Research

Research

Journal Articles

Efficient Estimation of Factor Models with Time and Cross‐sectional Dependence

Published April 2017, Journal of Applied Econometrics 32 (6), 1107-1122

Abstract. This paper studies the efficient estimation of large‐dimensional factor models with both time and cross‐sectional dependence assuming (N,T) separability of the covariance matrix. The asymptotic distribution of the estimator of the factor and factor‐loading space under factor stationarity is derived and compared to that of the principal component (PC) estimator. The paper also considers the case when factors exhibit a unit root. We provide feasible estimators and show in a simulation study that they are more efficient than the PC estimator in finite samples. In application, the estimation procedure is employed to estimate the Lee–Carter model and life expectancy is forecast. The Dutch gender gap is explored and the relationship between life expectancy and the level of economic development is examined in a cross‐country comparison.

Working Papers

A Bootstrap Test for the Existence of Moments for GARCH Processes

Updated July 2019

Abstract. This paper studies the joint inference on conditional volatility parameters and the innovation moments by means of bootstrap to test for the existence of moments for GARCH(p,q) processes. We propose a residual bootstrap to mimic the joint distribution of the quasi-maximum likelihood estimators and the empirical moments of the residuals and also prove its validity. A bootstrap-based test for the existence of moments is proposed, which provides asymptotically correctly-sized tests without losing its consistency property. It is simple to implement and extends to other GARCH-type settings. A simulation study demonstrates the test's size and power properties in finite samples and an empirical application illustrates the testing approach.

A General Framework for Prediction in Time Series Models

with Eric Beutner and Stephan Smeekes, updated February 2019

In this paper we propose a general framework to analyze prediction in time series models and show how a wide class of popular time series models satisfies this framework. We postulate a set of high-level assumptions, and formally verify these assumptions for the aforementioned time series models. Our framework coincides with that of Beutner et al. (2019) who establish the validity of conditional confidence intervals for predictions made in this framework. The current paper therefore complements the results in Beutner et al. (2019) by providing practically relevant applications of their theory.

A Justification of Conditional Confidence Intervals

with Eric Beutner and Stephan Smeekes, updated January 2019

Abstract. To quantify uncertainty around point estimates of conditional objects such as conditional means or variances, parameter uncertainty has to be taken into account. Attempts to incorporate parameter uncertainty are typically based on the unrealistic assumption of observing two independent processes, where one is used for parameter estimation, and the other for conditioning upon. Such unrealistic foundation raises the question whether these intervals are theoretically justified in a realistic setting. This paper presents an asymptotic justification for this type of intervals that does not require such an unrealistic assumption, but relies on a sample-split approach instead. By showing that our sample-split intervals coincide asymptotically with the standard intervals, we provide a novel, and realistic, justification for confidence intervals of conditional objects. The analysis is carried out for a rich class of time series models.

A Residual Bootstrap for Conditional Value-at-Risk

with Eric Beutner and Stephan Smeekes, updated November 2018

Abstract. This paper proposes a fixed-design residual bootstrap method for the two-step estimator of Francq and Zakoïan (2015) associated with the conditional Value-at-Risk. The bootstrap's consistency is proven under mild assumptions for a general class of volatility models and bootstrap intervals are constructed for the conditional Value-at-Risk to quantify the uncertainty induced by estimation. A large-scale simulation study is conducted revealing that the equal-tailed percentile interval based on the fixed-design residual bootstrap tends to fall short of its nominal value. In contrast, the reversed-tails interval based on the fixed-design residual bootstrap yields accurate coverage. In the simulation study we also consider the recursive-design bootstrap. It turns out that the recursive-design and the fixed-design bootstrap perform equally well in terms of average coverage. Yet in smaller samples the fixed-design scheme leads on average to shorter intervals. An empirical application illustrates the interval estimation using the fixed-design residual bootstrap.

A Residual Bootstrap for Conditional Expected Shortfall

with Sean Telg, updated November 2018

Abstract. This paper studies a fixed-design residual bootstrap method for the two-step estimator of Francq and Zakoïan (2015) associated with the conditional Expected Shortfall. For a general class of volatility models the bootstrap is shown to be asymptotically valid under the conditions imposed by Beutner et al. (2018). A simulation study is conducted revealing that the average coverage rates are satisfactory for most settings considered. There is no clear evidence to have a preference for any of the three proposed bootstrap intervals. This contrasts results in Beutner et al. (2018) for the VaR, for which the reversed-tails interval has a superior performance.

Work in Progress

• “Testing for Structural Breaks in Approximate Factor Models” with Dante Amengual.

Conferences

• 12th International Conference on Computational and Financial Econometrics, Pisa, December 2018
• Workshop on Financial Econometrics, Örebro, November 2018
• 12th Netherlands Econometrics Study Group, Amsterdam, May 2018
• 13th German Probability and Statistics Days, Freiburg, February 2018
• 11th International Conference on Computational and Financial Econometrics, London, December 2017
• 1st International Conference on Econometrics and Statistics, Hong Kong, June 2017
• 11th Netherlands Econometrics Study Group, Leuven, June 2016
• 40th Symposium of the Spanish Economic Association, Girona, December 2015