Clustering Huge Number of Financial Time Series: A Panel Data Approach With High-Dimensional Predictors and Factor Structures

References

• Alessi, L., Barigozzi, M., and Capasso, M. (2010), “Improved Penalization for Determining The Number of Factors in Approximate Static Factor Models,” Statistics and Probability Letters, 80, 1806–1813.
   Web of Science ®Google Scholar
• Amengual, D., and Watson, M. W. (2007), “Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel,” Journal of Business and Economic Statistics, 25, 91–96.
   Web of Science ®Google Scholar
• Ando, T., and Bai, J. (2014), “Selecting the Regularization Parameters in High-Dimensional Panel Data Models: Consistency and Efficiency,” Econometric Reviews, forthcoming.
   Google Scholar
• ——— (2015), “Asset Pricing With a General Multifactor Structure,” Journal of Financial Econometrics, 13, 556–604.
   Web of Science ®Google Scholar
• ——— (2016), “Panel Data Models With Grouped Factor Structure Under Unknown Group Membership,” Journal of Applied Econometrics, 136, 163–191.
   Google Scholar
• Bai, J. (2009), “Panel Data Models With Interactive Fixed Effects,” Econometrica, 77, 1229–1279.
   Web of Science ®Google Scholar
• Bai, J., and Ng, S. (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221.
   Web of Science ®Google Scholar
• Baca, S. P., Garbe, B. L., and Weiss, R. A. (2000), “The Rise of Sector Effects in Major Equity Markets,” Financial Analysts Journal, 56, 34–40.
   Google Scholar
• Beckers, S., Connor, G., and Curds, R. (1996), “National Versus Global Influences on Equity Returns,” Financial Analysts Journal, 52, 31–39.
   Google Scholar
• Bonhomme, S., and Manresa, E. (2015), “Grouped Patterns of Heterogeneity in Panel Data,” Econometrica, 83, 1147–1184.
   Web of Science ®Google Scholar
• Breheny, P. and Huang, J. (2011), “Coordinate Descent Algorithms for Nonconvex Penalized Regression, With Applications to Biological Feature Selection,” Annals of Applied Statistics, 5, 232–253.
   PubMed Web of Science ®Google Scholar
• Candes, E., and Tao, T. (2007), “The Dantzig Selector: Statistical Estimation When p is Much Larger Than n,” Annals of Statistics, 35, 2313–2351.
   Web of Science ®Google Scholar
• Cavaglia, S., Brightman, C., and Aked, M. (2000), “The Increasing Importance of Industry Factors,” Financial Analysts Journal, 56, 41–54.
   Google Scholar
• Chamberlain, G., and M. Rothschild., (1983), “Arbitrage, Factor Structure and Mean-Variance Analysis in Large Asset Markets,” Econometrica, 51, 1305–1324.
   Web of Science ®Google Scholar
• Connor, G., and Korajzcyk, R. (1986), “Performance Measurement With the Arbitrage Pricing Theory: A New Framework for Analysis,” Journal of Financial Economics, 15, 373–394.
   Web of Science ®Google Scholar
• Diebold, F., Li, C., and Yue, V. (2008), “Global Yield Curve Dynamics and Interactions: A Dynamic Nelson-Siegel Approach,” Journal of Econometrics, 146, 315–363.
   Web of Science ®Google Scholar
• Diebold, F. X., and Yilmaz, K. (2014), “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms,” Journal of Econometrics, 182, 119–134.
   Web of Science ®Google Scholar
• Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), “Least Angle Regression,” Annals of Statistics, 32, 407–499.
   Web of Science ®Google Scholar
• Fama, E. F., and French, K. R. (1993), “Common Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics, 33, 3–56.
   Web of Science ®Google Scholar
• ——— (1998), “Value Versus Growth: The International Evidence,” Journal of Finance, 53, 1975–1999.
   Web of Science ®Google Scholar
• ——— (2012), “Size, Value, and Momentum in International Stock Returns,” Journal of Finance, 105, 457–472.
   Google Scholar
• Fama, E. F., and MacBeth, J. D. (1973), “Risk, Return, and Equilibrium: Empirical Tests,” Journal of Political Economy, 81, 607–636.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” Journal of the American Statistical Association, 96, 1348–1361.
   Web of Science ®Google Scholar
• Fan, J., and Peng, H. (2004), “Nonconcave Penalized Likelihood With a Diverging Number of Parameters,” Annals of Statistics, 32, 928–961.
   Web of Science ®Google Scholar
• Fan, J., Liao, Y., and Mincheva, M. (2013), “Large Covariance Estimation by Thresholding Principal Orthogonal Complements,” Journal of the Royal Statistical Society, Series B, 75, 603–680.
   Google Scholar
• Forbes, K. J., and Rigobon, R. (2002), “No Contagion, Only Interdependence: Measuring Stock Market Comovements,” Journal of Finance, 57, 2223–2261.
   Web of Science ®Google Scholar
• Forgy, E. W. (1965), “Cluster Analysis of Multivariate Data: Efficienty vs. Interpretability of Classifications,” Biometrics, 21, 768–769.
   Web of Science ®Google Scholar
• Forni, M., Hallin, M., Lippi, M., and Reichlin, L. (2000), “The Generalized Dynamic Factor Model: Identification and Estimation,” Review of Economics and Statistics, 82, 540–554.
   Web of Science ®Google Scholar
• Forni, M., and Lippi, M. (2001), “The Generalized Factor Model: Representation Theory,” Econometric Theory, 17, 1113–1141.
