Click HERE for new research (all types and topics).
Click HERE for research papers listed reverse chronologically.
Click HERE for the real-time Aruoba-Diebold-Scotti (ADS) Business Conditions Index
(Federal Reserve Bank of Philadelphia).
Click BELOW for books:
Yield Curve Modeling and Forecasting: The Dynamic Nelson-Siegel Approach, Princeton University Press, in press, with G. Rudebusch.
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.
Financial Risk Measurement and Management, Edward Elgar Publishing (International Library of Critical Writings in Economics, Volume **), in press.
Classics old and new, with an interpretive introduction.
The Known, the Unknown and the Unknowable in Financial Risk Management, Princeton University Press, 2010, with N. Doherty and R. Herring.
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.
Elements of Forecasting (Fourth Edition), South-Western College Publishing, 2007.
This book remains popular, but it's getting old and I'm getting bored. Rather than doing yet another small tweak (i.e., edition), I've decided that it's time to put it to bed and do a fresh new book, retaining the appeal of the Elements but going well beyond and bringing it into the new millenium, highly e-aware, and sold at an attractively low price. Work has commenced. Comments/suggestions most welcome.
Risk Management for Central Bank Foreign Reserves, European Central Bank, 2004, with C. Bernadel, P. Cardon, J. Coche and S. Manganelli (eds.).
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.
Business Cycles: Durations, Dynamics and
Forecasting, Princeton University Press, 1999, with G. Rudebusch.
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 comparison.
Empirical
Modeling of Exchange Rate Dynamics, Springer-Verlag, 1988.
My 1986 Ph.D. dissertation, written mostly 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.
Click BELOW for research papers by topic:
Asset return volatility and correlation
measurement, modeling and forecasting
Realized volatility computed from high-frequency data; range-based
volatility; GARCH-based volatility; measuring volatility spillovers;
volatility and macroeconomic fundamentals; applications to portfolio
allocation, risk management and asset pricing.
Yield curve measurement, modeling and
forecasting
Reinterpreting Nelson-Siegel as a modern three-factor model of level,
slope and curvature; superior forecasting performance; links of factors
to macroeconomic fundamentals (inflation, capacity utilization);
hedging bond portfolio risk using generalized duration; globalizing the
model; making the model arbitrage-free.
General financial market
measurement, modeling and forecasting
Stock returns and expected business conditions; market timing and
direction-of-change forecasting; density forecasting and forecast
evaluation; forecasting under asymmetric loss functions; specifying
forecasting models and measuring forecastability; forecast evaluation
in cointegrated systems.
Macroeconomic and business cycle
measurement, modeling and forecasting
Global stock market volatility and macroeconomic fundamentals;
real-time measurement of business conditions; stock returns and
expected business conditions; dynamic factor models; regime switching;
business cycle effects in credit risk modeling; yield curve modeling
with macro interactions; real-time news effects in financial markets;
how to calibrate if you must.
Measuring Connectedness in Financial and Macroeconomic Contexts
General connectedness neasures based on the network topology of variance decompositions (e.g., from VARs with time-varying parameters). Applications to connectedness of within-country financial markets, cross-country financial markets, financial firms, country real outputs, ...
Miscellaneous issues in forecasting,
risk measurement and risk management
Several surveys of aspects of volatility modeling and forecasting;
weather derivatives; VaR horizons; liquidity risk; extreme values;
forecast accuracy comparison; long memory; regime switching.
Lighter Fare:
"The Known,
the Unknown, and the Unknowable in Financial Risk Management"
(Introduction to 2010 book of the same title with R.J. Herring and N.J.
Doherty).
Statistical issues emerge as central to risk measurement,
but economic issues of incentives and strategic behavior
emerge as central for risk management, as we illustrate in a
variety of contexts.
"The
New Role of Risk Management: Rebuilding the Model,"
Knowledge@Wharton Interview, June 24, 2009. Audio and related materials here.
"The Nobel Prize for Robert F. Engle", Scandinavian Journal of Economics, 106, 165-185, 2004.
Understanding Rob Engle's 2003 Nobel Prize in Economics.
Volatility and correlation modeling in financial markets.
What happened and why.
"Econometrics: Retrospect and Prospect," Journal
of Econometrics, 100, 73-75, 2001.
Looking backward and forward on the twenty-fifth anniversary of the
founding of the Journal of Econometrics.
"Great Realizations," Risk, March 2000,
105-108 (with T. Andersen and T. Bollerslev).
Describes the potential of realized volatility methods, in conjunction
with modern high-frequency data, for measuring asset return
volatilities and correlations. Introduces the volatility
signature plot for detecting and mitigating the effects of
microstructure noise.
"The Past, Present and Future of Macroeconomic
Forecasting," Journal of Economic Perspectives, 12,
175-192, 1998.
General equilibrium models useful for forecasting?! Lots
of people think this article is naive, or just plain wrong. Time
will tell... |
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