Statistical arbitrage with vine copulas

References

• Aas, K. , Czado, C. , Frigessi, A. and Bakken, H. , Pair-copula constructions of multiple dependence. Insur. Math. Econ. , 2009, 44 , 182–198.
   Web of Science ®Google Scholar
• Avellaneda, M. and Lee, J.H. , Statistical arbitrage in the US equities market. Quant. Finance , 2010, 10 , 761–782.
   Web of Science ®Google Scholar
• Bacon, C.R. , Practical Portfolio Performance: Measurement and Attribution , 2nd ed., 2008 (John Wiley & Sons: Chichester).
   Google Scholar
• Bedford, T. and Cooke, R.M. , Probability density decomposition for conditionally dependent random variables modeled by vines. Ann. Math. Art. Int. , 2001, 32 , 245–268.
   Web of Science ®Google Scholar
• Bedford, T. and Cooke, R.M. , Vines: A new graphical model for dependent random variables. Ann. Stat. , 2002, 1031–1068.
   Web of Science ®Google Scholar
• Bollinger, J. , Using Bollinger bands. Stock. & Comm. , 1992, 10 , 47–51.
   Google Scholar
• Brechmann, E.C. and Czado, C. , Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50. Stat. Risk. Mod. , 2013, 30 , 307–342.
   Google Scholar
• Brechmann, E.C. and Czado, C. , COPAR -- Multivariate time series modeling using the copula autoregressive model. Appl. Stoch. Mod. Bus. Ind. , 2015, 31 , 495–514.
   Web of Science ®Google Scholar
• Brechmann, E.C. , Czado, C. and Aas, K. , Truncated regular vines in high dimensions with application to financial data. Can. J. Stat. , 2012, 40 , 68–85.
   Web of Science ®Google Scholar
• Brechmann, E.C. , Czado, C. and Paterlini, S. , Flexible dependence modeling of operational risk losses and its impact on total capital requirements. J. Bank. Finance , 2014, 40 , 271–285.
   Web of Science ®Google Scholar
• Brechmann, E.C. and Schepsmeier, U. , Modeling dependence with C-and D-vine copulas: The R-package CDVine. J. Stat. Softw. , 2013, 52 , 1–27.
   PubMed Web of Science ®Google Scholar
• Calabrese, R. , Degl’Innocenti, M. and Osmetti, S.A. , The effectiveness of TARP-CPP on the US banking industry: A new copula-based approach. Eur. J. Oper. Res. , 2017, 256 , 1029–1037.
   Web of Science ®Google Scholar
• Chen, H. , Chen, S.J. and Li, F. , Empirical investigation of an equity pairs trading strategy. Working Paper, Columbia University, 2012.
   Google Scholar
• Cherubini, U. , Luciano, E. and Vecchiato, W. , Copula Methods in Finance , 2004 (John Wiley & Sons: Chichester).
   Google Scholar
• Clegg, M. and Krauss, C. , Pairs trading with partial cointegration. Quant. Finance , 2017, 18 , 121–138.
   Web of Science ®Google Scholar
• Czado, C. , Pair-copula constructions of multivariate copulas. In Copula Theory and its Applications: Proceedings of the Workshop Held in Warsaw, 25--26 September 2009 , edited by P. Jaworski , F. Durante , W.K. Härdle and T. Rychlik , pp. 93–109, 2010 (Springer: Berlin and Heidelberg).
   Google Scholar
• Czado, C. , Brechmann, E.C. and Gruber, L. , Selection of vine copulas. In Copulae in Mathematical and Quantitative Finance: Proceedings of the Workshop Held in Cracow, 10--11 July 2012 , edited by P. Jaworski , F. Durante , and W.K. Härdle , pp. 17–37, 2013 (Springer: Berlin and Heidelberg).
   Google Scholar
• Diers, D. , Eling, M. and Marek, S.D. , Dependence modeling in non-life insurance using the Bernstein copula. Insur. Math. Econ. , 2012, 50 , 430–436.
   Web of Science ®Google Scholar
• Ding, P. , On the conditional distribution of the multivariate t distribution. Am. Stat. , 2016, 70 , 293–295.
   Web of Science ®Google Scholar
• Dissmann, J. , Brechmann, E.C. , Czado, C. and Kurowicka, D. , Selecting and estimating regular vine copulae and application to financial returns. Comp. Stat. Data. Anal. , 2013, 59 , 52–69.
