Professor Valentina Corradi

Professor of Econometrics

Qualifications: BA, MA, PhD

Email:
Phone: Work: 01483 68 3914
Room no: 35 AD 00

Office hours

Tuesday 10:30 am - 12:30 pm

Further information

Biography

Valentina Corradi obtained a PhD in Economics in 1994 at the University of California, San Diego. She held positions at University of Pennsylvania, Queen Mary-University of London, University of Exeter and University of Warwick. 

Her work has been published on Journal of Econometrics, Econometric Theory,  Journal of the American Statistical Association, Review of Economic Studies, International Economic Review and Journal of Monetary Economics.  

Valentina's current research interests include: (i) modelling and testing for jumps in financial assets (ii) evaluation of trading strategies (iii) financial analysts bias (iv) bandwidth selection for non-stationary processes (v) heaping and measurement error in child mortality data.

Research Interests

  • Econometric Theory
  • Financial Econometrics
  • Time Series: Predictive evaluation
  • Realized measures and Jumps
  • Data driven procedure for bandwidth selection
  • Moment inequalities
  • Factor Models
  • Conditional CAPM.

Publications

Journal articles

  • Bandi FM, Corradi V. (2014) 'Nonparametric nonstationarity tests'. Econometric Theory, 30 (1), pp. 127-149.

    Abstract

    We propose additive functional-based nonstationarity tests that exploit the different divergence rates of the occupation times of a (possibly nonlinear) process under the null of nonstationarity (stationarity) versus the alternative of stationarity (nonstationarity). We consider both discrete-time series and continuous-time processes. The discrete-time case covers Harris recurrent Markov chains and integrated processes. The continuous-time case focuses on Harris recurrent diffusion processes. Notwithstanding finite-sample adjustments discussed in the paper, the proposed tests are simple to implement and rely on tabulated critical values. Simulations show that their size and power properties are satisfactory. Our robustness to nonlinear dynamics provides a solution to the typical inconsistency problem between assumed linearity of a time series for the purpose of nonstationarity testing and subsequent nonlinear inference. Copyright © Cambridge University Press 2013 A ̂.

  • Corradi V, Swanson NR. (2014) 'Testing for structural stability of factor augmented forecasting models'. Journal of Econometrics,

    Abstract

    Mild factor loading instability, particularly if sufficiently independent across the different constituent variables, does not affect the estimation of the number of factors, nor subsequent estimation of the factors themselves (see e.g.  Stock and Watson (2009)). This result does not hold in the presence of large common breaks in the factor loadings, however. In this case, information criteria overestimate the number of breaks. Additionally, estimated factors are no longer consistent estimators of "true" factors. Hence, various recent research papers in the diffusion index literature focus on testing the constancy of factor loadings. However, forecast failure of factor augmented models can be due to either factor loading instability, regression coefficient instability, or both. To address this issue, we develop a test for the joint hypothesis of structural stability of both factor loadings and factor augmented forecasting model regression coefficients. Our proposed test statistic has a chi-squared limiting distribution, and we are able to establish the first order validity of (block) bootstrap critical values. Empirical evidence is also presented for 11 US macroeconomic indicators. © 2014 Elsevier B.V. All rights reserved.

  • Corradi V, Distaso W, Mele A. (2013) 'Macroeconomic determinants of stock volatility and volatility premiums'. Journal of Monetary Economics, 60 (2), pp. 203-220.

    Abstract

    How does stock market volatility relate to the business cycle? We develop, and estimate, a no-arbitrage model, and find that (i) the level and fluctuations of stock volatility are largely explained by business cycle factors and (ii) some unobserved factor contributes to nearly 20% to the overall variation in volatility, although not to its ups and downs. Instead, this "volatility of volatility" relates to the business cycle. Finally, volatility risk-premiums are strongly countercyclical, even more than stock volatility, and partially explain the large swings of the VIX index during the 2007-2009 subprime crisis, which our model captures in out-of-sample experiments. © 2012 Elsevier B.V.

  • Corradi V, Distaso W, Fernandes M. (2012) 'International market links and volatility transmission'. Journal of Econometrics, 170 (1), pp. 117-141.

