CEMFI

About

Dante Amengual is Associate Professor of Economics at CEMFI in Madrid, Spain. He earned his PhD in economics from Princeton University in 2009 after completing a degree in Economics at the Universidad de la República in Montevideo, Uruguay, and obtaining a diploma on graduate studies at CEMFI. His research interests include time series econometrics and asset pricing.

Current Research

Teaching

Publications

Papers

Endogenous health groups and heterogeneous dynamics of the elderly

Joint with Jesús Bueren and Julio A. Crego

Forthcoming in the Journal of Applied Econometrics

We propose a methodology to classify individuals into few but meaningful health groups by estimating a panel Markov switching model that exploits rich information from panel household surveys. Using the HRS, we identify four persistent health groups, depending on individual's physical and mental disabilities. Our classification outperforms existing health measures at explaining entry in nursing homes, home health care, out-of-pocket medical expenses, and mortality for individuals in the HRS, ELSA, and SHARE. Through a workhorse model of savings, we recover an asset cost of bad health that is twice as big as when using self-reported health.

Testing distributional assumptions using a continuum of moments

Joint with Marine Carrasco and Enrique Sentana

Journal of Econometrics, 218 (2), pp. 655-689, October 2020.

We propose specification tests for parametric distributions that compare the potentially complex theoretical and empirical characteristic functions using the continuum of moment conditions analogue to an overidentifying restrictions test, which takes into account the correlation between influence functions for different argument values. We derive its asymptotic distribution for fixed regularization parameter and when this vanishes with the sample size. We show its consistency against any deviation from the null, study its local power and compare it with existing tests. An extensive Monte Carlo exercise confirms that our proposed tests display good power in finite samples against a variety of alternatives.

Is a normal copula the right copula?

Joint with Enrique Sentana

Journal of Business and Economic Statistics, 38 (2), pp. 350-366, April 2020.

We derive computationally simple and intuitive expressions for score tests of Gaussian copulas against Generalized Hyperbolic alternatives, including symmetric and asymmetric Student t, and many other examples. We decompose our tests into third and fourth moment components, and obtain one-sided Likelihood Ratio analogues, whose standard asymptotic distribution we provide. Our Monte Carlo exercises confirm the reliable size of parametric bootstrap versions of our tests, and their substantial power gains over alternative procedures. In an empirical application to CRSP stocks, we find that short-term reversals and momentum effects are better captured by non-Gaussian copulas, whose parameters we estimate by indirect inference.

Normality tests for latent variables

Joint with Martín Almuzara and Enrique Sentana

Quantitative Economics, 10 (3), pp. 981-1017, July 2019

We exploit the rationale behind the Expectation Maximization algorithm to derive simple to implement and interpret LM normality tests for the innovations of the latent variables in linear state space models against generalized hyperbolic alternatives, including symmetric and asymmetric Student ts. We decompose our tests into third and fourth moment components, and obtain one‐sided likelihood ratio analogues, whose asymptotic distribution we provide. When we apply our tests to a common trend model which combines the expenditure and income versions of US aggregate real output to improve its measurement, we reject normality if the sample period extends beyond the Great Moderation.

Resolution of policy uncertainty and sudden declines in volatility

Joint with Dacheng Xiu

Journal of Econometrics, 203 (2), pp. 297–315, April 2018

We introduce downward volatility jumps into a general non-affine modeling framework of the term structure of variance. With variance swaps and S&P 500 returns, we find that downward volatility jumps are associated with a resolution of policy uncertainty, mostly through statements from FOMC meetings and speeches of the Federal Reserve’s chairman. Ignoring such jumps may lead to an incorrect interpretation of the tail events, and hence biased estimates of variance risk premia. On the modeling side, we explore the structural differences and relative goodness-of-fits of factor specifications. We find that log-volatility models with at least one Ornstein–Uhlenbeck factor and double-sided jumps are superior in capturing volatility dynamics and pricing variance swaps, compared to the affine model prevalent in the literature or non-affine specifications without downward jumps.

