Referred Publications


1.          Costola, M., and Caporin, M., 2016, Rational learning for risk-averse investors by conditioning on behavioral choices, 11-1, 1650003, Annals of Financial Economics,doi:10.1142/S2010495216500032;

2.          Caporin, M., Rossi, E., and Santucci de Magistris, P., 2016, Volatility jumps and their economic determinants, Journal of Financial Econometrics, 14-1, 29-80, doi:10.1093/jjfinec/nbu028;

3.          Billio, M., Caporin, M., and Costola, M., 2015, Backward/Forward optimal combination of performance measures, North American Journal of Economics and Finance, 34, C, 63-83,doi:10.1016/j.najef.2015.08.002, ;

4.          Asai, M., Caporin, M., and McAleer, M., 2015, Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility ModelsInternational Review of Economics and Finance, 40, C, 40-50, doi:10.1016/j.iref.2015.02.004;

5.          Caporin, M., and Velo, G., 2015, Forecasting realized range volatility: dynamic features and predictive variables, International Review of Economics and Finance, 40, C, 98-112doi:10.1016/j.iref.2015.02.021;

6.          Caporin, M., Hammoudeh, S., and Khalifa, A., 2015, Spillovers between energy and FX markets: The importance of asymmetry, uncertainty and business cycle, Energy Policy, 87, 72-82,doi:10.1016/j.enpol.2015.08.039;

7.          Baldovin, F., Caporin, M., Caraglio, M., Stella, A., and Zamparo, M., 2015, Option pricing with non-Gaussian scaling and infinite-state switching volatility, Journal of Econometrics, 187, 486-497,doi:10.1016/j.jeconom.2015.02.033;

8.          Caporin, M., Ranaldo, A., and Velo, G., Precious metals under the microscope: A high-frequency analysisQuantitative Finance, 15, 743-759, doi:10.1080/14697688.2014.947313;

9.          Baldovin, F., Camana, F., Caporin, M., Caraglio, M., and Stella, A., 2015, Ensemble properties of high frequency data and intraday trading rules, Quantitative Finance, 15, 231-245,doi:10.1080/14697688.2013.867454;

10.       Caporin, M., and Paruolo, P., 2015, Proximity-Structured Multivariate Volatility Models,Econometric Reviews, 34, 559-593, doi:10.1080/07474938.2013.807102;

11.       Caporin, M., Jannin, G.M., Lisi, F., and Maillet, B.B., 2014, A survey of the four families of performance measures, Journal of Economic Surveys, 28, 917-942, doi:10.1111/joes.12041;

12.       Aielli, G.P., and Caporin, M., 2014, Variance clustering improved dynamic conditional correlation estimators, Computational Statistics and Data Analysis, 76, 556-576,doi:10.1016/j.csda.2013.01.029;

13.       Caporin, M., and McAleer, M., 2014, Robust ranking of multivariate GARCH models by problem dimension, Computational Statistics and Data Analysis, 76, 172-185,doi:10.1016/j.csda.2012.05.012;

14.       Caporin, M., Jimenez-Martin, J.A., and Gonzales-Serrano, L., 2014, Currency hedging strategies and strategic benchmarks and the Global and Euro Sovereign financial crises, Journal of International Financial Markets Institutions and Money, 31, 159-177,doi:10.1016/j.intfin.2014.03.015;

15.       Caporin, M., and Lisi, F., 2013, A conditional single index model with local covariates for detecting and evaluating active portfolio management, North American Journal of Economics and Finance, 26, 236-249, doi:10.1016/j.najef.2013.02.003;

16.       Aielli, G.P., and Caporin, M., 2013, Fast Clustering of GARCH Processes Via Gaussian Mixture Models, Mathematics and Computers in Simulations, 94, 205-222,doi:10.1016/j.matcom.2012.09.015;

17.       Bonato, M., Caporin, M., and Ranaldo, A., 2013, Risk spillovers in international equity portfolios,Journal of Empirical Finance, 24, 121-137, doi:10.1016/j.jempfin. 2013.09.005;

