Miscellaneous Issues in Forecasting, Risk Measurement and Risk Management

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. (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.

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.

Diebold, F.X., Doherty, N.A. and Herring, R.J. (2010), "The Known, the Unknown, and the Unknowable in Financial Risk Management," (First chapter of book with the same title.)

We provide a variety of glimpses into the successes and failures of various parts of modern financial risk management. However, it is not our intent -- indeed it is not logically possible -- to provide a survey of the known, the unknown and the unknowable (KuU). Instead, we aim to provide illustrations of 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.

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, in press.

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., 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.

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...

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. and Inoue, A. (2001), "Long Memory and Regime Switching,” Journal of Econometrics, 105, 131-159.

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., 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. (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., 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., 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. 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.

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

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. (1991), "A Note on Bayesian Forecast Combination Procedures,” in Economic Structural Change: Analysis and Forecasting (A. Westlund and P. Hackl, eds.), 225-232, 1991. New York: Springer-Verlag.

Diebold, F.X. and Nason, J. (1990), "Nonparametric Exchange Rate Prediction?,” Journal of International Economics, 28, 315-332.

Diebold, F.X. and Pauly, P. (1990), "The Use of Prior Information in Forecast Combination,” International Journal of Forecasting, 6, 503-508.

Diebold, F.X. (1989), "Forecast Combination and Encompassing: Reconciling Two Divergent Literatures,” International Journal of Forecasting, 5, 589-592.

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.

Diebold, F.X. (1988), "Serial Correlation and the Combination of Forecasts,” Journal of Business and Economic Statistics, 6, 105-112.