Francis X Diebold
Biographic Data
| ID | 984025 |
|---|---|
| NAME | Francis X Diebold |
| GIVEN NAMES | Francis X |
| FAMILY NAME | Diebold |
| SIGNATURE | DIEBOLD F X |
| AFFILIATIONS | University of Pennsylvania |
| VERIFIED | No |
| TOTAL WORKS | 28 |
| TOTAL CITATIONS | 373 |
| AUTHOR COUNT | 27 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 1988 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 3 |
Business Cycles: Durations, Dynamics, and Forecasting
Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold–Mariano Tests
The Diebold–Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Much of the large ensuing literature, however, uses DM-type tests for comparing models, in pseudo-out-of-sample environments. In that case, simpler yet more compelling full-sample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch t…
On the network topology of variance decompositions: Measuring the connectedness of financial firms
Better to give than to receive: Predictive directional measurement of volatility spillovers
The Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice
A clear understanding of what we know, don't know, and can't know should guide any reasonable approach to managing financial risk, yet the most widely used measure in finance today--Value at Risk, or VaR--reduces these risks to a single number, creating a false sense of security among risk managers, executives, and regulators. This book introduces a more realistic and holistic framework called KuU --the K nown, the u nknown, and the U nknowable--…
Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice
Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence
Using survey data, we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a counter-cyclical fashion. Moreover, inclusion of expected business conditions in otherwise-standard predictive return regressions substantially reduce the explanatory power of the conventional financial predictors, including the dividend yield, defaul…
Real-Time Measurement of Business Conditions
We construct a framework for measuring economic activity at high frequency, potentially in real time. We use a variety of stock and flow data observed at mixed frequencies (including very high frequencies), and we use a dynamic factor model that permits exact filtering. We illustrate the framework in a prototype empirical example and a simulation study calibrated to the example
Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets
We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of "return spillovers" and "volatility spillovers". Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers; both turn out to be empirically important. In particular, in an analysis of 19 global equity markets from the earl…
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility
A growing literature documents important gains in asset return volatility forecasting via use of realized variation measures constructed from high-frequency returns. We progress by using newly developed bipower variation measures and corresponding nonparametric tests for jumps. Our empirical analyses of exchange rates, equity index returns, and bond yields suggest that the volatility jump component is both highly important and distinctly less per…
Modeling and Forecasting Realized Volatility
This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intrad…
Comparing Predictive Accuracy
We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss of function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample…
Unit-Root Tests Are Useful for Selecting Forecasting Models
We study the usefulness of unit-root tests as diagnostic tools for selecting forecasting models. Difference-stationary and trend-stationary models of economic and financial time series often imply very different predictions, so deciding which model to use is tremendously important for applied forecasters. We consider three strategies: Always difference the data, never difference, or use a unit-root pretest. We characterize the predictive loss of …
How Relevant is Volatility Forecasting for Financial Risk Management
It depends. If volatility fluctuates in a forecastable way, volatility forecasts are useful for risk management (hence the interest in volatility forecastability in the risk management literature). Volatility forecastability, however, varies with horizon, and different horizons are relevant in different applications. Moreover, existing assessments of volatility forecastability are plagued by the fact that they are joint assessments of volatility …
Long Memory and Persistence in Aggregate Output
We examine persistence in U.S. aggregate output by estimating fractionally integrated ARIMA models.These models provide better low-frequency approximations to the Wold representation than previous stochastic specifications, and earlier results on the importance of a permanent component emerge as special cases.We find evidence of long memory, which induces persistence, though this long memory need not be associated with a unit root.Our point estim…
Multivariate Density Forecast Evaluation and Calibration In Financial Risk Management: High-Frequency Returns on Foreign Exchange
We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions under which a technique of density forecast “calibration” can be used to improve deficient density forecasts, and we show how the calibration method can be…
Cointegration and Long-Horizon Forecasting
We consider the forecasting of cointegrated variables, and we show that at long horizons nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box–Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to value the maintenance of cointegrating relationships a…
Bootstrapping Multivariate Spectra
We generalize the Franke-Härdle (1992) spectral-density bootstrap to the multivariate case. The extension is nontrivial and facilitates use of the Franke-Härdle bootstrap in frequency-domain econometric work, which often centers on crossvariable dynamic interactions. We document the bootstrap's good finite-sample performance in a small Monte Carlo experiment, and we conclude by highlighting key directions for future research
The Past, Present, and Future of Macroeconomic Forecasting
Broadly defined, macroeconomic forecasting is alive and well. Nonstructural forecasting, which is based largely on reduced-form correlations, has always been well and continues to improve. Structural forecasting, which aligns itself with economic theory and hence rises and falls with theory, receded following the decline of Keynesian theory. In recent years, however, powerful new dynamic stochastic general equilibrium theory has been developed an…
Bounded Rationality and Strategic Complementarity in a Macroeconomic Model: Policy Effects, Persistence and Multipliers
