Forecasting market states

References

• Ang, A. and Bekaert, G., International asset allocation with time-varying correlations. Rev. Financial Stud., 2002, 15, 1137–1187. doi: 10.1093/rfs/15.4.1137
   Web of Science ®Google Scholar
• Ang, A. and Chen, J., Asymmetric correlations of equity portfolios. J. Financ. Econ., 2002, 63, 443–494. doi: 10.1016/S0304-405X(02)00068-5
   Web of Science ®Google Scholar
• Aste, T. and Di Matteo, T., Causality network retrieval from short time series. ArXiv preprint arXiv:1706.01954, 2017.
   Google Scholar
• Barfuss, W., Massara, G.P., di Matteo, T. and Aste, T., Parsimonious modeling with information filtering networks. Phys. Rev. E, 2016, 94, 062306. doi: 10.1103/PhysRevE.94.062306
   PubMed Web of Science ®Google Scholar
• Bishop, C.M., Pattern Recognition and Machine Learning, 2006 (Springer-Verlag: New York).
   Google Scholar
• Black, F. and Litterman, R., Global portfolio optimization. Financ. Anal. J., 1992, 48, 28–43. doi: 10.2469/faj.v48.n5.28
   Google Scholar
• Bollerslev, T., Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Rev. Econ. Stat., 1990, 4, 498–505. doi: 10.2307/2109358
   Google Scholar
• Campbell, J.Y., Lo, A.W. and MacKinlay, A.C., The Econometrics of Financial Markets, 1997 (Princeton University Press: Princeton, NJ).
   Google Scholar
• Cizeau, P., Potters, M. and Bouchaud, J.P., Correlation structure of extreme stock returns. Quant. Finance, 2010, 1, 217–222. doi: 10.1080/713665669
   Google Scholar
• Danielsson, J., Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab, 2011 (Wiley-Blackwell: Hoboken).
   Google Scholar
• De Maesschalck, R., Jouan-Rimbaud, D. and Massart, D.L., The Mahalanobis distance. Chemometr. Intell. Lab. Syst., 2000, 50, 1–18. doi: 10.1016/S0169-7439(99)00047-7
   Web of Science ®Google Scholar
• Duffie, D. and Pan, J., An overview of value at risk. J. Derivatives, 1997, 4, 7–49. doi: 10.3905/jod.1997.407971
   Google Scholar
• Engle, R., Dynamic conditional correlation. J. Bus. Econ. Stat., 2002, 20, 339–350. doi: 10.1198/073500102288618487
   Web of Science ®Google Scholar
• Focardi, S.M. and Fabozzi, F.J., A methodology for index tracking based on time-series clustering. Quant. Finance, 2004, 4, 417–425. doi: 10.1080/14697680400008668
   Web of Science ®Google Scholar
• Friedman, J., Hastie, T. and Tibshirani, R., Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 2008, 9, 432–441. doi: 10.1093/biostatistics/kxm045
   PubMed Web of Science ®Google Scholar
• Grabarnik, P. and Särkkä, A., Interacting neighbour point processes: Some models for clustering. J. Stat. Comput. Simul., 2001, 68, 103–125. doi: 10.1080/00949650108812059
   Web of Science ®Google Scholar
• Hallac, D., Nystrup, P. and Boyd, S., Greedy Gaussian segmentation of multivariate time series. ArXiv e-prints, 2016.
   Google Scholar
• Hallac, D., Vare, S., Boyd, S.P. and Leskovec, J., Toeplitz inverse covariance-based clustering of multivariate time series data. CoRR, abs/1706.03161, 2017.
   Google Scholar
• Hamilton, J.D., A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 1989, 57, 357–384. doi: 10.2307/1912559
   Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R. and Friedman, J., The Elements of Statistical Learning, 2008 (Springer: New York).
   Google Scholar
• Hendricks, D., Gebbie, T. and Wilcox, D., Detecting intraday financial market states using temporal clustering. Quant. Finance, 2016, 16, 1657–1678. doi: 10.1080/14697688.2016.1171378
   Web of Science ®Google Scholar
• Kim, C.J. and Nelson, C., State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications, 1999 (The MIT Press: Cambridge, MA).
