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Research Papers

A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering

, &
Pages 981-996 | Received 15 Dec 2017, Accepted 05 Oct 2018, Published online: 14 Nov 2018

References

  • Alexander, C., Principal component models for generating large GARCH covariance matrices. Econ. Notes, 2002, 31, 337–359. doi: 10.1111/1468-0300.00089
  • Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P., Modeling and forecasting realized volatility. Econometrica, 2003, 71, 579–625. doi: 10.1111/1468-0262.00418
  • Barberis, N., Greenwood, R., Jin, L. and Shleifer, A., X-CAPM: An extrapolative capital asset pricing model. J. Financ. Econ., 2015, 115, 1–24. doi: 10.1016/j.jfineco.2014.08.007
  • Bauwens, L., Laurent, S. and Rombouts, J.V., Multivariate GARCH models: A survey. J. Appl. Econom., 2006, 21, 79–109. doi: 10.1002/jae.842
  • Borghesi, C., Marsili, M. and Miccichè, S., Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode. Phys. Rev. E, 2007, 76, 026104. doi: 10.1103/PhysRevE.76.026104
  • Bouchaud, J.P. and Potters, M., Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management, 2009 (Cambridge University Press: Cambridge).
  • Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M., Time Series Analysis: Forecasting and Control, p. 33, 2015 (John Wiley & Sons: Hoboken, NJ).
  • Breidt, F.J., Crato, N. and De Lima, P., The detection and estimation of long memory in stochastic volatility. J. Econom., 1998, 83, 325–348. doi: 10.1016/S0304-4076(97)00072-9
  • Bun, J., Bouchaud, J.P. and Potters, M., Cleaning large correlation matrices: Tools from random matrix theory. Phys. Rep., 2017, 666, 1–109. doi: 10.1016/j.physrep.2016.10.005
  • Chakraborti, A., Toke, I.M., Patriarca, M. and Abergel, F., Econophysics review: II. Agent-based models. Quant. Finance, 2011, 11, 1013–1041. doi: 10.1080/14697688.2010.539249
  • Chen, N.F., Roll, R. and Ross, S.A., Economic forces and the stock market. J. Bus., 1986, 59, 383–403. doi: 10.1086/296344
  • Chicheportiche, R. and Bouchaud, J.P., A nested factor model for non-linear dependencies in stock returns. Quant. Finance, 2015, 15, 1789–1804. doi: 10.1080/14697688.2014.994668
  • Child, D., The Essentials of Factor Analysis, 2006 (A&C Black: London).
  • Clark, P.K., A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 1973, 41, 135–155. doi: 10.2307/1913889
  • Connor, G., Hagmann, M. and Linton, O., Efficient semiparametric estimation of the Fama–French model and extensions. Econometrica, 2012, 80, 713–754. doi: 10.3982/ECTA7432
  • Cont, R., Empirical properties of asset returns: Stylized facts and statistical issues. Quant. Finance, 2001, 1, 223–236. doi: 10.1080/713665670
  • Darbyshire, J., The Volatility Surface: A Practitioner's Guide, Vol. 357, 2017 (Aitch & Dee Limited: Nottinghamshire).
  • Engel, C., Mark, N.C. and West, K.D., Factor model forecasts of exchange rates. Econom. Rev., 2015, 34, 32–55. doi: 10.1080/07474938.2014.944467
  • Faff, R., A simple test of the Fama and French model using daily data: Australian evidence. Appl. Financ. Econom., 2004, 14, 83–92. doi: 10.1080/0960310042000176353
  • Faff, R., Gharghori, P. and Nguyen, A., Non-nested tests of a GDP-augmented Fama–French model versus a conditional Fama–French model in the Australian stock market. Int. Rev. Econom. Finance, 2014, 29, 627–638. doi: 10.1016/j.iref.2013.07.007
  • Fama, E.F. and French, K.R., The cross-section of expected stock returns. J. Finance, 1992, 47, 427–465. doi: 10.1111/j.1540-6261.1992.tb04398.x
  • Fama, E.F. and French, K.R., Common risk factors in the returns on stocks and bonds. J. Financ. Econ., 1993, 33, 3–56. doi: 10.1016/0304-405X(93)90023-5
  • Fama, E.F. and French, K.R., Multifactor explanations of asset pricing anomalies. J. Finance, 1996, 51, 55–84. doi: 10.1111/j.1540-6261.1996.tb05202.x
  • Fama, E.F. and French, K.R., A five-factor asset pricing model. J. Financ. Econ., 2015, 116, 1–22. doi: 10.1016/j.jfineco.2014.10.010
  • Feller, W., An Introduction to Probability Theory and its Applications, 2008, Vol. 2 (John Wiley & Sons: Hoboken, NJ).
  • Grauer, R.R. and Janmaat, J.A., Cross-sectional tests of the CAPM and Fama–French three-factor model. J. Bank. Financ, 2010, 34, 457–470. doi: 10.1016/j.jbankfin.2009.08.011
  • Hull, J. and White, A., The pricing of options on assets with stochastic volatilities. J. Finance, 1987, 42, 281–300. doi: 10.1111/j.1540-6261.1987.tb02568.x
  • Hull, J.C., Options, Futures, and Other Derivatives, 2006 (Pearson Education India: Delhi).
  • Jackson, D.A., Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology, 1993, 74, 2204–2214. doi: 10.2307/1939574
  • Jolliffe, I.T., A note on the use of principal components in regression. Appl. Statist., 1982, 31, 300–303. doi: 10.2307/2348005
  • Jolliffe, I.T., Principal component analysis and factor analysis. In Principal Component Analysis, pp. 115–128, 1986 (Springer Science & Business Media: Berlin).