Market-based estimation of stochastic volatility models

Joint with Yacine Aït-Sahalia and Elena Manresa

Journal of Econometrics, 187 (2), pp. 418-435, August 2015

We propose a method for estimating stochastic volatility models by adapting the HJM approach to the case of volatility derivatives. We characterize restrictions that observed variance swap dynamics have to satisfy to prevent arbitrage opportunities. When the drift of variance swap rates are affine under the pricing measure, we obtain closed form expressions for those restrictions and formulas for forward variance curves. Using data on the S&P500 index and variance swap rates on different time to maturities, we find that linear mean-reverting one factor models provide inaccurate representation of the dynamics of the variance swap rates while two-factor models significantly outperform the former both in and out of sample.

Sequential estimators of shape parameters in multivariate dynamic models

Joint with Gabriele Fiorentini and Enrique Sentana

Journal of Econometrics, 177 (2), pp. 233-249, December 2013

Sequential maximum likelihood and GMM estimators of distributional parameters obtained from the standardised innovations of multivariate conditionally heteroskedastic dynamic regression models evaluated at Gaussian PML estimators preserve the consistency of mean and variance parameters while allowing for realistic distributions. We assess their efficiency, and obtain moment conditions leading to sequential estimators as efficient as their joint ML counterparts. We also obtain standard errors for VaR and CoVaR, and analyse the effects on these measures of distributional misspecification. Finally, we illustrate the small sample performance of these procedures through simulations and apply them to analyse the risk of large eurozone banks.

A comparison of mean-variance efficiency tests

Joint with Enrique Sentana

Journal of Econometrics, 154 (1), pp. 16-34, January 2010

We analyse the asymptotic properties of mean–variance efficiency tests based on generalised methods of moments, and parametric and semiparametric likelihood procedures that assume elliptical innovations. We study the trade-off between efficiency and robustness, and prove that the parametric estimators provide asymptotically valid inferences when the conditional distribution of the innovations is elliptical but possibly misspecified and heteroskedastic. We compare the small sample performance of the alternative tests in a Monte Carlo study, and find some discrepancies with their asymptotic properties. Finally, we present an empirical application to US stock returns, which rejects the mean–variance efficiency of the market portfolio.

Consistent estimation of the number of dynamic factors in a large N and T panel

Joint with Mark W. Watson

Journal of Business and Economic Statistics, 25 (1), pp. 91-96, January 2007

Bai and Ng proposed a consistent estimator for the number of static factors in a large N and T approximate factor model. This article shows how the Bai–Ng estimator can be modified to consistently estimate the number of dynamic factors in a restricted dynamic factor model. The modification is straightforward: The standard Bai–Ng estimator is applied to residuals obtained by projecting the observed data onto lagged values of principal-components estimates of the static factors.

Notes, Comments and Contributions to Volumes

Gaussian rank correlation and regression

Joint with Enrique Sentana and Zhanyuan Tian

CEMFI Working Paper 2004, revised July 2020, forthcoming in A. Chudik, C. Hsiao and A. Timmermann (eds.) Essays in Honor of M. Hashem Pesaran, Advances in Econometrics, Emerald.

We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions - OLS applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogues otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model, and momentum and reversal effects in individual stock returns confirm that Gaussian rank procedures are insensitive to outliers.

Comments on "Reflections on the probability space induced by moment conditions with implications for Bayesian inference" by A.R. Gallant

Joint with Enrique Sentana

Journal of Financial Econometrics, 14 (2), pp. 248–252, Spring 2016

Working Papers and Work in Progress

Working Papers

Normal but skewed?

Joint with Xinyue Bei and Enrique Sentana

CEMFI Working Paper 2104, May 2021

We propose a multivariate normality test against skew normal distributions using higher-order log-likelihood derivatives which is asymptotically equivalent to the likelihood ratio but only requires estimation under the null. Numerically, it is the supremum of the univariate skewness coefficient test over all linear combinations of the variables. We can simulate its exact finite sample distribution for any multivariate dimension and sample size. Our Monte Carlo exercises confirm its power advantages over alternative approaches. Finally, we apply it to the joint distribution of US city sizes in two consecutive censuses finding that non-normality is very clearly seen in their growth rates.

