Publications

Survey density forecast comparison in small samples (with Fabrizio Iacone and Fabio Profumo)

International Journal of Forecasting, 2024 [Replication Files]

Abstract: We apply fixed-b and fixed-m asymptotics to tests of equal predictive accuracy and of encompassing for survey density forecasts. We verify in an original Monte Carlo design that fixed-smoothing asymptotics delivers correctly sized tests in this framework, even when only a small number of out of sample observations is available. We use the proposed density forecast comparison tests with fixed-smoothing asymptotics to assess the predictive ability of density forecasts from the European Central Bank’s Survey of Professional Forecasters (ECB SPF). We find an improvement in the relative predictive ability of the ECB SPF since 2010, suggesting a change in the forecasting practice after the financial crisis.

Does real-time macroeconomic information help to predict interest rates? (with Alberto Caruso) 

Journal of Money, Credit and Banking, 2023, 55(8), 2027 - 2059. [Replication Files]

Abstract: We analyse the predictive ability of real-time macroeconomic information for the yield curve of interest rates. We specify a mixed-frequency macro-yields model in real-time that incorporates interest rate surveys and treats macroeconomic factors as unobservable components. Results indicate that real-time macroeconomic information is helpful to predict interest rates, and that data revisions  drive a superior predictive ability of revised macro data over real-time macro data. We also find that interest rate surveys can have significant predictive power over and above real-time macro variables.

Testing the predictive accuracy of COVID-19 forecasts (with Fabrizio Iacone, Alessia Paccagnini and Paulo Santos Monteiro) 

International Journal of Forecasting, 2023, 39(2), 606-622. [VoxEU Column]

Abstract: We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention for the epidemic in the United States. We find three main results.  First, at the short horizon (1-week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3- and 4-week ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results,  collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.

European spreads at the interest rate lower bound (with Sergio Pastorello)

Journal of Economic Dynamics and Control, 2020, 119.

Abstract: This paper analyzes the effect of the interest rate lower bound on long-term sovereign bond spreads in the euro area. We specify a joint shadow rate term structure model for the risk-free, the German, and the Italian sovereign yield curves. In our model, the behavior of long-term spreads becomes strongly nonlinear in the underlying factors when interest rates are close to the lower bound, which occurs in the data  since the beginning of 2012. We fit the model via Quasi-Maximum Likelihood and show three consequences of  the nonlinear behavior of sovereign spreads: i)  they are asymmetrically distributed, ii) they are affected by (possibly exogenous) changes in the lower bound, and iii) they become less informative about sovereign risk than when interest rates are far from the lower bound. Shadow spreads, however, still provide reliable information.

Comparing predictive accuracy in small samples using fixed-smoothing asymptotics (with Fabrizio Iacone)

Journal of Applied Econometrics, 2020, 35(4), 391–405. [Online Appendix, Matlab Function, R Function]

Abstract: We consider fixed-smoothing asymptotics for the Diebold and Mariano (1995) test of predictive accuracy. We show that this approach delivers predictive accuracy tests that are correctly sized even when only a small number of out of sample observations is available. We apply the fixed-smoothing asymptotics to the Diebold and Mariano (1995) test to evaluate the predictive accuracy of the Survey of Professional Forecasters (SPF) and of the ECB Survey of Professional Forecasters (ECB SPF) against a simple random walk. Our results show that the predictive abilities of the SPF and of the ECB SPF were partially spurious.

International Stock Comovements with Endogenous Clusters (with Laura E. Jackson and Michael T. Owyang)

Journal of Economic Dynamics and Control, 2020, 116.

Abstract: We use an endogenous cluster factor model to examine international stock return comovements of country-industry portfolios. Our model allows country-industry portfolio comovements to be driven by a global and a cluster component, with the cluster membership endogenously determined. Results indicate that country-industry portfolios tend to cluster mainly within geographical areas that can include one or more countries. The cluster component was the main driver of country-industry portfolio returns for most of the sample, except from mid-2000 to mid-2010s when the global component had a more prominent role. At the end of the sample, a large cluster among European countries emerges.

Testing for optimal monetary policy via moment inequalities (with Valentina Corradi and Paulo Santos Monteiro)

Journal of Applied Econometrics, 2018, 33(6), 780-796.

Abstract: The specification of an optimizing model of the monetary transmission mechanism requires selecting a policy regime, commonly commitment or discretion. In this paper we propose a new procedure for testing optimal monetary policy, relying on moment inequalities that nest commitment and discretion as two special cases. The approach is based on the derivation of bounds for inflation that are consistent with optimal policy under either policy regime. We derive testable implications that allow for specification tests and discrimination between the two alternative regimes. The proposed procedure is implemented to examine the conduct of monetary policy in the United States economy.

Dynamic Linkages Across Country Yield Curves: The Effects of Global and Local Yield Curve Factors on US, UK and German Yields (with Ian Garrett and Javier Sanhueza)

New Methods in Fixed Income Modeling. Contributions to Management Science, Springer, 205-222, 2018.

Abstract: We analyze the relationship between the yield curves of the USA, the UK and Germany using global and local factors. Our focus is on dynamic linkages across and between yield curves and factors. We disentangle the latent global and local factors contained in country factors, based on the Diebold and Li (2006) parametrization of Nelson and Siegel’s (1987) three factor model and a quasi-maximum likelihood approach. The results indicate that global factors explain on average 55% of the variance of yields. Using impulse response analysis, we examine the effects of shocks to the factors on yields. We find that the response of yields to shocks to global factors is larger and longer-lasting than the response to shocks to local factors.

Unspanned macroeconomic factors in the yield curve (with Domenico Giannone and Michele Modugno)

Journal of Business and Economic Statistics, 2016, 34(3), 472-485. [Online Appendix] [Replication Files]

Abstract: In this paper, we extract common factors from a cross-section of U.S. macro-variables and Treasury zero-coupon yields. We find that two macroeconomic factors have an important predictive content for government bond yields and excess returns. These factors are not spanned by the cross-section of yields and are well proxied by economic growth and real interest rates.

A simple two-component model for the distribution of intraday returns (with David Veredas)

The European Journal of Finance, 2012, 18(9), 775-797. [Web Appendix PDF Excel]

Abstract: We model the conditional probability law of high frequency financial returns by means of quantile regression. Using three years of 30 minutes sampled returns for a set of stocks traded at the Spanish Stock Exchange, a pure limit order book electronic platform, we show that the conditional probability density depends on past returns and on the time of the day. Two practical applications illustrate the usefulness of the methodology. First, we provide quantile-based measures of conditional volatility, asymmetry and kurtosis that do not depend on the existence of moments. We find seasonal patterns and time dependencies beyond volatility. Second, we estimate and forecast intraday Value at Risk. A battery of tests show that our methodology delivers good risk assessments for intraday returns, and it clearly outperforms GARCH-based Value at Risk assessments.

How arbitrage-free is the Nelson and Siegel model? (with Ken Nyholm and Rositsa Vidova-Koleva)

Journal of Empirical Finance, 2011,18(3), 393-407.

Abstract: We test whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities, as shown by Bjork and Christensen (1999) and Filipovic (1999). Still, central banks and wealth managers rely heavily on it. Using zero-coupon yield curve data from the US market, we find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson-Siegel model. We therefore conclude that the Nelson-Siegel yield curve model is compatible with the no-arbitrage constraints on the US market. To corroborate this result, we also show that the Nelson-Siegel model performs as well as its no-arbitrage counterpart in an out-of-sample forecasting experiment.