    Abstract

    This paper gauges volatility transmission between stock markets by testing conditional independence of their volatility measures. In particular, we check whether the conditional density of the volatility changes if we further condition on the volatility of another market. We employ nonparametric methods to estimate the conditional densities and model-free realized measures of volatility, allowing for both microstructure noise and jumps. We establish the asymptotic normality of the test statistic as well as the first-order validity of the bootstrap analog. Finally, we uncover significant volatility spillovers between the stock markets in China, Japan, UK and US. © 2012 Elsevier B.V. All rights reserved.

  • Corradi V, Distaso W, Swanson NR. (2011) 'Predictive inference for integrated volatility'. Journal of the American Statistical Association, 106 (496), pp. 1496-1512.

    Abstract

    Numerous volatility-based derivative products have been engineered in recent years. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this article we propose nonparametric estimators of the aforementioned quantities, based on model-free volatility estimators. We establish consistency and asymptotic normality for the feasible estimators and study their finite-sample properties through a Monte Carlo experiment. Finally, using data from the New York Stock Exchange, we provide an empirical application to volatility directional predictability. © 2011 American Statistical Association.

  • Corradi V, Swanson NR. (2011) 'Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models'. Journal of Econometrics, 161 (2), pp. 304-324.

    Abstract

    This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation-based framework for constructing predictive densities for one-factor and stochastic volatility models. We then construct tests that are in the spirit of Diebold and Mariano (1995) and White (2000). In order to establish the asymptotic properties of our tests, we also develop a recursive variant of the nonparametric simulated maximum likelihood estimator of Fermanian and Salani (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared. © 2011 Elsevier B.V. All rights reserved.

  • Corradi V, Distaso W, Swanson NR. (2009) 'Predictive density estimators for daily volatility based on the use of realized measures'. Journal of Econometrics, 150 (2), pp. 119-138.

    Abstract

    The main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen et al. [Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P., 2003. Modelling and forecasting realized volatility. Econometrica 71, 579-626], and by Andersen et al. [Andersen, T.G., Bollerslev, T., Meddahi, N., 2004. Analytic evaluation of volatility forecasts. International Economic Review 45, 1079-1110; Andersen, T.G., Bollerslev, T., Meddahi, N., 2005. Correcting the errors: Volatility forecast evaluation using high frequency data and realized volatilities. Econometrica 73, 279-296], who address the issue of pointwise prediction of volatility via ARMA models, based on the use of realized volatility. Our approach is to use a realized volatility measure to construct a non-parametric (kernel) estimator of the predictive density of daily volatility. We show that, by choosing an appropriate realized measure, one can achieve consistent estimation, even in the presence of jumps and microstructure noise in prices. More precisely, we establish that four well known realized measures, i.e. realized volatility, bipower variation, and two measures robust to microstructure noise, satisfy the conditions required for the uniform consistency of our estimator. Furthermore, we outline an alternative simulation based approach to predictive density construction. Finally, we carry out a simulation experiment in order to assess the accuracy of our estimators, and provide an empirical illustration that underscores the importance of using microstructure robust measures when using high frequency data. © 2009 Elsevier B.V. All rights reserved.

  • Corradi V, Fernandez A, Swanson NR. (2009) 'Information in the revision process of real-time datasets'. Journal of Business and Economic Statistics, 27 (4), pp. 455-467.

    Abstract

    Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests that have power against generic nonlinear alternatives. A Monte Carlo study shows that the suggested tests have good finite sample properties. Additionally, we carry out an empirical illustration using a real-time dataset for money, output, and prices. Overall, we find evidence against data rationality for output and prices, but not for money. © 2009 American Statistical Association.

  • Awartani B, Corradi V, Distaso W. (2009) 'Assessing market microstructure effects via realized volatility measures with an application to the dow Jones industrial average stocks'. Journal of Business and Economic Statistics, 27 (2), pp. 251-265.

    Abstract

    Transaction prices of financial assets are contaminated by market microstructure effects. This is particularly relevant when estimating volatility using high frequency data. In this article, we assess statistically the effect of microstructure noise on volatility estimators, and test the hypothesis that its variance is independent of the sampling frequency. We provide evidence based on the Dow Jones Industrial Average stocks.We find that noise has a statistically significant effect on volatility estimators at frequencies of 2-3 min or higher. The independently and identically distributed specification with constant variance seems to be a plausible model for microstructure noise, except for ultra high frequencies. © 2009 American Statistical Association.

