Imperial College London

ProfessorPaoloZaffaroni

Business School

Professor of Financial Econometrics
 
 
 
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Contact

 

+44 (0)20 7594 9186p.zaffaroni

 
 
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Location

 

Business School BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

28 results found

Raponi V, Robotti C, Zaffaroni P, 2020, Testing Beta-Pricing Models Using Large Cross-Sections, REVIEW OF FINANCIAL STUDIES, Vol: 33, Pages: 2796-2842, ISSN: 0893-9454

Journal article

, 2019, TIME SERIES IN HIGH DIMENSIONS The General Dynamic Factor Model, ISBN: 9789813278004

Book

Forni M, Hallin M, Lippi M, Zaffaroni Pet al., 2017, Dynamic factor models with infinite-dimensional factor space: asymptotic analysis, Journal of Econometrics, Vol: 199, Pages: 74-92, ISSN: 0304-4076

Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni et al., (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni et al. (2004). Those estimators, however, rely on Brillinger’s concept of dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the factors has finite dimension, which severely restricts their generality—prohibiting, for instance, autoregressive factor loadings. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni et al., (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional factor space. A Monte Carlo experiment and an empirical exercise on US macroeconomic data corroborate those theoretical results and demonstrate the excellent performance of those estimators in out-of-sample forecasting.

Journal article

Wu WB, Zaffaroni P, 2017, Asymptotic theory for spectral density estimates of general multivariate time series, Econometric Theory, Vol: 34, Pages: 1-22, ISSN: 0266-4666

We derive uniform convergence results of lag-window spectral density estimates for a general class of multivariate stationary processes represented by an arbitrary measurable function of iid innovations. Optimal rates of convergence, that hold as both the time series and the cross section dimensions diverge, are obtained under mild and easily verifiable conditions. Our theory complements earlier results, most of which are univariate, which primarily concern in-probability, weak or distributional convergence, yet under a much stronger set of regularity conditions, such as linearity in iid innovations. Based on cross spectral density functions, we then propose a new test for independence between two stationary time series. We also explain the extent to which our results provide the foundation to derive the double asymptotic results for estimation of generalized dynamic factor models.

Journal article

Golinski A, Zaffaroni P, 2016, Long memory affine term structure models, Journal of Econometrics, Vol: 191, Pages: 33-56, ISSN: 0304-4076

We develop a Gaussian discrete time essentially affine term structure model with long memory state variables. This feature reconciles the strong persistence observed in nominal yields and inflation with the theoretical implications of affine models, especially for long maturities. We characterize in closed-form the dynamic and cross-sectional implications of long memory for our model. We explain how long memory can naturally arise within the term structure of interest rates, providing a theoretical underpinning for our model. Despite the infinite-dimensional structure that long memory implies, we show how to cast the model in state space and estimate it by maximum likelihood. An empirical application of our model is presented.

Journal article

Robinson PM, Zaffaroni P, 2016, Modelling Nonlinearity and Long Memory in Time Series - (Now published in Nonlinear Dynamics and Time Series, C D Cutler and D T Kaplan (eds), Fields Institute Communications, 11 (1997), pp.`61-170.)

We discuss models that impart a form of long memory in raw time series xt or instantaneous functions thereof, in particular . on the basis of a linear or nonlinear model. The capacity of linear models for xt to imply long-memory in nonlinear functions of xt is discussed. Empirical observation motivates investigation of models which lead to short memory, or even white noise, xt but a long memory . One such model which we describe is based on the long memory generalized ARCH model introduced by Robinson (1991b). The other is an extension of the nonlinear moving average model of Robinson (1977).