   Web of Science ®Google Scholar
• Do, B. and Faff, R. , Does simple pairs trading still work? Financ. Anal. J. , 2010, 66 , 83–95.
   Web of Science ®Google Scholar
• Dragulescu, A.A. , xlsx: Read, write, format Excel 2007 and Excel 97/2000/XP/2003 files. R package version 0.5.7 , 2014.
   Google Scholar
• Durrleman, V. , Nikeghbali, A. and Roncalli, T. , Copulas approximation and new families. Working Paper, Crédit Lyonnais, 2000.
   Google Scholar
• Eaton, M.L. , Multivariate Statistics: A Vector Space Approach , 1983 (John Wiley & Sons: New York).
   Google Scholar
• Eddelbuettel, D. , Francois, R. , Allaire, J.J. , Ushey, K. , Kou, Q. , Bates, D. and Chambers, J. , Rcpp: Seamless R and C++ integration. J. Stat. Softw. , 2011, 40 (8), 1–18.
   Web of Science ®Google Scholar
• Fama, E.F. and French, K.R. , Multifactor explanations of asset pricing anomalies. J. Finance , 1996, 51 , 55–84.
   Web of Science ®Google Scholar
• Fama, E.F. and French, K.R. , A five-factor asset pricing model. J. Financ. Econ. , 2015, 116 , 1–22.
   Web of Science ®Google Scholar
• Fischer, M. , Köck, C. , Schlüter, S. and Weigert, F. , An empirical analysis of multivariate copula models. Quant. Finance , 2009, 9 , 839–854.
   Web of Science ®Google Scholar
• Fortin, I. and Kuzmics, C. , Tail-dependence in stock-return pairs. Int. Syst. Acc. Fin. Man. , 2002, 11 , 89–107.
   Google Scholar
• Gatev, E. , Goetzmann, W.N. and Rouwenhorst, K.G. , Pairs trading: Performance of a relative-value arbitrage rule. Rev. Financ. Stud. , 2006, 19 , 797–827.
   Web of Science ®Google Scholar
• Gohel, D. , ReporteRs: Microsoft Word, Microsoft PowerPoint and HTML documents generation. R package version 0.8.8 , 2016.
   Google Scholar
• Gonzalez-Fernandez, Y. and Soto, M. , vines: Multivariate dependence modeling with vines. R package version 1.1.5 , 2015.
   Google Scholar
• Heinen, A. and Valdesogo, A. , Asymmetric CAPM dependence for large dimensions: The canonical vine autoregressive model. Core Discussion Papers, Center for Operations Research and Econometrics, 2009.
   Google Scholar
• Hofert, M. , Kojadinovic, I. , Mächler, M. and Yan, J. , copula: Multivariate dependence with copulas. R package version 0.999-16 , 2015.
   Google Scholar
• Hothorn, T. , Zeileis, A. , Farebrother, R.W. and Cummins, C. , lmtest: Testing linear regression models. R package , 2015.
   Google Scholar
• Huck, N. , Pairs selection and outranking: An application to the S &P 100 index. Eur. J. Oper. Res. , 2009, 196 , 819–825.
   Web of Science ®Google Scholar
• Huck, N. , Pairs trading and outranking: The multi-step-ahead forecasting case. Eur. J. Oper. Res. , 2010, 207 , 1702–1716.
   Web of Science ®Google Scholar
• Jacobs, H. and Weber, M. , On the determinants of pairs trading profitability. J. Financ. Markets , 2015, 23 , 75–97.
   Web of Science ®Google Scholar
• Joe, H. , Multivariate extreme-value distributions with applications to environmental data. Can. J. Stat. , 1994, 22 , 47–64.
   Web of Science ®Google Scholar
• Joe, H. , Multivariate Models and Dependence Concepts, Monographs on Statistics and Applied Probability , 1st ed., Vol. 73, 1997 (Chapman & Hall: London).
   Google Scholar
• Joe, H. and Xu, J.K. , The estimation method of inference functions for margins for multivariate models. Working Paper, University of British Columbia, 1996.
   Google Scholar
• Knoll, J. , Stübinger, J. and Grottke, M. , Exploiting social media with higher-order factorization machines: Statistical arbitrage on high-frequency data of the S &P 500. FAU Discussion Papers in Economics (13), University of Erlangen-Nürnberg, 2017.