Motivated by recent developments in the bounded rationality and strategic complementarity literatures, we examine an intentionally simple and stylised aggregative economic model, when the assumptions of fully rational expectations and no strategic interactions are relaxed. We show that small deviations from rational expectations, taken alone, lead only to small deviations from classical policy‐ineffectiveness, but that the situation can change dr…
Measuring Business Cycles: A Modern Perspective
In the first half of this century, special attention was given to two features of the business cycle: the comovement of many individual economic series and the different behavior of the economy during expansions and contractions. Recent theoretical and empirical research has revived interest in each attribute separately, and we survey this work. Notable empirical contributions are dynamic factor models that have a single common macroeconomic fact…
Comparing Predictive Accuracy
We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample te…
Is Consumption Too Smooth? Long Memory and the Deaton Paradox
Consumption (Economics); Income
Real Exchange Rates under the Gold Standard
In this paper, the authors assert that most studies that have sought to determine the validity of purchasing power parity are flawed for two reasons. First, post-1973 data contain, by definition, only a very limited amount of the low-frequency information relevant for examination of long-run parity. Second, the dynamic econometric techniques used to model deviations from parity are typically quite crude with respect to admissible low-frequency dy…
Post-Deregulation Bank-Deposit-Rate Pricing: The Multivariate Dynamics
The relationship between wholesale and retail interest rates since deregulation is of substantial interest to economists and policymakers, because the predictability of the monetary aggregates and their relationship to bank reserves depend on adjustment patterns in the wholesale and retail money markets. We provide evidence on the nature of wholesale–retail interest rate relationships by examining the dynamic interactions among two wholesale inte…
Better to give than to receive: Predictive directional measurement of volatility spillovers
Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets
We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of "return spillovers" and "volatility spillovers". Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers; both turn out to be empirically important. In particular, in an analysis of 19 global equity markets from the earl…
A Nonparametric Investigation of Duration Dependence in the American Business Cycle
Does the termination probability of a business expansion or contraction increase with age? This question may be formally addressed by analyzing the nature of duration dependence in aggregate economic activity. The author's null hypothesis is that there is no duration dependence, which they test via intentionally nonparametric procedures. They also argue that a common notion of business cycle periodicity can be usefully interpreted in terms of who…
The Past, Present, and Future of Macroeconomic Forecasting
Broadly defined, macroeconomic forecasting is alive and well. Nonstructural forecasting, which is based largely on reduced-form correlations, has always been well and continues to improve. Structural forecasting, which aligns itself with economic theory and hence rises and falls with theory, receded following the decline of Keynesian theory. In recent years, however, powerful new dynamic stochastic general equilibrium theory has been developed an…
Real Exchange Rates under the Gold Standard
In this paper, the authors assert that most studies that have sought to determine the validity of purchasing power parity are flawed for two reasons. First, post-1973 data contain, by definition, only a very limited amount of the low-frequency information relevant for examination of long-run parity. Second, the dynamic econometric techniques used to model deviations from parity are typically quite crude with respect to admissible low-frequency dy…
Bounded Rationality and Strategic Complementarity in a Macroeconomic Model: Policy Effects, Persistence and Multipliers
Motivated by recent developments in the bounded rationality and strategic complementarity literatures, we examine an intentionally simple and stylised aggregative economic model, when the assumptions of fully rational expectations and no strategic interactions are relaxed. We show that small deviations from rational expectations, taken alone, lead only to small deviations from classical policy‐ineffectiveness, but that the situation can change dr…
[An Application of Operational-Subjective Statistical Methods to Rational Expectations]: Comment
Serial Correlation and the Combination of Forecasts
It is shown that regression-based methods of forecast combination lead to serially correlated combined prediction errors. The form of the serial correlation is characterized, and specification, estimation, and prediction are treated. A fully optimal combined predictor, which exploits the serial correlation, is developed and compared with existing regression-based methods in a numerical example, leading to decreases in mean squared prediction erro…
Post-Deregulation Bank-Deposit-Rate Pricing: The Multivariate Dynamics
The relationship between wholesale and retail interest rates since deregulation is of substantial interest to economists and policymakers, because the predictability of the monetary aggregates and their relationship to bank reserves depend on adjustment patterns in the wholesale and retail money markets. We provide evidence on the nature of wholesale–retail interest rate relationships by examining the dynamic interactions among two wholesale inte…
A Nonparametric Investigation of Duration Dependence in the American Business Cycle
Does the termination probability of a business expansion or contraction increase with age? This question may be formally addressed by analyzing the nature of duration dependence in aggregate economic activity. The author's null hypothesis is that there is no duration dependence, which they test via intentionally nonparametric procedures. They also argue that a common notion of business cycle periodicity can be usefully interpreted in terms of who…
Is Consumption Too Smooth? Long Memory and the Deaton Paradox
Consumption (Economics); Income
Real Exchange Rates under the Gold Standard
In this paper, the authors assert that most studies that have sought to determine the validity of purchasing power parity are flawed for two reasons. First, post-1973 data contain, by definition, only a very limited amount of the low-frequency information relevant for examination of long-run parity. Second, the dynamic econometric techniques used to model deviations from parity are typically quite crude with respect to admissible low-frequency dy…
Comparing Predictive Accuracy
We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample te…
Measuring Business Cycles: A Modern Perspective