Working paper

Forni M, Hallin M, Lippi M, Zaffaroni Pet al., 2013, Dynamic Factor Models with Infinite-Dimensional Factor Space: One-Sided Estimation, Journal of Econometrics

Factor model methods recently have become extremely popular in the theory andpractice of large panels of time series data. Those methods rely on various factor models which all are particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000). That paper, however, relies on Brillinger's dynamic principal components. The corresponding estimators are two-sided lters whose performance at the end of the observation period or for forecasting purposes is rather poor. No such problem arises with estimators based on standard principal components, which have been dominant in this literature. On the other hand, those estimators require the assumption that the space spanned by the factors has nite dimension. In the present paper, we argue that such an assumption is extremely restrictive and potentially quite harmful. Elaborating upon recent results by Anderson and Deistler (2008a, b) on singular stationary processes with rational spectrum, we obtain one-sided representations for the GDFM without assuming finite dimension of the factor space. Construction of the corresponding estimators is also briefly outlined. In a companion paper, we establish consistency and rates for such estimators, and provide Monte Carlo results further motivating our approach.

Journal article

Avarucci M, Beutner E, Zaffaroni P, 2013, ON MOMENT CONDITIONS FOR QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF MULTIVARIATE ARCH MODELS, ECONOMETRIC THEORY, Vol: 29, Pages: 545-566, ISSN: 0266-4666

Journal article

Forni M, Hallin M, Lippi M, Zaffaroni Pet al., 2011, One-Sided Representations of Generalized Dynamic Factor Models

Factor model methods recently have become extremely popular in the theory and practice of large panels of time series data. Those methods rely on various factor models which all are particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000). In that paper, however, estimation relies on Brillinger’s concept of dynamic principal components, which produces filters that are in general two-sided and therefore yield poor performances at the end of the observation period and hardly can be used for forecasting purposes. In the present paper, we remedy this problem, and show how, based on recent results on singular stationary processes with rational spectra, one-sided estimators are possible for the parameters and the common shocks in the GDFM. Consistency is obtained, along with rates. An empirical section, based on US macroeconomic time series, compares estimates based on our model with those based on the usual staticrepresentation restriction, and provide convincing evidence that the assumptions underlying the latter are not supported by the data.

Working paper

Zaffaroni P, 2009, Whittle estimation of EGARCH and other exponential volatility models, JOURNAL OF ECONOMETRICS, Vol: 151, Pages: 190-200, ISSN: 0304-4076

Journal article

Pesaran MH, Schleicher C, Zaffaroni P, 2009, Model averaging in risk management with an application to futures markets, JOURNAL OF EMPIRICAL FINANCE, Vol: 16, Pages: 280-305, ISSN: 0927-5398

Journal article

Altissimo F, Mojon B, Zaffaroni P, 2009, Can aggregation explain the persistence of inflation?, JOURNAL OF MONETARY ECONOMICS, Vol: 56, Pages: 231-241, ISSN: 0304-3932

Journal article

Pesaran MH, Zaffaroni P, 2009, Optimality and Diversifiability of Mean Variance and Arbitrage Pricing Portfolios

This paper investigates the limit properties of mean-variance (mv) and arbitrage pricing (ap) trading strategies using a general dynamic factor model, as the number of assets diverge to infinity. It extends the results obtained in the literature for the exact pricing case to two other cases of asymptotic no-arbitrage and the unconstrained pricing scenarios. The paper characterizes the asymptotic behaviour of the portfolio weights and establishes that in the non-exact pricing cases the ap and mv portfolio weights are asymptotically equivalent and, moreover, functionally independent of the factors conditional moments. By implication, the paper sheds light on a number of issues of interest such as the prevalence of short-selling, the number of dominant factors and the granularity property of the portfolio weights.

Working paper

Zaffaroni P, 2008, Large-scale volatility models: Theoretical properties of professionals' practice, JOURNAL OF TIME SERIES ANALYSIS, Vol: 29, Pages: 581-599, ISSN: 0143-9782

Journal article

Pesaran MH, Schleicher C, Zaffaroni P, 2008, Model Averaging in Risk Management with an Application to Futures Markets

This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for individual as well as `average' models. The asymptotic as well as the exact ¯nite-sample distribution of the test statistic, dealing with the possibility of parameter uncertainty, are established. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily returns on six currencies, four equity indices, four ten year government bonds and four commodities over the period 1991-2007. The empirical evidence supports the use of `thick' model averaging strategies over single models or Bayesian type model averaging procedures.