   Google Scholar
• Kotz, S. and Nadarajah, S. , Multivariate t-distributions and their Applications , 2004 (Cambridge University Press: Cambridge, United Kingdom).
   Google Scholar
• Krauss, C. , Statistical arbitrage pairs trading strategies: Review and outlook. J. Econ. Surv. , 2017, 31 , 513–545.
   Web of Science ®Google Scholar
• Krauss, C. , Do, X.A. and Huck, N. , Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S &P 500. Eur. J. Oper. Res. , 2017, 259 , 689–702.
   Web of Science ®Google Scholar
• Krauss, C. and Stübinger, J. , Non-linear dependence modelling with bivariate copulas: Statistical arbitrage pairs trading on the S &P 100. Appl. Econ. , 2017, 49 , 5352–5369.
   Web of Science ®Google Scholar
• Lau, C.A. , Xie, W. and Wu, Y. , Multi-dimensional pairs trading using copulas. Working Paper, Nanyang Technological University, 2016.
   Google Scholar
• Leifeld, P. , texreg: Conversion of R regression output to LaTeX or HTML tables. J. Stat. Softw. , 2013, 55 (8), 1–24.
   Web of Science ®Google Scholar
• Liew, R.Q. and Wu, Y. , Pairs trading: A copula approach. J. Dev. Hed. Fund. , 2013, 19 , 12–30.
   Google Scholar
• Liu, C. , Statistical analysis using the multivariate t distribution. PhD Thesis, Harvard University, 1994.
   Google Scholar
• Low, R.K.Y. , Alcock, J. , Faff, R. and Brailsford, T. , Canonical vine copulas in the context of modern portfolio management: Are they worth it? J. Bank. Finance , 2013, 37 , 3085–3099.
   Web of Science ®Google Scholar
• Lumley, T. and Zeileis, A. , sandwich: Robust covariance matrix estimators. R package version 2.4.0 , 2015.
   Google Scholar
• Mangold, B. , A multivariate linear rank test of independence based on a multiparametric copula with cubic sections. FAU Discussion Papers in Economics (10), 2015 (University of Erlangen-Nürnberg).
   Google Scholar
• Mina, J. and Xiao, J.Y. , Return to RiskMetrics: The evolution of a standard. RiskMetrics Group , 2001, 1 , 1–11.
   Google Scholar
• Nadarajah, S. and Kotz, S. , Mathematical properties of the multivariate t distribution. Acta App. Math. , 2005, 89 , 53–84.
   Web of Science ®Google Scholar
• Nelsen, R.B. , An Introduction to Copulas, Springer Series in Statistics , 2nd ed., 2006 (Springer: New York).
   Google Scholar
• Perlin, M.S. , M of a kind: A multivariate approach at pairs trading. Working Paper, ICMA/Reading University, 2007.
   Google Scholar
• Peterson, B.G. and Carl, P. , PerformanceAnalytics: Econometric tools for performance and risk analysis. R package version 1.4.3541 , 2014.
   Google Scholar
• Pfaff, B. and McNeil, A. , QRM: Provides R-language code to examine quantitative risk management concepts. R package , 2014.
   Google Scholar
• Pircalabu, A. and Jung, J. , A mixed C-vine copula model for hedging price and volumetric risk in wind power trading. Quant. Finance , 2017, 1 , 1–18.
   Google Scholar
• Rad, H. , Low, R.K.Y. and Faff, R. , The profitability of pairs trading strategies: Distance, cointegration and copula methods. Quant. Finance , 2016, 16 , 1541–1558.
   Web of Science ®Google Scholar
• Rose, D. , Modeling and estimating multivariate dependence structures with the Bernstein copula. PhD Thesis, LMU München, 2015.
   Google Scholar
• Ryan, J.A. , quantmod: Quantitative financial modelling framework. R package version 0.4-7 , 2015.
   Google Scholar
• Ryan, J.A. and Ulrich, J.M. , xts: eXtensible time series. R package , 2014.
   Google Scholar
• Sancetta, A. and Satchell, S. , The Bernstein copula and its applications to modeling and approximations of multivariate distributions. Eco. T. , 2004, 20 , 535–562.
   Web of Science ®Google Scholar
• Scheffer, M. and Weiß, G.N.F. , Smooth nonparametric Bernstein vine copulas. Quant. Finance , 2016, 17 , 139–156.
