Pairs trading with a mean-reverting jump–diffusion model on high-frequency data

References

• Andersen, T.G. , Bollerslev, T. , Frederiksen, P. and Ørregaard Nielsen, M., Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns. J. Appl. Econom., 2010, 25 , 233–261.
   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
• Ball, C.A. and Torous, W.N., On jumps in common stock prices and their impact on call option pricing. J. Finance, 1985, 40 , 155–173.
   Web of Science ®Google Scholar
• Barlow, M.T., A diffusion model for electricity prices. Math. Finance, 2002, 12 , 287–298.
   Web of Science ®Google Scholar
• Benth, F.E. , Kiesel, R. and Nazarova, A., A critical empirical study of three electricity spot price models. Energ. Econ., 2012, 34 , 1589–1616.
   Web of Science ®Google Scholar
• Bertram, W.K., An empirical investigation of Australian stock exchange data. Physica A, 2004, 341 , 533–546.
   Web of Science ®Google Scholar
• Bertram, W.K., Optimal trading strategies for Itô diffusion processes. Physica A, 2009, 388 , 2865–2873.
   Web of Science ®Google Scholar
• Bertram, W.K., Analytic solutions for optimal statistical arbitrage trading. Physica A, 2010, 389 , 2234–2243.
   Web of Science ®Google Scholar
• Boehmer, E. and Wu, J., Short selling and the price discovery process. Rev. Financ. Stud., 2012, 26 , 287–322.
   Web of Science ®Google Scholar
• Bogomolov, T., Pairs trading based on statistical variability of the spread process. Quant. Finance, 2013, 13 , 1411–1430.
   Google Scholar
• Bollerslev, T. , Todorov, V. and Li, S.Z., Jump tails, extreme dependencies, and the distribution of stock returns. J. Econometrics, 2013, 172 , 307–324.
   Web of Science ®Google Scholar
• Bollinger, J., Using Bollinger bands. Stock. & Commun., 1992, 10 , 47–51.
   Google Scholar
• Bollinger, J., Bollinger on Bollinger Bands, 2001 (McGraw-Hill: New York).
   Google Scholar
• Bowen, D. , Hutchinson, M.C. and O’Sullivan, N., High frequency equity pairs trading: Transaction costs, speed of execution and patterns in returns. J. Trad., 2010, 5 , 31–38.
   Google Scholar
• Bowen, D.A. and Hutchinson, M.C., Pairs trading in the UK equity market: Risk and return. Euro. J. Finance, 2015, 22 , 1363–1387.
   Web of Science ®Google Scholar
• Byrd, R.H. , Lu, P. , Nocedal, J. and Zhu, C., A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput., 1995, 16 , 1190–1208.
   Web of Science ®Google Scholar
• Carr, P. , Geman, H. , Madan, D.B. and Yor, M., The fine structure of asset returns: An empirical investigation. J. Bus., 2002, 75 , 305–332.
   Web of Science ®Google Scholar
• Cartea, Á. and Figueroa, M.G., Pricing in electricity markets: A mean reverting jump diffusion model with seasonality. Appl. Math. Finance, 2005, 12 , 313–335.
   Google Scholar
• Cartea, Á. , Jaimungal, S. and Penalva, J., Algorithmic and High-frequency Trading, 2015 (Cambridge University Press: Cambridge).
   Google Scholar
• Chan, K. , Chockalingam, M. and Lai, K.W.L., Overnight information and intraday trading behavior: Evidence from NYSE cross-listed stocks and their local market information. J. Multinat. Finan. Manage., 2000, 10 , 495–509.
   Google Scholar
• Chang, E.C. , Luo, Y. and Ren, J., Short-selling, margin-trading, and price efficiency: Evidence from the Chinese market. J. Bank. Finance, 2014, 48 , 411–424.
   Web of Science ®Google Scholar
• Clegg, M. and Krauss, C., Pairs trading with partial cointegration. Quant. Finance, 2018, 18 , 121–138.
   Web of Science ®Google Scholar
• Cont, R., Empirical properties of asset returns: Stylized facts and statistical issues. Quant. Finance, 2001, 1 , 223–236.