   Google Scholar
• Laloux, L., Cizeau, P., Bouchaud, J.P. and Potters, M., Noise dressing of financial correlation matrices. Phys. Rev. Lett., 1999, 83, 1467–1470. doi: 10.1103/PhysRevLett.83.1467
   Web of Science ®Google Scholar
• Lauritzen, S.L., Graphical Models, 1996 (Oxford University Press: Oxford).
   Google Scholar
• Lee, S. and Stevenson, S., Time weighted portfolio optimisation. J. Prop. Investment Finance, 2003, 21, 233–249. doi: 10.1108/14635780310481667
   Google Scholar
• Liao, W.T., Clustering of time series data – a survey. Pattern Recogn., 2005, 38, 1857–1874. doi: 10.1016/j.patcog.2005.01.025
   Web of Science ®Google Scholar
• Lin, W.L., Engle, R. and Ito, T., Do bulls and bears move across borders? International transmission of stock returns and volatility. Rev. Financ. Stud., 1994, 7, 507–38. doi: 10.1093/rfs/7.3.507
   Web of Science ®Google Scholar
• Longerstaey, J. and Spencer, M., RiskMetrics Technical Document, J.P. Morgan/Reuters, 1996.
   Google Scholar
• Massara, G.P., di Matteo, T. and Aste, T., Network filtering for big data: Triangulated maximally filtered graph. CoRR, abs/1505.02445, 2015.
   Google Scholar
• Musmeci, N., Aste, T. and Di Matteo, T., What does past correlation structure tell us about the future? An answer from network filtering. ArXiv e-prints, 2016.
   Google Scholar
• Musmeci, N., Nicosia, V., Aste, T., Di Matteo, T. and Latora, V., The multiplex dependency structure of financial markets. Complexity, 2017, 2017, 13. doi: 10.1155/2017/9586064
   Google Scholar
• Nevill-Manning, C.G. and Witten, I.H., Identifying hierarchical structure in sequences: A linear-time algorithm. J. Artif. Intell. Res., 1997, 7, 67–82. doi: 10.1613/jair.374
   Web of Science ®Google Scholar
• Neyman, J. and Pearson, E.S., IX. On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. London A: Math. Phys. Eng. Sci., 1933, 231, 289–337. doi: 10.1098/rsta.1933.0009
   Google Scholar
• Ren, L., Wei, Y., Cui, J. and Du, Y., A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data. J. Stat. Comput. Simul., 2017, 87, 2494–2508. doi: 10.1080/00949655.2017.1299151
   Web of Science ®Google Scholar
• Schmitt, T.A., Chetalova, D., Schäfer, R. and Guhr, T., Non-stationarity in financial time series: Generic features and tail behavior. EPL (Europhys. Lett.), 2013, 103, 58003. doi: 10.1209/0295-5075/103/58003
   Google Scholar
• Sharpe, W.F., Mutual fund performance. J. Bus., 1966, 39, 119–138. doi: 10.1086/294846
   Web of Science ®Google Scholar
• Sharpe, W.F., The Sharpe ratio. J. Portf. Manag., 1994, 21, 49–58. doi: 10.3905/jpm.1994.409501
   Web of Science ®Google Scholar
• Tong, H., On a threshold model. In Pattern Recognition and Signal Processing, edited by C. H. Chen, 1978 (Sijthoff and Noordhoff: Amsterdam).
   Google Scholar
• Tsay, R., Analysis of Financial Time Series, 2nd ed., 2005 (Wiley: Hoboken, NJ).
   Google Scholar
• Viterbi, A., Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory, 1967, 13, 260–269. doi: 10.1109/TIT.1967.1054010
   Web of Science ®Google Scholar
• Zolhavarieh, S., Aghabozorgi, S. and Wah Teh, Y., A review of subsequent time series clustering. Sci. World J., 2014, 2014, 312521. doi: 10.1155/2014/312521
   Google Scholar