Showing papers by "Francis X. Diebold published in 2013"

Yield Curve Modeling and Forecasting: The Dynamic Nelson-Siegel Approach

Abstract: 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. In this book, Francis Diebold and Glenn Rudebusch 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). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

Measuring the Dynamics of Global Business Cycle Connectedness

Abstract: Using a connectedness-measurement technology fundamentally grounded in modern network theory, we measure real output connectedness for a set of six developed countries, 1962-2010. We show that global connectedness is sizable and time-varying over the business cycle, and we study the nature of the time variation relative to the ongoing discussion about the changing nature of the global business cycle. We also show that connectedness corresponding to transmissions to others from the United States and Japan is disproportionately important.

Financial Risk Measurement for Financial Risk Management

Abstract: Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfolio-level and asset-level analysis. Asset-level analysis is particularly challenging because the demands of real-world risk management in financial institutions—in particular, real-time risk tracking in very high-dimensional situations—impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also cross-fertilize the academic and practitioner communities, promoting improved market risk measurement technologies that draw on the best of both.

On the Correlation Structure of Microstructure Noise: A Financial Economic Approach

Abstract: We introduce the financial economics of market microstructure to the financial econometrics of asset return volatility estimation. In particular, we derive the crosscorrelation function between latent returns and market microstructure noise in several leading microstructure environments. We propose and illustrate several corresponding theory-inspired volatility estimators, which we apply to stock and oil prices. Our analysis and results are useful for assessing the validity of the frequently-assumed independence of latent price and microstructure noise, for explaining observed cross-correlation patterns, for predicting as-yet undiscovered patterns, and most importantly, for promoting improved microstructure-based volatility empirics and improved empirical microstructure studies. Simultaneously and conversely, our analysis is far from the last word on the subject, as it is based on stylized benchmark models; it comes with a “call to action” for development and use of richer microstructure models in volatility estimation and beyond.

Improving GDP Measurement: A Measurement-Error Perspective

Abstract: We provide a new and superior measure of U.S. GDP, obtained by applying optimal signal-extraction techniques to the (noisy) expenditure-side and income-side estimates. Its properties - particularly as regards serial correlation - differ markedly from those of the standard expenditure-side measure and lead to substantially-revised views regarding the properties of GDP.

Improving GDP measurement: a measurement-error perspective

Abstract: We provide a new and superior measure of U.S. GDP, obtained by applying optimal signal-extraction techniques to the (noisy) expenditure-side and income-side estimates. Its properties - particularly as regards serial correlation - differ markedly from those of the standard expenditure-side measure and lead to substantially-revised views regarding the properties of GDP.