Giovanni Urga

Giovanni Urga joined Cass Business School in July 1999 as Senior Lecturer in Financial Econometrics. He was promoted to Reader in May 2001 and to Full Professor in May 2002. He was Director of the PhD Programme and Co-ordinator of the Marie Curie Training Site in "Corporate Finance, Capital Markets and Banking" from 2002-2005. Since 1992, he is Professor of Econometrics at the Economics Department of Bergamo University (Italy). His teaching includes Advanced Financial Econometrics, Advanced Financial Modelling and Forecasting, Econometrics of Financial Markets, and Stationary and Non-Stationary Panel Data Econometrics. Formely, he was Research Fellow at London Business School (1994-1999), Visiting Lecturer at New Economic School in Moscow (1996-1999), Lecturer at Queen Mary and Westfield College in London (1992-1994), and Research Officer at the Institute of Economics and Statistics in Oxford (1991-1992). Professor Urga is referee for the Journal of Applied Econometrics, International Journal of Forecasting, The Economic Journal, Journal of Economics, Economics of Innovation and New Technology, Oxford Bulletin of Economics and Statistics, Oxford Economic Papers, Economic Modelling, Economic Systems, Journal of Comparative Economics, The Econometrics Journal, International Review of Economics and Finance, the Scandanavian Journal of Economics, Journal of Economic Dynamics and Control, Journal of International Money and Finance, Journal Money Credit and Banking, Journal of Business and Economic Statistics. GUEST EDITOR (1) Special Annals Issue of the Journal of Econometrics (2005) on "Modelling Structural Breaks, Long Memory and Stock Market Volatility"; (2) Special Issue of the Journal of Business and Economic Statistics (2007) on "Common Features in London". ASSOCIATE EDITOR: Empirical Economics

Qualifications

BSc in Economics (Pavia University) and PhD (Oxford).

Visiting Appointments

• Professor in Econometrics, Université Panthéon Assas - Paris II CRED 12 Place du Panthéon 75 230 PARIS CEDEX 05, Apr 2015 – present
• Professor in Econometrics, Department of Economics, Bergamo University, Italy, Oct 1992 – present

Memberships of Committees

Member, Scientific and Organizing Committee of the series of International Panel Data Conferences, Jul 2013 – present

Memberships of Professional Organisations

• Regular, American Financial Association and American Statistical Association, Oct 2000 – present
• Regular, Econometrics Society, Oct 1992 – present
• Regular, Royal Economic Society, Oct 1992 – present

Languages

Italian.

Expertise

Primary Topics

• Capital Markets
• Econometrics
• Financial Econometrics
• Fixed-Income Investments
• Risk Management

Additional Topics

• Asset Pricing
• Economics
• International Financial Markets
• Monetary Economics

Industries/Professions

banking

Geographic Areas

• Americas - North
• Asia
• Europe - Eastern
• Europe - Western

Research

• MODELLING AND TESTING FOR JUMPS IN FINANCIAL ASSETS. We use high frequency data (BrokerTec US Treasury data on the 2-, 5-, 10- and 30- year bonds) to examine and compare the results of alternative univariate jump tests recently proposed in the literature, as a first step to evaluate the performance of these tests. The following tests are considered: Aϊt-Sahalia and Jacod (2008), Andersen, Bollerslev and Dobrev (2007), Barndorff-Nielsen and Shephard (2005), Jiang and Oomen (2006), Lee and Mykland (2007) and Mancini (2001). We are interested in identifying which tests are likely to exhibit more power, as well as in determining how the sampling frequency affects the jump identification for different tests. Moreover, we investigate how bond prices react to different types of (scheduled/ non-scheduled) information releases. Dumitru, A. and G. URGA (2012) “Identifying Jumps in Financial Assets: A Comparison between non Parametric Jump Tests”, Journal of Business and Economic Statistics 30, 242-255. Novotny, J., Petrov, D. and G. URGA (2015) “Trading Price Jump Clusters in Foreign Exchange Markets”, Journal of Financial Markets 24, 66-92.