18.       Caporin, M., Ranaldo, A., and Santucci de Magistris, P., 2013, On the Predictability of Stock Prices: a Case for High and Low Prices, Journal of Banking and Finance, 37, 5132-5146,doi:10.1016/j.jbankfin.2013.05.24;

19.       Kasch, M., and Caporin, M., 2013, Volatility threshold dynamic conditional correlations: an International analysis, Journal of Financial Econometrics, 11, 706-742, doi:10.1093/jjfinec/nbs028;

20.       Caporin, M., and McAleer,M., 2013, Ten Things You Should Know about the Dynamic Conditional Correlation Representation, Econometrics, 1, 115-126, doi:10.3390/econometrics1010115

21.       Caporin, M., 2013, Equity and CDS sector indices: dynamic models and risk hedging, North American Journal of Economics and Finance, 25, 261-275, doi:10.1016/j.najef.2012.06.004;

22.       Caporin, M., and Preś, J., 2013, Forecasting temperature indices density with time-varying long-memory models, Journal of Forecasting, 32, 339-352, doi:10.1002/for.1272;

23.       Bonato, M., Caporin, M., and Ranaldo, A., 2012, A forecast based comparison of restricted Wishart Auto Regressive models for realized covariance matrices, European Journal of Finance, 18, 761-774,doi:10.1080/1351847X.2011.601629;

24.       Caporin, M., and Lisi, F., 2012, On the role of risk in the Morningstar rating for funds,Quantitative Finance, 12, 1477-1486, doi:10.1080/14697688.2012.665999;

25.       Caporin, M., Preś, J. and Torro, H., 2012, Model Based Monte Carlo Pricing of Energy and Temperature Quanto Options, Energy Economics, 34, 1700-1712, doi:10.1016/j.eneco.2012.02.008;

26.       Caporin, M., and McAleer, M., Do we really need both BEKK and DCC? A tale of two covariance models, 2012, Journal of Economic Surveys, 26, 736-751, doi:10.1111/j.1467-6419.2011.00683.x;

27.       Caporin, M., and Pres, J., 2012, Modeling and forecasting wind speed intensity for weather risk management, Computational Statistics and Data Analysis, 56, 3459-3476,doi:10.1016/j.csda.2010.06.019;

28.       Caporin, M. and Santucci de Magistris, P., 2012, On the evaluation of Marginal Expected Shortfall,Applied Economics Letters, 19, 175-179 doi:10.1080/ 13504851.2011.570704;

29.       Caporin, M. and Lisi, F., 2011, Comparing and selecting performance measures using rank correlations, Economics: The Open-Access, Open-Assessment E-Journal, Vol. 5, 10,doi:10.5018/economics-ejournal.ja.2011-10;

30.       Caporin, M., and McAleer, M., 2011, Thresholds, news impact surfaces and dynamic asymmetric multivariate GARCH, Statistica Neerlandica, 65-2, 125-163, doi:10.1111/j.1467-9574.2010.00479.x;

31.       Billio, M., and Caporin, M., 2010, Market linkages, variance spillovers and correlation stability: empirical evidences of financial market contagion, Computational Statistics and Data Analysis, 54-11, 2443-2458, doi:10.1016/j.csda.2009.03.018;

32.       Caporin, M., and McAleer, M., 2010, A Scientific Classification of Volatility Models, Journal of Economic Surveys, 2010, 24-1, 192-195, doi:10.1111/j.1467-6419.2009.00584.x;

33.       Caporin, M., and McAleer, M., 2010, The Ten Commandments for Investment Management,Journal of Economic Surveys, 24,-1, 196-200, doi:10.1111/j.1467-6419.2009.00585.x;

34.       Caporin, M. and Lisi, F., 2010, Misspecification tests for periodic long memory GARCH models,Statistical Methods and Applications, 19-1, 47-62, doi:10.1007/s10260-009-0118-z;

