein (ed.), LongRun Growth and ShortRun Stabilization: Essays in Memory of Albert Ando. Cheltenham, U.K.: Edward Elgar, 240274. More on the DieboldLi 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, DirectionofChange Forecasting, and Volatility Dynamics," Management Science, 52, 12731287. 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, 513548. 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 highdimensional 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, 398404. 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, 415420. 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 NelsonSiegel arbitragefree. Campbell, S. and Diebold, F.X. (2005), "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, 100, 616. We take a simple yet sophisticated timeseries 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 longhorizon 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, 8286. 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, 165185, 2004. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2003), "Modeling and Forecasting Realized Volatility," Econometrica, 71, 529626. Andersen, T., Bollerslev, T., Diebold, F.X. and Vega, C. (2003), "Micro Effects of Macro Announcements: RealTime Price Discovery in Foreign Exchange," American Economic Review, 93, 3862. 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, 115122. Diebold, F.X. (2003), "The ET Interview: Professor Robert F. Engle," Econometric Theory, 19, 11591193. Alizadeh, S., Brandt, M. and Diebold, F.X. (2002), "RangeBased Estimation of Stochastic Volatility Models," Journal of Finance, 57, 10471092. 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, 4376. (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, 4255. 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, 113. Published in abridged form as "Liquidity on the Outside," Risk, 12, 6873, 1999. DIebold, F.X. and Inoue, A. (2001), "Long Memory and Regime Switching,” Journal of Econometrics, 105, 131159. Diebold, F.X. and Kilian, L. (2001), "Measuring Predictability: Theory and Macroeconomic Applications," Journal of Applied Econometrics, 16, 657669. 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, 159179. Diebold, F.X. and Kilian, L. (2000), "Unit
Root Tests are Useful for Selecting Forecasting Models," Journal
of Business and Economic Statistics, 18, 265273. Diebold, F.X., Hahn, J. and Tay, A. (1999), "Multivariate Density Forecast Evaluation and Calibration in Financial Risk Management: HighFrequency Returns on Foreign Exchange," Review of Economics and Statistics, 81, 661673. 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, 7690. Oxford: Oxford University Press. Christoffersen, P. and Diebold, F.X. (1998), "Cointegration and LongHorizon Forecasting," Journal of Business and Economic Statistics, 16, 450458. 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, 109118. Diebold, F.X. (1998), "The Past, Present and Future of Macroeconomic Forecasting," Journal of Economic Perspectives, 12, 175192. Diebold, F.X., Gunther, T. and Tay, A. (1998), "Evaluating Density Forecasts, with Applications to Financial Risk Management," International Economic Review, 39, 863883. Diebold, F.X. Hickman, A., Inoue, A. and Schuermann, T. (1998), "Converting 1Day Volatility to hDay Volatility: Scaling by Rooth is Worse than You Think," Wharton Financial Institutions Center, Working Paper 9734. Published in condensed form as "Scale Models," Risk, 11, 104107, 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, 433452. Christoffersen, P. and Diebold, F.X. (1997), "Optimal Prediction Under Asymmetric Loss," Econometric Theory, 13, 808817. Diebold, F.X., Neumark, D. and Polsky, D. (1997), "Job Stability in the United States,” Journal of Labor Economics, 15, 206233. 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, 221241. Diebold, F.X. and Lopez, J.A. (1996), "Forecast Evaluation and Combination," in G.S. Maddala and C.R. Rao (eds.), Handbook of Statistics. Amsterdam: NorthHolland, 241268. Diebold, F.X. and Rudebusch (1991), "Is Consumption too Smooth? Long Memory and the Deaton Paradox,” Review of Economics and Statistics, 73, 19. Diebold, F.X. and Rudebusch, G.D. (1989), "Long Memory and Persistence in Aggregate Output,” Journal of Monetary Economics, 24, 189209. Diebold, F.X. (1989), "Random Walks vs. Fractional Integration: Power Comparisons of Scalar and Joint Tests of the VarianceTime Function,” in Baldev Raj (ed.), Advances in Econometrics and Modeling, 2945. Advanced Studies in Theoretical and Applied Econometrics, Volume 15. Boston: Kluwer Academic Publishers. Diebold, F.X. (1988), "Serial Correlation and the Combination of Forecasts,” Journal of Business and Economic Statistics, 6, 105112.
