Ricerca

Publications in International Journals

 

1.      

Caporin, M., Corazzini, L., and Costola, M., 2018, Measuring the Behavioural Component of the S&P 500 and Its Relationship to Financial Stress and Aggregated Earnings Surprises, British Journal of Management, forthcoming;

2.      

Blasi, S., Caporin, M., and Fontini, F., 2018, A multidimensional analysis of the relationship between firms’ Corporate Social Responsibility activities and their economic performance, Ecological Economics, doi:10.1016/j.ecolecon.2018.01.014;

3.      

Caporin, M., Pelizzon, L., Ravazzolo, F., and Rigobon, R., 2018, Sovereign contagion in Europe, Journal of Financial Stability, doi:10.1016/j.jfs.2017.12.004;

4.      

Bonaccolto, G., Caporin, M., and Paterlini, S., 2018, Asset allocation with penalized quantile regression, Computational Management Science, doi:10.1007/s10287-017-0288-3;

5.      

Caporin, M., Costola, M, Jannin, J., and Maillet, B., 2018, On the (Ab)Use of Omega?, Journal of Empirical Finance, doi:10.1016/j.jempfin.2017.11.007;

6.      

Caporin, M., Kolokolov, A., and Renò, R., 2017, Systemic co-jumps, Journal of Financial Economics, doi:10.1016/j.jfineco.2017.06.016;

7.      

Caporin, M., and Poli, F., 2017, Building News Measures from Textual Data and an Application to Volatility Forecasting, Econometrics, doi:10.3390/econometrics5030035;

8.      

Caporin, M., and Gupta, R., 2017, Time-varying persistence in US inflation, Empirical Economics, 53-2, 423-439, doi:10.1007/s00181-016-1144-y;

9.      

Caporin, M., and Fontini, F., 2017, The Long-Run Oil-Natural Gas Price Relationship and the Shale Gas Revolution, Energy Economics, 64, 511-519, doi:10.1016/j.eneco.2016.07.024;

10.   

Caporin, M., Rossi, E, and Santucci de Magistris, P., 2017, Chasing volatility: a persistent multiplicative error component model with jumps, Journal of Econometrics, 198-1, 122-145, doi:10.1016/j.jeconom.2017.01.005;

11.   

Caporin, M., Khalifa, A., and Hammoudeh, S., 2017, The relationship between oil prices and rig counts: The importance of lags, Energy Economics, 63, 213-226, doi:10.1016/j.eneco.2017.01.015;

12.   

Bonaccolto, G., and Caporin, M., 2016, The determinants of equity risk and their forecasting implications: a quantile regression perspective, Journal of Risk and Financial Management, 2016, 9, 8, doi:10.3390/jrfm9030008;

13.   

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;

14.   

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;

15.   

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;

16.   

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

17.   

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

18.   

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;

19.   

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;

20.   

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

21.   

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;

22.   

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

23.   

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;

24.   

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;

25.   

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;

26.   

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;

27.   

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;

28.   

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;

29.   

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;

30.   

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;

31.   

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;

32.   

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

33.   

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;

34.   

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;

35.   

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;

36.   

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;

37.   

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;

38.   

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;

39.   

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;

40.   

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;

41.   

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;

42.   

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;

43.   

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;

44.   

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;

45.   

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;

46.   

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;

47.   

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;

48.   

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

49.   

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

50.   

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

51.   

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;

52.   

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;

53.   

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

54.   

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

55.   

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;

56.   

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;

57.   

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

58.   

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.  

Bonato, M., Caporin M., and Ranagldo A., 2016, A forecast-based comparison of restricted Wishart autoregressive models for realized covariance matrices, in Nolte I., Salmon M. and Adcock C. (eds.), High Frequency Trading and Limit Order Book Dynamics, Routledge, ISBN  978-1138829381, reprinted from European Journal of Finance (2012);

2.  

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

3.  

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;

4.  

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;

5.  

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;

6.  

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;

7.  

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. 

Italiano