35.       Billio, M., and Caporin, M., 2009, A generalised dynamic conditional correlation model for portfolio risk evaluation, Mathematics and Computer in Simulation, 79-8, 2566-2578,doi:10.1016/j.matcom.2008.12.011;

36.       Bordignon, S., Caporin, M., and Lisi, F., 2009, Periodic Long Memory GARCH models,Econometric Reviews, 28-(1-3), 60-82, doi:10.1080/07474930802387860;

37.       Caporin, M., and McAleer, M., 2008, Scalar BEKK and Indirect DCC, Journal of Forecasting, 27-6, 537-549, doi:10.1002/for.1074;

38.       Caporin, M., 2008, Evaluating value-at-risk measures in presence of long memory conditional volatility, Journal of Risk, 10-3, 79-110;

39.       Billio, M., and Caporin, M., and Cazzavillan, G., 2007, Dating Euro15 monthly business cycle jointly using GDP and IPI, Journal of Business Cycle Analysis and Measurement, 3-3, 336-366,doi:10.1787/17293626;

40.       Bordignon, S., Caporin, M., and Lisi, F., 2007, Generalised Long Memory GARCH models for intra-daily volatility, Computational Statistics and Data Analysis, 51-12, 5900-5912,doi:10.1016/j.csda.2006.11.004;

41.       Caporin, M., 2007, Variance (Non-)Causality in multivariate GARCH: a new model, Econometric Reviews, 26(1), 1-24, doi:10.1080/07474930600972178;

42.       Caporin, M., and McAleer, M., 2006, Dynamic Asymmetric GARCH, Journal of Financial Econometrics, 4(3), 385-412, doi:10.1093/jjfinec/nbj011;

43.       Billio, M., and Caporin, M., and Gobbo, M., 2006, Flexible Dynamic Conditional Correlation Multivariate GARCH for asset allocation, Applied Financial Economic Letters, 2(2), 123-130,doi:10.1080/17446540500428843;

44.       Billio, M., and Caporin, M., 2005, Dynamic conditional correlations: block structures and Markov switches for contagion analysis, Statistical Methods and Applications, 2005-14, 145-161,doi:10.1007/s10260-005-0108-8;

45.       Caporin, M., 2003, Identification of Long memory in GARCH models , Statistical Methods and Applications, 12, 133-151, doi:10.1007/s10260-003-0056-0;

46.       Bertelli, S. and Caporin, M., 2002, A note on calculating autocovariances of long memory processes, Journal of Time Series Analysis, 23(5), 503-508, doi:10.1111/1467-9892.00275.


Book chapters

1.          Caporin, M. and Fontini, F., 2015, Damages Evaluation, Periodic Floods, and Local Sea Level Rise: The Case of Venice, Italy, in Ramiah, W. and Gregoriou, G.N. (eds.), The Handbook of Environmental and Sustainable Finance, Academic Press – Elsevier, ISBN 978-0-128-03615-0,doi:10.1016/B978-0-12-803615-0.00005-4

2.          Caporin, M., and Velo, G.G., 2013, Modeling and forecasting realized range volatility, in Torelli, N. and Pesarin, F., (eds.), Advances in Theoretical and Applied Statistics, Springer, ISBN 978-3-642-35587-5;

3.          Billio, M., Caporin, M., Pelizzon, L., and Sartore, D., 2013, CDS Industrial Sector Indices, Credit and Liquidity Risk, in Rösch, D., and Scheule, H. (eds.), Credit Securities and Derivatives – Challenges for the Global Markets, John Wiley & Sons, Inc., New Jersey, US, ISBN 978-1-119-96396-7, doi:10.1002/9781118818503.ch15;

4.          Caporin, M., and Pelizzon, L., 2012, Market volatility, optimal portfolios and naive asset allocations, in Wehn, C., Hoppe, C., and Gregoriou, G.N. (eds.), Rethinking Valuation and Pricing Models: Lessons Learned from the Crisis and Future Challenges, Handbooks in Economics, Academic Press - Elsevier, ISBN 978-0124158757, doi:10.1016/B978-0-12-415875-7.00025-7;