Multivariate Hermite polynomials and information matrix tests

Joint with Gabriele Fiorentini and Enrique Sentana

CEMFI Working Paper 2103, May 2021

We show that the information matrix test for a multivariate normal random vector coincides with the sum of the two moment tests that look at the means of all the different third- and fourth-order multivariate Hermite polynomials, respectively. We also explain how to simulate its exact, parameter-free, finite sample distribution to any desired degree of accuracy for any dimension of the random vector and sample size. Specifically, we exploit the numerical invariance of the test statistic to affine transformations of the observed variables to simulate draws extremely quickly.

Hypothesis tests with a repeatedly singular information matrix

Joint with Xinyue Bei and Enrique Sentana

March 2021

We study score-type tests in likelihood contexts in which the nullity of the information matrix under the null is larger than one, thereby generalizing earlier results in the literature. Examples include multivariate skew normal distributions, Hermite expansions of Gaussian copulas, purely non-linear predictive regressions, multiplicative seasonal time series models and multivariate regression models with selectivity. Our proposal, which involves higher order derivatives, is asymptotically equivalent to the likelihood ratio but only requires estimation under the null. We conduct extensive Monte Carlo exercises that study the finite sample size and power properties of our proposal and compare it to alternative approaches.

Moment tests of independent components

Joint with Gabriele Fiorentini and Enrique Sentana

February 2021

We propose simple specification tests for independent component analysis and structural vector autoregressions with non-Gaussian shocks that check the normality of a single shock and the potential cross-sectional dependence among several of them. Our tests compare the integer (product) moments of the shocks in the sample with their population counterparts. Importantly, we explicitly consider the sampling variability resulting from using shocks computed with consistent parameter estimators. We study the finite sample size of our tests in extensive simulation exercises and discuss some bootstrap procedures. We also show that our tests have non-negligible power against a variety of empirically plausible alternatives.

Testing a large number of hypotheses in approximate factor models

Joint with Luca Repetto

CEMFI Working Paper 1410, December 2014

We propose a method to test hypotheses in approximate factor models when the number of restrictions under the null hypothesis grows with the sample size. We use a simple test statistic, based on the sums of squared residuals of the restricted and the unrestricted versions of the model, and derive its asymptotic distribution under different assumptions on the covariance structure of the error term. We show how to standardize the test statistic in the presence of both serial and cross-section correlation to obtain a standard normal limiting distribution. We provide estimators for those quantities that are easy to implement. Finally, we illustrate the small sample performance of these testing procedures through Monte Carlo simulations and apply them to reconsider Reis and Watson (2010)'s hypothesis of existence of a pure inflation factor in the US economy.

Work in Progress

Specification tests for non-Gaussian structural vector autoregressions

Joint with Gabriele Fiorentini and Enrique Sentana

The Life Cycle Implications of Healthy Habits

Joint with Jesús Bueren and Josep Pijoan-Mas

Information matrix tests for Gaussian mixtures

Joint with Gabriele Fiorentini and Enrique Sentana

Tests for random coefficient variation in vector autoregressive models

Joint with Gabriele Fiorentini and Enrique Sentana

GDP Solera: the ideal vintage mix

Joint with Martín Almuzara, Gabriele Fiorentini and Enrique Sentana

Financial contagion in the eurozone

Joint with Julio A. Crego and Enrique Sentana

Testing for structural breaks in approximate factor models

Joint with Alexander Heinemann

Inference in multivariate dynamic models with elliptical innovations

Joint with Enrique Sentana

Old Work

The Term Structure of Variance Risk Premia (2008)

I study volatility dynamics and volatility risk premia under alternative stochastic volatility models. Using daily data on S&P 500 returns and variance swap rates I find that two-factor models, in which the additional factor represents the level to which the spot variance reverts, significantly improve the fit of the term structure of variance derivatives. Market prices of variance shocks are negative and economically large under all the specifications, leading to significant gains from taking short positions in variance swaps. One-factor models may lead to risk premia term structures that understate the impact of the current state of the economy at longer horizons. Moreover, term structures from two-factor models generate different patterns during quiet and turbulent market periods.