• ASYMPTOTICS AND STRUCTURAL BREAKS IN PANEL MODELS. We develop a novel asymptotic theory for panel models with common shocks. We also propose an estimation and testing framework for parameter instability in cointegrated panel regressions with common and idiosyncratic trends. We develop tests for structural change for the slope parameters under the null hypothesis of no structural break against the alternative hypothesis of (at least) one common change point which is possibly unknown. We derive the limiting distributions of the proposed test statistics. Monte Carlo simulations examine size and power of the proposed tests. Kao, C., Trapani, L. and G. URGA (2012) “The Asymptotic for Panel Models with Common Shocks”, Econometric Reviews 31, 390-439. Kao, C., Trapani, L. and G. URGA (2016) “Testing for Instability in Covariance Structures”. Revise and Resubmit (3rd round) in Bernoulli Journal.

• IDENTIFICATION ROBUST INFERENCE IN COINTEGRATING REGRESSIONS In cointegrating regressions, available estimators and test statistics are nuisance parameter dependent. This paper addresses this problem from an identification-robust perspective with focus on set estimation of the long-run coefficient (denoted β). We propose to invert LR-type statistics that test a specified value for β against an unrestricted or a cointegration-restricted alternative. Tests in implicit form as in Phillips (1994) are also inverted. Allowing for weak identification, we propose three methods to adequately size the considered tests: a bounds-based critical value based on Dufour (1989, 1997) and Dufour and Khalaf (2002), a data-dependent "Type 2 Robust" critical value based on Andrews and Cheng (2013), and a simulation-based method based on Dufour (2006). For two empirically relevant special cases, we provide analytical solutions to the test inversion problem using the mathematics of quadrics as in Dufour and Taamouti (2005). We conduct a simulation study to assess the properties of our proposed inference methods. In addition, we also check whether and to what degree popular estimation methods, specifically the standard Maximum Likelihood of Johansen (1995), the Fully Modified OLS (Phillips and Hansen, 1990; Phillips, 1991, 1995), the Dynamic OLS of Stock and Watson (1993), and the stationarity-test based method from Wright (2000), suffer from this problem, imposing and relaxing strong exogeneity. Simulation results can be summarized as follows. The size of DOLS and FMOLS based t-tests exceeds 90% at the identification boundary. Failure of weak-exogeneity causes severe distortions for DOLS as well as for FMOLS even when β is identified. The test from Wright (2000) is also oversized at the boundary. In contrast, even when weak exogeneity fails, all our proposed LR-based corrections have good size regardless of the identification status, and good power when β is identified. Khalaf, L. and G. URGA (2014) “Identification Robust Inference in Cointegrating Regressions”, Journal of Econometrics 182, 385-396.

• THE IMPACT OF MACRO NEWS ON THE TERM STRUCTURE OF INTEREST RATES. The evaluation of the impact of the news effects is one of the key questions in financial economics and a hot topic in recent studies of macroeconomic analysis. It may not be the act of releasing information to the market which is important, nor the (gross) information embodied in the estimate itself, rather, it is the extent to which the actual announcement differs from the expected which determines the response of the market to the new information (Kim et al. 2004). The aim of this project is to increase the knowledge of the impact of macro news, coming from scheduled macro announcements, on the US interest rates term structure. Boffelli, S. and G. URGA (2014), “Evaluating Correlations in European Government Bond Spreads”, in (eds) Perna, C. and M., Sibillo), Mathematical and Statistical Methods for Actuarial Sciences and Finance, Spring. Boffelli, S. and G. URGA (2015), “Macroannouncements, Bond Auctions and Rating Actions in the European Government Bond Spreads”, Journal of International Money and Finance 53, 148-173. Boffelli, S., Skintzi, V. D. and G. URGA (2016)“High and Low Frequency Correlations in European Government Bond Spreads and Their Macroeconomic Drivers”, Journal of Financial Econometrics (Forthcoming).

• BREAKS AND LONG MEMORY PROCESSES IN ECONOMICS AND FINANCE. We propose a fractional version of two well-known credit risk pricing structural models: the Merton and Black and Cox models. We assume that the value of the firm obeys to a Geometric Fractional Brownian Motion. Prices for the equity, the bond and credit spreads are derived and a sensitivity analysis is performed. To provide a justification for these models, an empirical analysis is carried out, which employs two different datasets: Constant Maturity Yields and Moody’s Long-Term for the period December 1992–November 2003 Corporate Bond Yield Averages and Lehman Brothers Eurodollar Indices covering the period June 1996–July 2006. Long memory properties of Treasury and corporate bond yields as well as credit spreads are thus investigated. Leccadito, a., O. Rachedi, and G. URGA (2015) “Testing for True vs. Spurious Long Memory. Some Theoretical Results and a Monte Carlo Comparison”. Econometric Reviews 34, 452-479.