  • Corradi V, Iglesias EM. (2008) 'Bootstrap refinements for QML estimators of the GARCH(1,1) parameters'. Journal of Econometrics, 144 (2), pp. 500-510.

    Abstract

    This paper reconsiders a block bootstrap procedure for Quasi Maximum Likelihood estimation of GARCH models, based on the resampling of the likelihood function, as proposed by Gonçalves and White [2004. Maximum likelihood and the bootstrap for nonlinear dynamic models. Journal of Econometrics 119, 199-219]. First, we provide necessary conditions and sufficient conditions, in terms of moments of the innovation process, for the existence of the Edgeworth expansion of the GARCH(1,1) estimator, up to the k-th term. Second, we provide sufficient conditions for higher order refinements for equally tailed and symmetric test statistics. In particular, the bootstrap estimator based on resampling the likelihood has the same higher order improvements in terms of error in the rejection probabilities as those in Andrews [2002. Higher-order improvements of a computationally attractive k-step bootstrap for extremum estimators. Econometrica 70, 119-162]. © 2008 Elsevier B.V. All rights reserved.

  • Bhardwaj G, Corradi V, Swanson NR. (2008) 'A simulation-based specification test for diffusion processes'. Journal of Business and Economic Statistics, 26 (2), pp. 176-193.

    Abstract

    This article makes two contributions. First, we outline a simple simulation-based framework for constructing conditional distributions for multifactor and multidimensional diffusion processes, for the case where the functional form of the conditional density is unknown. The distributions can be used, for example, to form predictive confidence intervals for time period t + τ, given information up to period t. Second, we use the simulation-based approach to construct a test for the correct specification of a diffusion process. The suggested test is in the spirit of the conditional Kolmogorov test of Andrews. However, in the present context the null conditional distribution is unknown and is replaced by its simulated counterpart. The limiting distribution of the test statistic is not nuisance parameter-free. In light of this, asymptotically valid critical values are obtained via appropriate use of the block bootstrap. The suggested test has power against a larger class of alternatives than tests that are constructed using marginal distributions/ densities. The findings of a small Monte Carlo experiment underscore the good finite sample properties of the proposed test, and an empirical illustration underscores the ease with which the proposed simulation and testing methodology can be applied. © 2008 American Statistical Association.

  • Corradi V, Sarin R. (2008) 'Corrigendum to "Continuous approximations of stochastic evolutionary game dynamics" [J. Econ. Theory 94 (2000) 163-191].'. J. Economic Theory, 140 Article number 1 , pp. e2-e4.
  • Corradi V, Swanson NR. (2007) 'Evaluation of dynamic stochastic general equilibrium models based on distributional comparison of simulated and historical data'. Journal of Econometrics, 136 (2), pp. 699-723.

    Abstract

    We take as a starting point the existence of a joint distribution implied by different dynamic stochastic general equilibrium (DSGE) models, all of which are potentially misspecified. Our objective is to compare "true" joint distributions with ones generated by given DSGEs. This is accomplished via comparison of the empirical joint distributions (or confidence intervals) of historical and simulated time series. The tool draws on recent advances in the theory of the bootstrap, Kolmogorov type testing, and other work on the evaluation of DSGEs, aimed at comparing the second order properties of historical and simulated time series. We begin by fixing a given model as the "benchmark" model, against which all "alternative" models are to be compared. We then test whether at least one of the alternative models provides a more "accurate" approximation to the true cumulative distribution than does the benchmark model, where accuracy is measured in terms of distributional square error. Bootstrap critical values are discussed, and an illustrative example is given, in which it is shown that alternative versions of a standard DSGE model in which calibrated parameters are allowed to vary slightly perform equally well. On the other hand, there are stark differences between models when the shocks driving the models are assigned non-plausible variances and/or distributional assumptions. © 2005 Elsevier B.V. All rights reserved.

  • Corradi V, Swanson NR. (2007) 'Nonparametric bootstrap procedures for predictive inference based on recursive estimation schemes'. International Economic Review, 48 (1), pp. 67-109.

    Abstract

    We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out-of-sample nonlinear Granger causality, and in the other we outline a test for selecting among multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (Journal of Applied Econometrics 14 (1999), 491-510), within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation. © 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.