5.          Caporin, M. and McAleer, M., 2012, Model selection and testing of conditional and stochastic volatility models, with M. McAleer (Erasmus University Rotterdam), in Bauwens, L., Hafner, C., and Laurent, S. (eds.) Handbook of Volatility Models and Their Applications (Wiley Handbooks in Financial Engineering and Econometrics), John Wiley & Sons, Inc., New Jersey, US, ISBN 978-0470872512, doi:10.1002/9781118272039.ch8;

6.          Billio, M. and Caporin, M., 2011, Contagion dating through market interdependence analysis and correlation stability, in Robert W. Kolb (eds.) Financial Contagion: The Viral Threat to the Wealth of Nations, February 2011, John Wiley & Sons, Inc., New Jersey, US, ISBN 978-0470922385,doi:10.1002/9781118267646.ch4.


Contributions in Italian

1.          Caporin, M., Lanzavecchia, A., Lippoli, 2013, I fondi immobiliari italiani: NAV discount e valutazioni degli esperti indipendenti, Finanza Produzione e Marketing, 3-2013.


Submitted papers (and revision)

1.          “Multi-jumps”, with A. Kolokolov (University of Lund), and R. Renò (University of Siena), Journal of Financial Economics, Revise and Resubmit;

2.          “Sovereign contagion in Europe”, with L. Pelizzon (University Ca’ Foscari Venice), F. Ravazzolo (Norges Bank) and R. Rigobon (MIT), Review of Finance, Revise and Resubmit;

3.          “Time-varying persistence in US inflation”, with R. Gupta (University of Pretoria), Empirical Economics, major revision;

4.          “Chasing volatility: a persistent multiplicative error component model with jumps”, with E. Rossi (University of Pavia) and and P. Santucci de Magistris (Aarhus University), Journal of Econometrics, major revision;

5.          “Dynamic Principal Component: a new class of Multivariate GARCH models”, with G.P. Aielli,Journal of Business and Economic Statistics, major revision;

6.          “The long-run Oil-Natural Gas price relationship and the shale gas revolution”, with F. Fontini (University of Padova), Energy Economics, major revision;

7.          “On the (Ab)Use of Omega?”, with M. Costola (University Ca’ Foscari Venezia), G. Jannin (University Paris I – Sorbonne) and B. Maillet (University of Reunion), North American Journal of Economics and Finance, major revision;

8.          “The determinants of equity risk and their forecasting implications: a quantile regression perspective”, with G. Bonaccolto (University of Padova);

9.          “Asset allocation with penalized quantile regression”, with G. Bonaccolto (University of Padova) and S. Paterlini (EBS University Business School);

10.       “Oil returns conditional quantiles and uncertainty indexes: causality and forecasting implications”, with G. Bonaccolto (University od Padova) and R. Gupta (Pretoria University);

11.       “The relationship between oil prices and the rig counts: A one-way street with a lag”, with A. Khalifa (Qatar University) and S. Hammoudeh (Drexel University);

12.       “Networks in risk spillovers: a multivariate GARCH perspective”, with M. Billio (University Ca’ Foscari Venezia), L. Frattarolo (University Ca’ Foscari Venezia), and L. Pelizzon (University Ca’ Foscari Venezia and Goethe University Frankfurt);

13.       “Measuring the behavioral component of financial fluctuations: an analysis based on the S&P 500”, with L. Corazzini (University of Messina) and M. Costola (University Ca’ Foscari Venezia;

14.       “Are the S&P500 index and crude oil, natural gas and ethanol futures related for intra-day data?”, with C. Chang (NCHU, Taiwan) and M. McAleer (NTHU, Taiwan).