Working paper

Hidalgo J, Zaffaroni P, 2007, A goodness-of-fit test for ARCH(infinity) models, JOURNAL OF ECONOMETRICS, Vol: 141, Pages: 835-875, ISSN: 0304-4076

Journal article

Hidalgo J, Zaffaroni P, 2007, A goodness-of-fit test for ARCH(infinity) models, JOURNAL OF ECONOMETRICS, Vol: 141, Pages: 973-1013, ISSN: 0304-4076

Journal article

Altissimo F, Mojon B, Zaffaroni P, 2007, Fast micro und slow macro: can aggregation explain the persistence of inflation?

An aggregation exercise is proposed that aims at investigating whether the fast average adjustment of thedisaggregate inflation series of the euro area CPI translates into the slow adjustment of euro area aggregate inflation. We first estimate a dynamic factor model for 404 inflation sub-indices of the euro area CPI. This allows to decompose the dynamics ofinflation sub-indices in two parts: one due to a common "macroeconomic" shock and one due to sector specific "idiosyncratic" shocks. Although "idiosyncratic" shocks dominate the variance of sectoral prices, one common factor, which accounts for 30 per cent of the overall variance of the 404 disaggregate in.ation series, is the main driver of aggregate dynamics. In addition, the heterogenous propagation of this common shock across sectoral inflation rates, and in particular its slow propagation to inflation rates of services, generates the persistence of aggregate in.ation. We conclude that the aggregation process explains a fair amount ofaggregate in.ation persistence.JEL Classification: E31; E32.

Working paper

Zaffaroni P, 2007, Aggregation and memory of models of changing volatility, JOURNAL OF ECONOMETRICS, Vol: 136, Pages: 237-249, ISSN: 0304-4076

Journal article

Robinson PM, Zaffaroni P, 2006, Pseudo-maximum likelihood estimation of ARCH(infinity) models, ANNALS OF STATISTICS, Vol: 34, Pages: 1049-1074, ISSN: 0090-5364

Journal article

Zaffaroni P, 2006, Contemporaneous Aggregation of GARCH Processes, Journal of Time Series Analysis, Vol: 28, Pages: 521-544

Journal article

Zaffaroni P, 2004, Contemporaneous aggregation of linear dynamic models in large economies, Journal of Econometrics, Vol: 120, Pages: 75-102, ISSN: 0304-4076

Journal article

Zaffaroni P, 2004, Stationarity and memory of ARCH(infinity) models, ECONOMETRIC THEORY, Vol: 20, Pages: 147-160, ISSN: 0266-4666

Journal article

Zaffaroni P, d'Italia B, 2003, Gaussian inference on certain long-range dependent volatility models, JOURNAL OF ECONOMETRICS, Vol: 115, Pages: 199-258, ISSN: 0304-4076

Journal article

Zaffaroni P, Henry M, 2003, The long range dependence paradigm for macroeconomics and finance, Theory and applications of long-range dependence, Editors: Doukhan, Oppenheim, Taqqu, Publisher: Birkhauser, ISBN: 9780817641689

Book chapter

Michelacci C, Zaffaroni P, 2000, (Fractional) beta convergence, JOURNAL OF MONETARY ECONOMICS, Vol: 45, Pages: 129-153, ISSN: 0304-3932

Journal article

Robinson PM, Zaffaroni P, 1998, Nonlinear time series with long memory: a model for stochastic volatility, JOURNAL OF STATISTICAL PLANNING AND INFERENCE, Vol: 68, Pages: 359-371, ISSN: 0378-3758

Journal article

Zaffaroni P, Robinson PM, 1997, Modelling nonlinearity and long memory in time series, Nonlinear dynamics and times series, Editors: Cutler, Kaplan, Publisher: American Mathematical Society

Book chapter

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