  • Corradi V, Swanson NR. (2006) 'Predictive density and conditional confidence interval accuracy tests'. Journal of Econometrics, 135 (1-2), pp. 187-228.

    Abstract

    This paper outlines testing procedures for assessing the relative out-of-sample predictive accuracy of multiple conditional distribution models. The tests that are discussed are based on either the comparison of entire conditional distributions or the comparison of predictive confidence intervals. We also briefly survey existing related methods in the area of predictive density evaluation, including methods based on the probability integral transform and the Kullback-Leibler Information Criterion. The procedures proposed in this paper are similar in many ways to [Andrews', 1997. A conditional Kolmogorov test. Econometrica 65, 1097-1128.] conditional Kolmogorov test and to [White's, 2000. A reality check for data snooping. Econometrica 68, 1097-1126.] reality check. In particular, a predictive density test is outlined that involves comparing square (approximation) errors associated with models i, i = 1, ..., n, by constructing weighted averages over U of E ((Fi (u | Zt, θi †) - F0 (u | Zt, θ0))2), where F0 (· | ·) and Fi (· | ·) are true and model-i distributions, u ∈ U, and U is a possibly unbounded set on the real line. A conditional confidence interval version of this test is also outlined, and appropriate bootstrap procedures for obtaining critical values when predictions used in the formation of the test statistics are obtained via rolling and recursive estimation schemes are developed. An empirical illustration comparing alternative predictive models for U.S. inflation is given for the predictive confidence interval test. © 2005 Elsevier B.V. All rights reserved.

  • Corradi V, Swanson NR. (2006) 'Bootstrap conditional distribution tests in the presence of dynamic misspecification'. Journal of Econometrics, 133 (2), pp. 779-806.

    Abstract

    In this paper, we show the first order validity of the block bootstrap for Kolmogorov-type conditional distribution tests under dynamic misspecification and parameter estimation error. Our approach is unique because we construct statistics that allow for dynamic misspecification under both hypotheses. We consider two tests; the CK test of Andrews [1997. A conditional Kolmogorov test, Econometrica 65, 1097-1128], and a version of the DGT test of Diebold, Gunther and Tay [1998a. Evaluating density forecasts with applications to finance and management. International Economic Review 39, 863-883]. Test limiting distributions are Gaussian processes with covariance kernels that reflect dynamic misspecification and parameter estimation error. Critical values are based on an extension of the empirical process version of the block bootstrap to the case of nonvanishing parameter estimation error. Monte Carlo experiments are also carried out. © 2005 Elsevier B.V. All rights reserved.

  • Corradi V, Distaso W. (2006) 'Semi-parametric comparison of stochastic volatility models using realized measures'. Review of Economic Studies, 73 (3), pp. 635-667.

    Abstract

    This paper proposes a procedure to test for the correct specification of the functional form of the volatility process within the class of eigenfunction stochastic volatility models. The procedure is based on the comparison of the moments of realized volatility measures with the corresponding ones of integrated volatility implied by the model under the null hypothesis. We first provide primitive conditions on the measu rement error associated with the realized measure, which allow to construct asymptotically valid specification tests. Then we establish regularity conditions under which the considered realized measures, namely, realized volatility, bipower variation, and modified subsampled realized volatility, satisfy the given primitive assumptions. Finally, we provide an empirical illustration based on thr ee stocks from the Dow Jones Industrial Average. © 2006 The Review of Economic Studies Limited.

  • Corradi V, Swanson NR. (2006) 'The effect of data transformation on common cycle, cointegration, and unit root tests: Monte Carlo results and a simple test'. Journal of Econometrics, 132 (1), pp. 195-229.

    Abstract

    Cointegration, common cycle, and related tests statistics are often constructed using logged data, even without clear reason why logs should be used rather than levels. Unfortunately, it is also the case that standard data transformation tests, such as those based on Box-Cox transformations, cannot be shown to be consistent unless assumptions concerning whether variables I ( 0 ) or I ( 1 ) are made. In this paper, we propose a simple randomized procedure for choosing between levels and log-levels specifications in the (possible) presence of deterministic and/or stochastic trends, and discuss the impact of incorrect data transformation on common cycle, cointegration and unit root tests. © 2005 Elsevier B.V. All rights reserved.

  • Corradi V, Swanson NR. (2006) 'Chapter 5 Predictive Density Evaluation'. Handbook of Economic Forecasting, 1, pp. 197-284.