Research Topics

• MONTE CARLO COMBINED TESTS WITH NUISANCE PARAMETERS (with A. Bianchi, J.M. Dufour, L. Khalaf) The main of this project is to expand Monte Carlo tests to the case of non-identifiable parameters relevant in modelling economic and financial relationships. We plan to adopt the approach of Redner (1981), where the problem is framed in the theory of topological quotient spaces. Mainly for our reference and to clarify precisely the objects we deal with, first we introduce some notions on topological spaces and quotient topological spaces, and convergence of random variables on topological spaces. Next, we define the quotient parameter space as in Redner (1981) and formulate our setting in this context. Then, we will generalize Dufour (2006) results on Monte Carlo tests based on consistent point estimate to the case of non-identifiable parameters. Finally, we will produce the relevant proofs of the validity of the test and interesting applications of relevance to economics and finance.
• EUROPEAN SHADOW BANKING AND SYSTEMIC RISK (with C. Bellavite Pellegrini and M. Meoli) Shadow banking entities have been repeatedly charged with the breaking up of the recent financial crises, as they may have contributed to increase the systemic risk and thus jeopardise the stability of the whole financial system. This project examines the features of the shadow banking system in United Kingdom first and then the whole European banking system and estimate, by using the CoVaR methodology (Adrian and Brunnermeier, 2011, 2014), the contribution of the money market funds, an important part of the shadow banking entities, to the systemic risk in United Kingdom. We plan to investigate the impact of institutional corporate variables on the measure of systemic risk taking into account different financial crises since the sub-prime crisis originated in USA in 2007-2008.
• TIME-VARYING LOADINGS IN HIGH-DIMENTIONAL FACTOR MODELS (with R. Borghi, Eric Hillebrand, J. G. Mikkelsen) The main of this project is to develop a maximum likelihood estimator of time-varying loadings in high-dimensional factor models. We specify the loadings to evolve as stationary vector autoregressions (VAR) and show that consistent estimates of the loadings parameters can be obtained by a two-step maximum likelihood estimation procedure. In the first step, principal components are extracted from the data to form factor estimates. In the second step, the parameters of the loadings VARs are estimated as a set of univariate regression models with time-varying coefficients. We document the finite-sample properties of the maximum likelihood estimator through an extensive simulation study and illustrate the empirical relevance of the time-varying loadings structure using a large quarterly dataset for the US economy. Further empirical applications will be conducted using foreign exchange rates. In addition, this framework will be implemented for portfolios analysis: portfolio managers often use factor models to facilitate the estimation of the covariance matrix for large N portfolios of stocks and this practice helps portfolio optimisation, risk analysis and comovement analysis.
• BACK-TESTING ES TECHNIQUES FOR RISK BASED REGULATORY CAPITAL (A. Leccadito and L. Khalaf) The main aim of this project is to develop finite sample parametric and non-parametric methods, via power-enhancing statistical combinations aiming to harvest the size-correct power advantage of various new and existing tests. Combining information on the number and dynamic evolution of violations holds promise. In fact, when clustering is severe, the number of exceptions have also been observed to typically increase. Incremental information can also be expected by simultaneously testing several VaR probability levels. The project thus will generalize the multilevel tests of Perignon and Smith (2008) and Leccadito et al. (2014). An extensive simulation study will be reported and various empirical applications will be considered.
• COMBINING P-VALUES TO TEST FOR MULTIPLE BREAKS IN THE VECM WITH AND WITHOUT THE PRESENCE OF WEAK-EXOGENEITY (with A. Bianchi, M. Bergamelli, L. Khalaf) The main aim of the project is to develop limiting theory to detect and test for multiple structural breaks in the VECM framework. First, we show that breaks in the long run matrix BETA imply breaks in the short run impact matrix ALPHA, unless weak exogeneity is imposed, and breaks in BETA imply also breaks in the covariance matrix of the error term. Second, we extend the likelihood ratio test proposed in Hansen (2003) to the case of unknown break dates through the specification of several scenarios regarding the number and the location of the breaks. We define a minimum p-value statistic with critical values approximated by bootstrapping. Monte Carlo simulations show that the proposed statistic has optimal finite sample properties when imposing and relaxing weak exogeneity as well as when exploring the impact of weak identification of the cointegrating relationship. A series of applications will illustrate the empirical validity of the framework.
• JUMPS, COJUMPS AND ASYMMETRIC JUMP BETA IN HIGH FREQUENCY DATA (with V. Alexeev, S. Boffelli, J. Novotny) We propose to use a combination of univariate tests for jumps to construct a cojump testing procedure robust to microstructure noise and spurious detection. The proposed test allows us to distinguish between transitory-permanent and endogenous-exogenous co-jumps and determine a causality effect between price and liquidity. In the empirical application, we plan to apply the co-jump testing framework to evaluate the relationship the price and the available liquidity of EUR/USD FX spot during the week from May 3 to May 7, 2010. We also evaluate the impact of extreme market shifts on equity portfolios. Assuming that investors care differently about downside losses as opposed to upside gains, we estimate jump sensitivities for the negative and positive market shifts. We study the implications of the difference in negative and positive sensitivities to market jumps for portfolio risk management by contrasting the results for individual stocks with the results for portfolios with varying number of holdings. In the context of a portfolio, we investigate to what extent the downside and upside jump risks can be diversified away. Varying the jump identification threshold, we show that the asymmetry is more prominent for more extreme events and that the number of holdings required to stabilise portfolios’ sensitivities to negative jumps is higher than under positive jumps. Ignoring this asymmetry results in under-diversification of portfolios and increased exposure to extreme negative market shifts

