Macroeconomic and Business Cycle Measurement, Modeling and Forecasting

Books

Diebold, F.X. and Yilmaz, K. (2015), Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring, Oxford University Press. Front matter and T.O.C. here.

Diebold, F.X. and Rudebusch, G.D. (2013), Yield Curve Modeling and Forecasting. Princeton: Princeton University Press. First chapter here. Insightful Riccardo Rebonato review here.

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, Volume 267). Click here for first chapter.

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.

Diebold, F.X., Doherty, N. and Herring, R. (2010), The Known, the Unknown and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice (ed.). Princeton: Princeton University Press. (2012 Recipient of the American Risk and Insurance Association's Kulp-Wright Award; finalist for the Paul A. Samuelson Award, TIAA-CREF).

On the successes and failures of various parts of modern financial risk management, emphasizing the known (K), the unknown (u) and the unknowable (U). We illustrate a KuU-based perspective for conceptualizing financial risks and designing effective risk management strategies. Sometimes we focus on K, and sometimes on U, but most often our concerns blend aspects of K and u and U. Indeed K and U are extremes of a smooth spectrum, with many of the most interesting and relevant situations interior. Statistical issues emerge as central to risk measurement, and we push toward additional progress. But economic issues of incentives and strategic behavior emerge as central for risk management, as we illustrate in a variety of contexts.

Bernadel, C., Cardon, P., Coche, J., Diebold, F.X. and Manganelli, S. (2004), Risk Management for Central Bank Foreign Reserves (ed.). Frankfurt: European Central Bank.

Central banks manage huge forex portfolios. It would be socially irresponsible for them to manage those portfolios like aggressive and risky hedge funds, yet it would presumably be similarly socially irresponsible for them to settle for a risk-free return. So how should central banks manage their portfolios? This book grapples with that interesting question.

Diebold, F.X. and Rudebusch, G.D. (1999), Business Cycles: Durations, Dynamics and Forecasting, Princeton: Princeton University Press.

A collection of our papers in time-series macro-econometrics, with an interpretive introductions. Includes material on the analysis of business cycle durations, long memory, regime switching, leading indicators, turning points, and predictive accuracy comparisons.

Diebold, F.X. (1988), Empirical Modeling of Exchange Rate Dynamics. New York and Berlin: Springer-Verlag.

My 1986 Ph.D. dissertation, written in 1984-1985, showing that ARCH effects are important in asset returns. Hard to believe from today's vantage point, but that was a very novel result at the time! Also contains work on testing for serial correlation in the presence of ARCH, Gaussian CLTs for ARCH processes (so that temporal aggregation reduces the fat tails in unconditional distributions), and multivariate latent-factor ARCH.