    Abstract

    This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various authors including Christoffersen and Diebold [Christoffersen, P., Diebold, F.X. (2000). "How relevant is volatility forecasting for financial risk management?". Review of Economics and Statistics 82, 12-22], Diebold, Gunther and Tay [Diebold, F.X., Gunther, T., Tay, A.S. (1998). "Evaluating density forecasts with applications to finance and management". International Economic Review 39, 863-883], Diebold, Hahn and Tay [Diebold, F.X., Hahn, J., Tay, A.S. (1999). "Multivariate density forecast evaluation and calibration in financial risk management: High frequency returns on foreign exchange". Review of Economics and Statistics 81, 661-673], White [White, H. (2000). "A reality check for data snooping". Econometrica 68, 1097-1126], Bai [Bai, J. (2003). "Testing parametric conditional distributions of dynamic models". Review of Economics and Statistics 85, 531-549], Corradi and Swanson [Corradi, V., Swanson, N.R. (2005a). "A test for comparing multiple misspecified conditional distributions". Econometric Theory 21, 991-1016; Corradi, V., Swanson, N.R. (2005b). "Nonparametric bootstrap procedures for predictive inference based on recursive estimation schemes". Working Paper, Rutgers University; Corradi, V., Swanson, N.R. (2006a). "Bootstrap conditional distribution tests in the presence of dynamic misspecification". Journal of Econometrics, in press; Corradi, V., Swanson, N.R. (2006b). "Predictive density and conditional confidence interval accuracy tests". Journal of Econometrics, in press], Hong and Li [Hong, Y.M., Li, H.F. (2003). "Nonparametric specification testing for continuous time models with applications to term structure of interest rates". Review of Financial Studies, 18, 37-84], and others are reviewed. Extensions of some existing techniques to the case of out-of-sample evaluation are also provided, and asymptotic results associated with these extensions are outlined. © 2006 Elsevier B.V. All rights reserved.

Conference papers

  • Corradi V, Swanson NR. (2014) 'Testing for structural stability of factor augmented forecasting models'. Journal of Econometrics, 182 (1), pp. 100-118.

    Abstract

    Mild factor loading instability, particularly if sufficiently independent across the different constituent variables, does not affect the estimation of the number of factors, nor subsequent estimation of the factors themselves (see e.g. Stock and Watson (2009)). This result does not hold in the presence of large common breaks in the factor loadings, however. In this case, information criteria overestimate the number of breaks. Additionally, estimated factors are no longer consistent estimators of "true" factors. Hence, various recent research papers in the diffusion index literature focus on testing the constancy of factor loadings. However, forecast failure of factor augmented models can be due to either factor loading instability, regression coefficient instability, or both. To address this issue, we develop a test for the joint hypothesis of structural stability of both factor loadings and factor augmented forecasting model regression coefficients. Our proposed test statistic has a chi-squared limiting distribution, and we are able to establish the first order validity of (block) bootstrap critical values. Empirical evidence is also presented for 11 US macroeconomic indicators. © 2014 Elsevier B.V. All rights reserved.

Book chapters

  • Corradi V, Distaso W. (2012) 'Multiple Forecast Model Evaluation'. in (ed.) The Oxford Handbook of Economic Forecasting

    Abstract

    © 2011 by Oxford University Press. All rights reserved. This article focuses on recent developments in the forecasting literature on how to simultaneously control both the overall error rate and the contribution of irrelevant models. As a novel contribution, it derives a general class of superior predictive ability tests, which controls for family-wise error rate (FWER) and the contribution of irrelevant models. The article is organized as follows. Section 2 defines the setup. Section 3 reviews the approaches that control for the conservative FWER. Section 4 considers a general class of tests characterized by multiple joint inequalities. Section 5 presents results allowing for control of the less conservative false discovery rate. Section 6 considers the model confidence set approach and offers a simple alternative that reduces the influence of irrelevant models in the initial set. Section 7 briefly reviews the empirical evidence, while Section 8 concludes.

Working Papers

  • Corradi V, Silvapulle MJ, Swanson NR. (2014) Consistent Pretesting for Jumps. Working Paper,
    [ Status: Submitted ]

Teaching

  • Econometrics for PhDs.

Departmental Duties

  • PhD Programme Director.

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