Books (2)

• Boffelli, S. and Urga, G. (2016). Financial Econometrics Using Stata. Stata Press. ISBN 978-1-59718-214-0.
• Urga, G. The second workshop of the series Investment and Public Policy on Investment decisions: evidence from macro data.

Chapters (6)

• Barone-Adesi, G., Gagliardini, P. and Urga, G. (2015). A Test of the Homogeneity of Asset pricing Models. Multi-moment Asset Allocation and Pricing Models (pp. 223–230). ISBN 978-1-119-20183-0.
• Urga, G. and Boffelli, S. (2014). Evaluating Correlations in European Government Bond Spreads. Mathematical and Statistical Methods for Actuarial Sciences
• and Finance (pp. 35–39). Springer International Publishing. ISBN 978-3-319-05013-3.
• Hall, S. and Urga, G. (2000). New Developments in the Analysis of Panel Data Sets. In Dahiya, S.B. (Ed.), THE CURRENT STATE OF BUSINESS DISCIPLINES, (Business Economics) (pp. 537–564).
• Henry, B., Sentance, A. and Urga, G. (1999). Finance, Profitability, and Investment in Manufacturing. In Driver, C. and Temple, P. (Eds.), Investment, Growth and Employment : Perspectives for Policy (pp. 29–50). Routledge. ISBN 978-0-415-19780-9.
• Nixon, J. and Urga, G. (1999). Unemployment and Capital Stock: Modelling the Supply Side of the UK Economy. In Driver, C. and Temple, P. (Eds.), Investment, Growth and Employment: Perspectives for Policy (pp. 221–248). Routledge. ISBN 978-0-415-19780-9.
• Banerjee, A. and Urga, G. (1996). Investigating Structural Breaks in UK Manufacturing Trade. In Allen, C. and Hall, S. (Eds.), Macroeconomic Modelling in a Changing World John Wiley & Sons. ISBN 978-0-471-95791-1.

Journal Articles (59)