   Web of Science ®Google Scholar
• Geweke, J. (1977), “The Dynamic Factor Analysis of Economic Time Series,” in Latent Variables in Socio-Economic Models, eds. D. J. Aigner, A. S. Goldberger, Amsterdam: North-Holland, pp. 365–383.
   Google Scholar
• Griffin, J. M. (2002), “Are the Fama and French Factors Global or Country Specific?,” Review of Financial Studies, 15, 783–803.
   Web of Science ®Google Scholar
• Griffin, J. M., and Karolyi, G. A. (1998), “Another Look at the Role of the Industrial Structure of Markets for International Diversification Strategies,” Journal of Financial Economics, 50, 351–373.
   Web of Science ®Google Scholar
• Hallin, M., and R. Lisˇka, R. (2007), “The Generalized Dynamic Factor Model: Determining the Number of Factors,” Journal of the American Statistical Association, 102, 603–617.
   Web of Science ®Google Scholar
• ——— (2011), “Dynamic Factors in the Presence of Blocks,” Journal of Econometrics, 163, 29–41.
   Web of Science ®Google Scholar
• Heston, S. L., and Rouwenhorst, K. G. (1994), “Does Industrial Structure Explain the Benefits of Industrial Diversification,” Journal of Financial Economics, 36, 3–27.
   Web of Science ®Google Scholar
• ——— (1995), “Industry and Country Effects in International Stock Returns,” Journal of Portfolio Management, 21, 53–58.
   Web of Science ®Google Scholar
• Hou, K., Karolyi, G. A., and Kho, B.-C. (2011), “What Factors Drive Global Stock Returns,” Review of Financial Studies, 24, 2527–2574.
   Web of Science ®Google Scholar
• Kose, A., Otrok, C., and Whiteman, C. (2008), “Understanding the Evolution of World Business Cycles,” International Economic Review, 75, 110–130.
   Google Scholar
• Kuo, W., and Satchell, S. E. (2001), “Global Equity Styles and Industry Effects: The Pre-Eminence of Value Relative to Size,” Journal of International Financial Markets, Institutions and Money, 11, 1–28.
   Google Scholar
• Lam, C., and Fan, J. (2008), “Profile-Kernel Likelihood Inference With Diverging Number of Parameters,” Annals of Statistics, 36, 2232–2260.
   PubMed Web of Science ®Google Scholar
• Lin, C., and Ng, S. (2012), “Estimation of Panel Data Models With Parameter Heterogeneity When Group Membership is Unknown,” Journal of Econometric Methods, 1, 42–55.
   Google Scholar
• Longstaff, F. A. (2010), “The Subprime Credit Crisis and Contagion in Financial Markets,” Journal of Financial Economics, 97, 436–450.
   Web of Science ®Google Scholar
• Moench, E., and Ng, S. (2011), “A Factor Analysis of Housing Market Dynamics in the U.S. and the Regions,” Econometrics Journal, 14, 1–24.
   Web of Science ®Google Scholar
• Moench, E., Ng, S., and Potter, S. (2012), “Dynamic Hierarchical Factor Models,” Review of Economics and Statistics, forthcoming. Available at Staff Reports 412, Federal Reserve Bank of New York.
   Web of Science ®Google Scholar
• Ohno, S., and Ando, T. (2015), “Stock Return Predictability: A Factor-Augmented Predictive Regression System With Shrinkage Method,” Econometric Reviews, forthcoming.
   PubMed Web of Science ®Google Scholar
• Park, T., and Casella, G. (2008), “The Bayesian Lasso,” Journal of the American Statistical Association, 103, 681–686.
   Web of Science ®Google Scholar
• Pesaran, M. H. (2006), “Estimation and Inference in Large Heterogeneous Panels With a Multifactor Error Structure,” Econometrica, 74, 967–1012.
   Web of Science ®Google Scholar
• Roll, R. (1992), “Industrial Structure and the Comparative Behavior of International Stock Market Indices,” Journal of Finance, 47, 3–42.
   Web of Science ®Google Scholar
• Sargent, T. J., and Sims, C. A. (1977), “Business Cycle Modeling Without Pretending to Have too Much a Priori Economic Theory,” in New Methods in Business Cycle Research, eds. C. Sims, et al., Minneapolis, MN: Federal Reserve Bank of Minneapolis.
   Google Scholar
• Stock, J. H., and Watson, M. W. (2002), “Forecasting Using Principal Components From a Large Number of Observable Factors,” Journal of the American Statistical Association, 97, 1167–1179.
   Web of Science ®Google Scholar
• Su, L., Shi, Z., and Phillips, P. (2016), “Identifying Latent Structures in Panel Data,” Econometrica, 84, 2215–2264.
   Google Scholar
• Sun, Y. X. (2005), “Estimation and Inference in Panel Structure Models,” Working paper, San Diego: Department of Economics, University of California.
   Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
   Google Scholar
• Wang, P. (2010), “Large Dimensional Factor Models With a Multi-Level Factor Structure,” Working paper, Department of Economics, HKUST.
   Google Scholar
• Yuan, M., and Lin, Y. (2006), “Model Selection and Estimation in Regression With Grouped Variables,” Journal of the Royal Statistical Society, B68, 49–67.
   Google Scholar
• Zhang, C. H. (2010), “Nearly Unbiased Variable Selection Under Minimax Concave Penalty,” Annals of Statistics, 38, 894–942.
   Web of Science ®Google Scholar
• Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.
   Web of Science ®Google Scholar
• Zou, H., and Hastie, T. (2005), “Regularization and Variable Selection via the Elastic Net,” Journal of the Royal Statistical Society, Series B, 67, 301–320.
   Google Scholar