Papers in progress

1.          “Network Connectivity and Systematic Risks”, with M. Billio (University Ca’ Foscari Venezia), R. Panzica (Goethe University Frankfurt), and L. Pelizzon (University Ca’ Foscari Venezia and Goethe University Frankfurt);

2.          “Spillover effect to Bailout Expectation: An Empirical Study of Denmark”, with G. Natvik (Norges Bank), F. Ravazzolo (Norges Bank), and P. Santucci de Magistris (Aarhus University);

3.          “Systemic risk measurement for GCC financial institutions: is there a role for oil?”, with M. Costola (University Ca’ Foscari Venezia), A. Khalifa (Qatar University) and S. Hammoudeh (Drexel University);

4.          “Causality networks: estimation and combination”, with R. Panzica (Goethe University Frankfurt) and G. Bonaccolto (University of Padova);

5.          “Systemic risk measurement with ΔCoVaR: persistence and local dependence”, with G. Bonaccolto (University of Padova) and S. Paterlini (EBS Wiesbaden);

6.          “Jump risk and pricing implicatons”, with N. Zambon (University of Padova) and W. Distaso (Imperial College London);

7.          “The temporal and cross-sectional dependence between realized volatility, price jumps and company-specific news”, with F. Poli (University of Padova);

8.          “Improved intersection rules for multiple co-jump detection”, with G. Bonaccolto (University of Padova);

9.          “Jump, Co-Jump and Multi-Jumps around the globe”, with G. Bonaccolto (University of Padova) and F. Poli (University of Padova);

10.       “The impact of unconventional monetary policies on European financial markets”, with L. Pelizzon (Goethe University Frankfurt) and A. Plazzi (Università della Svizzera Italiana).


Other publications

1.          Quarterly Newsletter “Nuova Previdenza”, IAMA Consulting, Milano; methodology boxes: “Gestioni a benchmark: vincoli e flessibilità”; “Manager diversification or manager competition?”; “Misure di performance innovative basate sui drawdown”; “Misure di performance in presenza di un target return”; “Il ruolo del gestore overlay nei fondi pensione”; “Performance attribution e scelte di gestione”; “Il ruolo e l’indipendenza dell’advisor”; “Le competenze della funzione finanza”;

2.          “Analisi delle transizioni nel mercato del lavoro”, with D. Favaro, 2006, Il mercato del lavoro nel Friuli Venezia Giulia, Rapporto 2006, Regione Autonoma Friuli Venezia Giulia, Direzione centrale lavoro, formazione, università e ricerca, Osservatorio regionale sul mercato del lavoro;

3.          “Il mercato del lavoro nel Friuli Venezia Giulia: un’analisi empirica basata su dati amministrativi”, 2005, Il mercato del lavoro nel Friuli Venezia Giulia, Rapporto 2005, Regione Autonoma Friuli Venezia Giulia, Direzione centrale lavoro, formazione, università e ricerca, Osservatorio regionale sul mercato del lavoro;


Research interests

- financial econometrics and financial time series analysis;

- market risk measurement and systemic risk measurement through dynamic models;

- multivariate models for financial market variances: GARCH, stochastic volatilities and their extensions; feasible parameterisations and efficient multi-step estimation approaches; application in asset allocation and market risk measurement; robust methods for model comparison; the curse of dimensionality; common patterns in the conditional variances; causality in variances; range based models;

- long term investment strategies: optimal strategies for agents with short term liquidity constraints; performance evaluation of long term investment plans and strategies; robust and optimal choice of agents optimizing criteria;

- active portfolio management: quantitative based strategies; performance evaluation of actively managed funds; style analysis and adherence to the benchmarks;

- high frequency data: conditional duration models, multivariate approaches and their possible application in high frequency quantitative trading; market microstructure and empirical analysis of market efficiency;

- long memory in variances, applications in financial time series forecasts and in weather derivatives pricing;

- business cycle and its relation with the financial markets, Markov-switching models, factor models and leading indicators;

- contagion analysis, business cycle and volatility spillover;

- time varying correlation models: specification and misspecification;

- spatial econometrics methods in finance;