• Kao, C., Trapani, L. and Urga, G. (2018). Testing for instability in covariance structures. Bernoulli, 24(1), pp. 740–771. doi:10.3150/16-BEJ894.
• Bellavite Pellegrini, C., Meoli, M. and Urga, G. (2017). Money market funds, shadow banking and systemic risk in United Kingdom. Finance Research Letters, 21, pp. 163–171. doi:10.1016/j.frl.2017.02.002.
• Mikkelsen, J.G., Hillebrand, E. and Urga, G. (2016). Maximum Likelihood Estimation of Time-Varying Loadings in High-Dimensional Factor Models. Journal of Econometrics .
• Boffelli, S., Skintzi, V.D. and Urga, G. (2016). High- and low-frequency correlations in European government bond spreads and their macroeconomic drivers. Journal of Financial Econometrics, 15(1), pp. 62–105.
• Bianchi, A., Dufour, J.-.M., Khalaf, L. and Urga, G. (2016). Monte Carlo Combined Tests with Non-Identifiable Nuisance Parameters. .
• Leccadito, A., Tunaru, R.S. and Urga, G. (2015). Trading strategies with implied forward credit default swap spreads. Journal of Banking & Finance, 58, pp. 361–375. doi:10.1016/j.jbankfin.2015.04.018.
• Novotný, J., Petrov, D. and Urga, G. (2015). Trading price jump clusters in foreign exchange markets. Journal of Financial Markets, 24, pp. 66–92. doi:10.1016/j.finmar.2015.03.002.
• Boffelli, S. and Urga, G. (2015). Macroannouncements, bond auctions and rating actions in the European government bond spreads. Journal of International Money and Finance, 53, pp. 148–173. doi:10.1016/j.jimonfin.2015.01.004.
• Ghalanos, A., Rossi, E. and Urga, G. (2015). Independent Factor Autoregressive Conditional Density Model. Econometric Reviews, 34(5), pp. 594–616. doi:10.1080/07474938.2013.808561.
• Leccadito, A., Rachedi, O. and Urga, G. (2015). True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison. Econometric Reviews, 34(4), pp. 452–479. doi:10.1080/07474938.2013.808462.
• Leccadito, A., Boffelli, S. and Urga, G. (2014). Evaluating the accuracy of value-at-risk forecasts: New multilevel tests. International Journal of Forecasting, 30(2), pp. 206–216. doi:10.1016/j.ijforecast.2013.07.014.
• Khalaf, L. and Urga, G. (2014). Identification robust inference in cointegrating regressions. Journal of Econometrics, 182(2), pp. 385–396. doi:10.1016/j.jeconom.2014.06.001.
• Driver, C., Trapani, L. and Urga, G. (2013). On the use of cross-sectional measures of forecast uncertainty. International Journal of Forecasting, 29(3), pp. 367–377. doi:10.1016/j.ijforecast.2012.11.005.
• Kao, C., Trapani, L. and Urga, G. (2012). Asymptotics for Panel Models with Common Shocks. Econometric Reviews, 31(4), pp. 390–439. doi:10.1080/07474938.2011.607991.
• Dumitru, A.M. and Urga, G. (2012). Identifying jumps in financial assets: A comparison between nonparametric jump tests. Journal of Business and Economic Statistics, 30(2), pp. 242–255. doi:10.1080/07350015.2012.66325.
• Trapani, L. and Urga, G. (2010). Micro versus macro cointegration in heterogeneous panels. Journal of Econometrics, 155(1), pp. 1–18. doi:10.1016/j.jeconom.2009.07.005.
• Trapani, L. and Urga, G. (2009). Optimal forecasting with heterogeneous panels: A Monte Carlo study. International Journal of Forecasting, 25(3), pp. 567–586. doi:10.1016/j.ijforecast.2009.02.001.
• Urga, G. and de Peretti, C. (2009). Stopping Tests in the Sequential Estimation for Multiple Structural Breaks. , T .
• Meoli, M., Paleari, S. and Urga, G. (2008). Changes in ownership and minority protection: Governance lessons from the case of Telecom Italia. International Journal of Managerial Finance, 4(4), pp. 323–342. doi:10.1108/17439130810902813.
• Huang, H., Kao, C. and Urga, G. (2008). Copula-based tests for cross-sectional independence in panel models. Economics Letters, 100(2), pp. 224–228. doi:10.1016/j.econlet.2008.01.017.
• Driver, C., Temple, P. and Urga, G. (2008). Real options — delay vs. pre-emption: Do industrial characteristics matter? International Journal of Industrial Organization, 26(2), pp. 532–545. doi:10.1016/j.ijindorg.2007.03.003.
• Urga, G. (2008). Testing for Instability in Factor Structure of Yield Curves. D. Phillips and Chihwa Kao .
• Meoli, M., Paleari, S. and Urga, G. (2008). Rights issues, private benefits and negative-NPV investments. Corporate Ownership and Control, 6(2 B CONT. 1), pp. 238–245.
• Bennett, J., Estrin, S. and Urga, G. (2007). Methods of privatization and economic growth in transition economies. The Economics of Transition, 15(4), pp. 661–683. doi:10.1111/j.1468-0351.2007.00300.x.
• Lazarovǎ, S., Trapani, L. and Urga, G. (2007). Common stochastic trends and aggregation in heterogeneous panels. Econometric Theory, 23(1), pp. 89–105. doi:10.1017/S0266466607070041.
• Urga, G. (2007). Common Features in Economics and Finance. Journal of Business & Economic Statistics, 25(1), pp. 2–11. doi:10.1198/073500106000000602.
• Driver, C., Temple, P. and Urga, G. (2006). Contrasts between types of assets in fixed investment equations as a way of testing real options theory. Journal of Business and Economic Statistics, 24(4), pp. 432–443. doi:10.1198/073500106000000062.
• Driver, C., Temple, P. and Urga, G. (2006). Identifying externalities in UK manufacturing using direct estimation of an average cost function. Economics Letters, 92(2), pp. 228–233. doi:10.1016/j.econlet.2006.02.003.
• Banerjee, A. and Urga, G. (2005). Modelling structural breaks, long memory and stock market volatility: An overview. Journal of Econometrics, 129(1-2), pp. 1–34. doi:10.1016/j.jeconom.2004.09.001.