  • Laloux, L., Cizeau, P., Bouchaud, J.P. and Potters, M., Noise dressing of financial correlation matrices. Phys. Rev. Lett., 1999, 83, 1467. doi: 10.1103/PhysRevLett.83.1467
  • Livan, G., Alfarano, S. and Scalas, E., Fine structure of spectral properties for random correlation matrices: An application to financial markets. Phys. Rev. E, 2011, 84, 016113. doi: 10.1103/PhysRevE.84.016113
  • Lockhart, R., Taylor, J., Tibshirani, R.J. and Tibshirani, R., A significance test for the lasso. Ann. Statist., 2014, 42, 413. doi: 10.1214/13-AOS1175
  • Majumdar, S.N. and Vivo, P., Number of relevant directions in principal component analysis and Wishart random matrices. Phys. Rev. Lett., 2012, 108, 200601.
  • Malevergne, Y. and Sornette, D., Collective origin of the coexistence of apparent random matrix theory noise and of factors in large sample correlation matrices. Phys. A, 2004, 331, 660–668. doi: 10.1016/j.physa.2003.09.004
  • Mandelbrot, B.B., The variation of certain speculative prices. In Fractals and Scaling in Finance, pp. 371–418, 1997 (Springer Science & Business Media: Berlin).
  • Markowitz, H., Portfolio selection. J. Finance, 1952, 7, 77–91.
  • Merton, R.C., An intertemporal capital asset pricing model. Econometrica, 1973, 41, 867–887. doi: 10.2307/1913811
  • Micciche, S., Empirical relationship between stocks cross-correlation and stocks volatility clustering. J. Stat. Mech., 2013, 2013, P05015. doi: 10.1088/1742-5468/2013/05/P05015
  • Musmeci, N., Aste, T. and Di Matteo, T., Relation between financial market structure and the real economy: Comparison between clustering methods. PloS One, 2015a, 10, e0116201. doi: 10.1371/journal.pone.0116201
  • Musmeci, N., Aste, T. and Di Matteo, T., Risk diversification: A study of persistence with a filtered correlation-network approach. J. Netw. Theory Finance, 2015b, 1, 77–98. doi: 10.21314/JNTF.2015.005
  • Musmeci, N., Aste, T. and Di Matteo, T., Interplay between past market correlation structure changes and future volatility outbursts. Sci. Rep., 2016, 6, 36320. doi: 10.1038/srep36320
  • Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L.A.N., Guhr, T. and Stanley, H.E., Random matrix approach to cross correlations in financial data. Phys. Rev. E, 2002, 65, 066126. doi: 10.1103/PhysRevE.65.066126
  • Preacher, K.J., Zhang, G., Kim, C. and Mels, G., Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behav. Res., 2013, 48, 28–56. doi: 10.1080/00273171.2012.710386
  • Racicot, F.E. and Rentz, W.F., Testing Fama–French's new five-factor asset pricing model: Evidence from robust instruments. Appl. Econ. Lett., 2016, 23, 444–448.
  • Reinganum, M.R., The arbitrage pricing theory: Some empirical results. J. Finance, 1981, 36, 313–321. doi: 10.1111/j.1540-6261.1981.tb00444.x
  • Roll, R. and Ross, S.A., An empirical investigation of the arbitrage pricing theory. J. Finance, 1980, 35, 1073–1103. doi: 10.1111/j.1540-6261.1980.tb02197.x
  • Sachs, L., Applied Statistics: A Handbook of Techniques, 2012 (Springer Science & Business Media: Berlin).
  • Sharpe, W.F., Capital asset prices: A theory of market equilibrium under conditions of risk. J. Finance, 1964, 19, 425–442.
  • Singh, A. and Xu, D., Random matrix application to correlations amongst the volatility of assets. Quant. Finance, 2016, 16, 69–83. doi: 10.1080/14697688.2015.1014400
  • Song, W.M., Di Matteo, T. and Aste, T., Hierarchical information clustering by means of topologically embedded graphs. PLoS One, 2012, 7, e31929. doi: 10.1371/journal.pone.0031929
  • Taylor, S.J., Modeling stochastic volatility: A review and comparative study. Math. Finance, 1994, 4, 183–204. doi: 10.1111/j.1467-9965.1994.tb00057.x
  • Theil, H., A rank-invariant method of linear and polynomial regression analysis. In Henri Theil's Contributions to Economics and Econometrics, pp. 345–381, 1992 (Springer: Berlin).
  • Thompson, B., Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications, 2004 (American Psychological Association: Washington, DC).
  • Tumminello, M., Lillo, F. and Mantegna, R.N., Hierarchically nested factor model from multivariate data. EPL (Europhys. Lett.), 2007, 78, 30006. doi: 10.1209/0295-5075/78/30006
  • Tumminello, M., Micciche, S., Lillo, F., Piilo, J. and Mantegna, R.N., Statistically validated networks in bipartite complex systems. PloS One, 2011, 6, e17994. doi: 10.1371/journal.pone.0017994
  • Van Der Maaten, L., Postma, E. and Van den Herik, J., Dimensionality reduction: A comparative. J. Mach. Learn. Res., 2009, 10, 66–71.
  • Zabarankin, M., Pavlikov, K. and Uryasev, S., Capital asset pricing model (CAPM) with drawdown measure. Eur. J. Oper. Res., 2014, 234, 508–517. doi: 10.1016/j.ejor.2013.03.024
  • Zhang, K. and Chan, L., Efficient factor garch models and factor-dcc models. Quant. Finance, 2009, 9, 71–91. doi: 10.1080/14697680802039840
  • Zou, H. and Hastie, T., Regularization and variable selection via the elastic net. J. R. Stat. Soc., 2005, 67, 301–320. doi: 10.1111/j.1467-9868.2005.00503.x

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