In the first half of this century, special attention was given to two features of the business cycle: the comovement of many individual economic series and the different behavior of the economy during expansions and contractions. Recent theoretical and empirical research has revived interest in each attribute separately, and we survey this work. Notable empirical contributions are dynamic factor models that have a single common macroeconomic fact…
Bounded Rationality and Strategic Complementarity in a Macroeconomic Model: Policy Effects, Persistence and Multipliers
Motivated by recent developments in the bounded rationality and strategic complementarity literatures, we examine an intentionally simple and stylised aggregative economic model, when the assumptions of fully rational expectations and no strategic interactions are relaxed. We show that small deviations from rational expectations, taken alone, lead only to small deviations from classical policy‐ineffectiveness, but that the situation can change dr…
Cointegration and Long-Horizon Forecasting
We consider the forecasting of cointegrated variables, and we show that at long horizons nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box–Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to value the maintenance of cointegrating relationships a…
Bootstrapping Multivariate Spectra
We generalize the Franke-Härdle (1992) spectral-density bootstrap to the multivariate case. The extension is nontrivial and facilitates use of the Franke-Härdle bootstrap in frequency-domain econometric work, which often centers on crossvariable dynamic interactions. We document the bootstrap's good finite-sample performance in a small Monte Carlo experiment, and we conclude by highlighting key directions for future research
The Past, Present, and Future of Macroeconomic Forecasting
Broadly defined, macroeconomic forecasting is alive and well. Nonstructural forecasting, which is based largely on reduced-form correlations, has always been well and continues to improve. Structural forecasting, which aligns itself with economic theory and hence rises and falls with theory, receded following the decline of Keynesian theory. In recent years, however, powerful new dynamic stochastic general equilibrium theory has been developed an…
Long Memory and Persistence in Aggregate Output
We examine persistence in U.S. aggregate output by estimating fractionally integrated ARIMA models.These models provide better low-frequency approximations to the Wold representation than previous stochastic specifications, and earlier results on the importance of a permanent component emerge as special cases.We find evidence of long memory, which induces persistence, though this long memory need not be associated with a unit root.Our point estim…
Multivariate Density Forecast Evaluation and Calibration In Financial Risk Management: High-Frequency Returns on Foreign Exchange
We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions under which a technique of density forecast “calibration” can be used to improve deficient density forecasts, and we show how the calibration method can be…
Unit-Root Tests Are Useful for Selecting Forecasting Models
We study the usefulness of unit-root tests as diagnostic tools for selecting forecasting models. Difference-stationary and trend-stationary models of economic and financial time series often imply very different predictions, so deciding which model to use is tremendously important for applied forecasters. We consider three strategies: Always difference the data, never difference, or use a unit-root pretest. We characterize the predictive loss of …
How Relevant is Volatility Forecasting for Financial Risk Management
It depends. If volatility fluctuates in a forecastable way, volatility forecasts are useful for risk management (hence the interest in volatility forecastability in the risk management literature). Volatility forecastability, however, varies with horizon, and different horizons are relevant in different applications. Moreover, existing assessments of volatility forecastability are plagued by the fact that they are joint assessments of volatility …
Comparing Predictive Accuracy
We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss of function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample…
Modeling and Forecasting Realized Volatility
This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intrad…
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility
A growing literature documents important gains in asset return volatility forecasting via use of realized variation measures constructed from high-frequency returns. We progress by using newly developed bipower variation measures and corresponding nonparametric tests for jumps. Our empirical analyses of exchange rates, equity index returns, and bond yields suggest that the volatility jump component is both highly important and distinctly less per…
Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence
Using survey data, we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a counter-cyclical fashion. Moreover, inclusion of expected business conditions in otherwise-standard predictive return regressions substantially reduce the explanatory power of the conventional financial predictors, including the dividend yield, defaul…
Real-Time Measurement of Business Conditions
We construct a framework for measuring economic activity at high frequency, potentially in real time. We use a variety of stock and flow data observed at mixed frequencies (including very high frequencies), and we use a dynamic factor model that permits exact filtering. We illustrate the framework in a prototype empirical example and a simulation study calibrated to the example
Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets
We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of "return spillovers" and "volatility spillovers". Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers; both turn out to be empirically important. In particular, in an analysis of 19 global equity markets from the earl…
The Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice
A clear understanding of what we know, don't know, and can't know should guide any reasonable approach to managing financial risk, yet the most widely used measure in finance today--Value at Risk, or VaR--reduces these risks to a single number, creating a false sense of security among risk managers, executives, and regulators. This book introduces a more realistic and holistic framework called KuU --the K nown, the u nknown, and the U nknowable--…
Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice
Better to give than to receive: Predictive directional measurement of volatility spillovers
Econometrics (24 works) · Economics (20 works) · Monetary Policy and Economic Impact (18 works) · Computer Science (13 works) · Financial Risk and Volatility Modeling (12 works) · Market Dynamics and Volatility (12 works) · Mathematics (11 works) · Statistics (8 works) · Macroeconomics (6 works) · Artificial Intelligence (5 works)