• Gagliardini, P., Trojani, F. and Urga, G. (2005). Robust GMM tests for structural breaks. Journal of Econometrics, 129(1-2), pp. 139–182. doi:10.1016/j.jeconom.2004.09.006.
• Driver, C., Temple, P. and Urga, G. (2005). Profitability, capacity, and uncertainty: A model of UK manufacturing investment. Oxford Economic Papers, 57(1), pp. 120–141. doi:10.1093/oep/gpi001.
• Adesi, G.B., Gagliardini, P. and Urga, G. (2004). Testing asset pricing models with coskewness. Journal of Business and Economic Statistics, 22(4), pp. 474–485. doi:10.1198/073500104000000244.
• Driver, C. and Urga, G. (2004). Transforming Qualitative Survey Data: Performance Comparisons for the UK. Oxford Bulletin of Economics and Statistics, 66(1), pp. 71–89.
• Driver, C., Imai, K., Temple, P. and Urga, G. (2004). The effect of uncertainty on UK investment authorisation: Homogeneous vs. heterogeneous estimators. Empirical Economics, 29(1), pp. 115–128. doi:10.1007/s00181-003-0192-2.
• Lanza, A., Temple, P. and Urga, G. (2003). The implications of tourism specialisation in the long run: An econometric analysis for 13 OECD economies. Tourism Management, 24(3), pp. 315–321. doi:10.1016/S0261-5177(02)00065-1.
• Geroski, P.A., Lazarova, S., Urga, G. and Walters, C.F. (2003). Are differences in firm size transitory or permanent? Journal of Applied Econometrics, 18(1), pp. 47–59. doi:10.1002/jae.676.
• Urga, G. and Walters, C. (2003). Dynamic translog and linear logit models: A factor demand analysis of interfuel substitution in US industrial energy demand. Energy Economics, 25(1), pp. 1–21. doi:10.1016/S0140-9883(02)00022-1.
• Urga, G., Trojani, F. and Sugita, K. (2003). Robust Tests for Endogenous Structural Breaks: Some Monte Carlo Evidence. , To be submitted .
• Urga, G., Hall, S. and Lazarova, S. (2003). Stochastic Common Trends and Long-Run Relationships in Heterogenous Panels. , Under review .
• Urga, G. and Hall, S. (2003). Testing for Time-Varying Stock Market Efficiency using Russian Stock Prices. , Under review .
• Urga, G. (2001). Theory and practice of econometric modelling using PcGive10. Journal of Economic Surveys, 15(4), pp. 571–588.
• Estrin, S., Urga, G. and Lazarova, S. (2001). Testing for Ongoing Convergence in Transition Economies, 1970 to 1998. Journal of Comparative Economics, 29(4), pp. 677–691. doi:10.1006/jcec.2001.1736.
• Temple, P., Urga, G. and Driver, C. (2001). The influence of uncertainty on investment in the UK: A macro or micro phenomenon? Scottish Journal of Political Economy, 48(4), pp. 361–382.
• Mertens, A. and Urga, G. (2001). Efficiency, scale and scope economies in the Ukrainian banking sector in 1998. Emerging Markets Review, 2(3), pp. 292–308. doi:10.1016/S1566-0141(01)00022-X.
• Peresetsky, A., Turmuhambetova, G. and Urga, G. (2001). The development of the GKO futures market in Russia. Emerging Markets Review, 2(1), pp. 1–16. doi:10.1016/S1566-0141(00)00016-9.
• Rockinger, M. and Urga, G. (2001). A time-varying parameter model to test for predictability and integration in the stock markets of transition economies. Journal of Business and Economic Statistics, 19(1), pp. 73–84. doi:10.1198/07350010152472634.
• Estrin, S., Lazarova, S. and Urga, G. (2001). Convergence in transition countries - Focus on investment: Central and Eastern Europe, 1970-1996. Economics of Planning, 34(3), pp. 215–230. doi:10.1023/A:1011810922630.
• Park, A. and Sehrt, K. (2001). Tests of financial intermediation and banking reform in China. Journal of Comparitive Economics, 29(4), pp. 608–644. doi:10.1006/jcec.2001.1740.
• Rockinger, M. and Urga, G. (2000). The Evolution of Stock Markets in Transition Economies. Journal of Comparative Economics, 28(3), pp. 456–472. doi:10.1006/jcec.2000.1669.
• Urga, G. (1999). An application of dynamic specifications of factor demand equations to interfuel substitution in US industrial energy demand. Economic Modelling, 16(4), pp. 503–513.
• Hall, S., Lazarova, S. and Urga, G. (1999). A principal components analysis of common stochastic trends in heterogeneous panel data: Some Monte Carlo evidence. Oxford Bulletin of Economics and Statistics, 61(S1), pp. 749–767. doi:10.1111/1468-0084.0610s1749.
• Allen, C. and Urga, G. (1999). Interrelated factor demands from dynamic cost functions: An application to the non-energy business sector of the UK economy. Economica, 66(263), pp. 403–413.
• Temple, P. and Urga, G. (1997). The competitiveness of UK manufacturing: evidence from imports. Oxford Economic Papers, 49(2), pp. 207–227.
• Urga, G. (1996). On the identification problem in testing the dynamic specification of factor-demand equations. Economics Letters, 52(3), pp. 205–210. doi:10.1016/S0165-1765(96)00867-1.
• Hall, S., Urga, G. and Whitley, J. (1996). Structural Change and Economic Behaviour. The Case of UK Exports. Economic Issues, 1(March - Part 1), pp. 39–50. doi:10.1111/j.1468-0319.1995.tb00051.x.
• Urga, G. (1992). The Econometrics of Panel Data: a Selective Introduction. Ricerche Economiche, 46(4), pp. 379–396.
• Urga, G. (1992). Dynamic Labour Demand Models in Italian Manufacturing (in Italian). Economia & Lavoro, 1999(1) .
• Urga, G. (1992). Employment and Investment Functions in Unionised Labour Markets: Theories and Evidence from Italian Firm Data (in Italian). Ricerca e Metodi per la Politica Economica .
• Bergamelli, M., Novotný, J. and Urga, G. MAXIMUM NON-EXTENSIVE ENTROPY BLOCK BOOTSTRAP FOR NON-STATIONARY PROCESSES. L'Actualité Economique, 91(1-2), pp. 115–139.

