Recent Work

Chen, F., Diebold, F.X. and Schorfheide, F. (2012), "A Markov-Switching Multi-Fractal Inter-Trade Duration Model, with Application to U.S. Equities," Manuscript, Huazhong University and University of Pennsylvania.

We propose and illustrate a Markov-switching multi-fractal duration (MSMD) model for analysis of inter-trade durations in financial markets. MSMD is a parameter-driven long-memory model of conditional intensity dynamics, with long memory driven by structural markov-switching 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 inter-trade durations: long-memory dynamics and over-dispersed distributions. Empirical exploration suggests MSMD's superiority relative to the leading competitor.

Diebold, F.X. and Strasser, G.H. (Revised 2012), "On the Correlation Structure of Microstructure Noise: A Financial Economic Approach." Issued earlier as "On the Correlation Structure of Microstructure Noise in Theory and Practice," NBER Working Paper 16469.

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 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 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 noise-robust measures of realized volatility.

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 business-cycle risk. I nevertheless also stress the limits of statistical analysis, and the associated importance of respecting the unknown and the unknowable.

Diebold, F.X. and Rudebusch, G.D. (2013), Yield Curve Modeling and Forecasting: The Dynamic Nelson-Siegel Approach. Princeton: Princeton University Press.

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. Against that background, we 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). We emphasize both descriptive and efficient-markets aspects, we pay special attention to the links between the yield curve and macroeconomic fundamentals, and we show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed.

Diebold, F.X. (2012), Financial Risk Measurement and Management (ed.). Cheltenham, U.K. and Northampton, Mass.: Edward Elgar Publishing Ltd. (International Library of Critical Writings in Economics).

Classics old and new. In the interpretive introductory chapter 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 business-cycle risk. I nevertheless also stress the limits of statistical analysis, and the associated importance of respecting the unknown and the unknowable.

Andersen, T.G., Bollerslev, T., Christoffersen, P.F. and Diebold, F.X. (2012), "Financial Risk Measurement for Financial Risk Management," in G. Constantinedes, M. Harris and Rene Stulz (eds.), Handbook of the Economics of Finance, Elsevier.

We stress a conditional approach at both the portfolio and individual-asset levels, at both high frequencies and business cycle frequencies, with special attention to dimensionality-reduction 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.

Two often-divergent U.S. GDP estimates are available, a widely-used expenditure side version GDPE, and a much less widely-used income-side 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 similarly-combined headline GDP estimate, potentially using the methods introduced in this paper.

Diebold, F.X. and Yilmaz, K. (2011), "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," Manuscript, University of Pennsylvania and Koc University.

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.