   Web of Science ®Google Scholar
• Schepsmeier, U. , Stoeber, J. , Brechmann, E.C. , Graeler, B. , Nagler, T. and Erhardt, T. , VineCopula: Statistical inference of vine copulas. R package , 2015.
   Google Scholar
• Schmid, F. and Schmidt, R. , Multivariate extensions of Spearman’s rho and related statistics. Stat. Probabil. Lett. , 2007, 77 , 407–416.
   Web of Science ®Google Scholar
• Simpson, G.L. , permute: Functions for generating restricted permutations of data. R package , 2015.
   Google Scholar
• Sklar, M. , Fonctions de répartition à n dimensions et leurs marges , pp. 229–231, 1959 (Publications de l’Institut de Statistique de l’Universit{\’e} de).
   Google Scholar
• Smith, M. , Min, A. , Almeida, C. and Czado, C. , Modeling longitudinal data using a pair-copula decomposition of serial dependence. J. Am. Stat. Assoc. , 2010, 105 , 1467–1479.
   Web of Science ®Google Scholar
• S\ &P Dow Jones Indices , Equity S\ &P 500 index. 2015. Available online at: https://us.spindices.com/indices/equity/sp-500
   Google Scholar
• Stander, Y. , Marais, D. and Botha, I. , Trading strategies with copulas. J. Eco. Fin. Sci. , 2013, 6 , 83–107.
   Google Scholar
• Stübinger, J. and Bredthauer, J. , Statistical arbitrage pairs trading with high-frequency data. Int. J. Econ. Financ. Iss. , 2017, 7 , 650–662.
   Google Scholar
• Stübinger, J. and Endres, S. , Pairs trading with a mean-reverting jump--diffusion model on high-frequency data. Quant. Finance , 2018. doi: 10.1080/14697688.2017.1417624
   PubMed Web of Science ®Google Scholar
• Trapletti, A. and Hornik, K. , tseries: Time series analysis and computational finance. R package version 0.10-38 , 2016.
   Google Scholar
• Ulrich, J. , TTR: Technical trading rules. R package version 0.23-1 , 2015.
   Google Scholar
• Valle, Dalla , L., de Giuli, M.E., Tarantola, C. and Manelli, C., Default probability estimation via pair copula constructions. Eur. J. Oper. Res. , 2016, 249 , 298–311.
   Web of Science ®Google Scholar
• Varadhan, R. , condMVNorm: Conditional multivariate normal distribution. R package , 2015.
   Google Scholar
• Weiß, G.N.F. and Supper, H. , Forecasting liquidity-adjusted intraday Value-at-risk with vine copulas. J. Bank. Finance , 2013, 37 , 3334–3350.
   Web of Science ®Google Scholar
• Whelan, N. , Sampling from Archimedean copulas. Quant. Finance , 2004, 4 , 339–352.
   Web of Science ®Google Scholar
• Wickham, H. and Francois, R. , dplyr: A grammar of data manipulation. R package version 0.5.0 , 2016.
   Google Scholar
• Wuertz, D. , fUnitRoots: Trends and unit roots. R package version 3010.78 , 2013.
   Google Scholar
• Rmetrics Core Team, Wuertz, D. and Setz, T. , fCopulae: Rmetrics - bivariate dependence structures with copulae. R package version 3042.82 , 2014.
   Google Scholar
• Rmetrics Core Team, Wuertz, D. , Setz, T. and Chalabi, Y. , timeSeries: Rmetrics -- Financial time series objects. R package version 3022.101.2 , 2015.
   Google Scholar
• Xie, W. , Liew, Q.R. , Wu, Y. and Zou, X. , Pairs trading with copulas, Working Paper, Nanyang Technological University, 2014.
   Google Scholar
• Xie, W. and Wu, Y. , Copula-based pairs trading strategy. In Asian Finance Association (AsFA) 2013 Conference , pp. 1–15, 2013 (Jiangxi University of Finance and Economics: Nanchang).
   Google Scholar
• Yang, J. , Chen, Z. , Wang, F. and Wang, R. , Composite Bernstein copulas. ASTIN Bulletin: The Journal of the IAA , 2015, 45 , 445–475.
   Web of Science ®Google Scholar
• Zeileis, A. and Grothendieck, G. , zoo: S3 infrastructure for regular and irregular time series. J. Stat. Softw. , 2005, 14 , 1–27.
   Web of Science ®Google Scholar