Course Directorship

• 2004 - present, Centre for Econometric Analysis, Director
• 2002 - 2005, PhD Programme, Director
• 2010 - 2010, PhD programme, Director

Editorial Activities (25)

• International Journal of Forecasting, Referee, 2015 – present.
• Journal of Financial Econometrics, Referee, 2014 – 2015.
• SSHRC Insight Grant proposal, Canada, Referee, 2014 – present.
• Journal of Banking and Finance, Referee, 2013 – present.
• Cambridge University Press, Oxford University Press, Princeton University Press, Book Reviews Editor, 2012.
• Economics Letters, Referee, 2011 – 2012.
• International Journal of Industrial Organization, Referee, 2011 – 2012.
• Mathematical Finance, Referee, 2011.
• The European Journal of Finance, Referee, 2011.
• Econometrica, Referee, 2010.
• Finance and Stochastics, Referee, 2010.
• Oxford University Papers, Referee, 2010.
• Econometric Reviews, Referee, 2010 – present.
• Journal of Applied Econometrics, Referee, 2009 – 2012.
• Energy Economics, Referee, 2009 – present.
• Econometric Theory, Referee, 2007.
• Review of Financial Studies, Referee, 2007.
• Journal of Financial Econometrics, Referee, 2006 – 2007.
• Journal of Money Credit and Banking, Referee, 2006.
• The Economic Journal, Referee, 2004 – 2008.
• Journal of Business and Economic Statistics, Special Editor, 2004 – 2007.
• Empirical Economics, Associate Editor, 2004 – present.
• Journal of Business and Economics Statistics, Referee, 2002 – 2011.
• Journal of Econometrics, Special Editor, 2002 – 2005.
• Journal of Econometrics, Referee, 2001 – present.