   Web of Science ®Google Scholar
• Cont, R., Volatility clustering in financial markets: Empirical facts and agent-based models. In Long Memory in Economics edited by G. Teyssière and A.P. Kirman, pp. 289–309, 2007 (Springer: Berlin).
   Google Scholar
• Cont, R. and Tankov, P., Financial Modelling with Jump Processes, Vol. 2., 2003 (CRC Press: London).
   Google Scholar
• Cummins, M. and Bucca, A., Quantitative spread trading on crude oil and refined products markets. Quant. Finance, 2012, 12 , 1857–1875.
   Web of Science ®Google Scholar
• Daouk, H. and Charoenrook, A.A., A study of market-wide short-selling restrictions. Working Paper, The Owen Graduate School of Management, 2005.
   Google Scholar
• D’Avolio, G., The market for borrowing stock. J. Financ. Econ., 2002, 66 , 271–306.
   Web of Science ®Google Scholar
• Diamond, D.W. and Verrecchia, R.E., Constraints on short-selling and asset price adjustment to private information. J. Financ. Econ., 1987, 18 , 277–311.
   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
• Do, B. and Faff, R., Are pairs trading profits robust to trading costs? J. Financ. Res., 2012, 35 , 261–287.
   Google Scholar
• Dragulescu, A.A., xlsx: Read, write, format Excel 2007 and Excel 97/2000/XP/2003 files. R package, 2014.
   Google Scholar
• Ekström, E. , Lindberg, C. and Tysk, J., Optimal liquidation of a pairs trade. In Advanced Mathematical Methods for Finance, edited by G. Di Nunno and B. Øksendal, pp. 247–255, 2011 (Springer: Berlin).
   Google Scholar
• Elliott, R.J. , van der Hoek, J. and Malcolm, W.P., Pairs trading. Quant. Finance, 2005, 5 , 271–276.
   Web of Science ®Google Scholar
• Escribano, A. , IgnacioPeña, J. and Villaplana, P., Modelling electricity prices: International evidence. Oxford Bull. Econ. Statist., 2011, 73 , 622–650.
   Web of Science ®Google Scholar
• Eydeland, A. and Wolyniec, K., Energy and Power Risk Management: New Developments in Modeling, Pricing, and Hedging, 2003 (John Wiley & Sons: Hoboken, NJ).
   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
• Gatev, E. , Goetzmann, W.N. and Rouwenhorst, K.G., Pairs trading: Performance of a relative value arbitrage rule. Working Paper, Yale School of Management’s International Center for Finance,1999.
   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
• Gloter, A. , Loukianova, D. and Mai, H., Jump filtering and efficient drift estimation for Lévy-driven SDE’s. Working Paper, Centre de Recherche en Economie et Statistique, 2016.
   Google Scholar
• Göncü, A. and Akyıldırım, E., Statistical arbitrage with pairs trading. Int. Rev. Finance, 2016a, 16 , 307–319.
   Web of Science ®Google Scholar
• Göncü, A. and Akyıldırım, E., A stochastic model for commodity pairs trading. Quant. Finance, 2016b, 16 , 1843–1857.
   Web of Science ®Google Scholar
• Hansen, P.R. and Lunde, A., A realized variance for the whole day based on intermittent high-frequency data. J. Financ. Economet., 2005, 3 , 525–554.
   Google Scholar
• Jain, A. , Jain, P.K. , McInish, T.H. and McKenzie, M., Worldwide reach of short selling regulations. J. Financ. Econ., 2013, 109 , 177–197.
   Web of Science ®Google Scholar
• Jegadeesh, N. and Titman, S., Returns to buying winners and selling losers: Implications for stock market efficiency. J. Finance, 1993, 48 , 65–91.
   Web of Science ®Google Scholar
• Jing, B.Y. , Kong, X.B. and Liu, Z., Modeling high-frequency financial data by pure jump processes. Ann. Stat., 2012, 759–784.
   Web of Science ®Google Scholar
• Jondeau, E. , Lahaye, J. and Rockinger, M., Estimating the price impact of trades in a high-frequency microstructure model with jumps. J. Bank. Finance, 2015, 61 , 205–224.
   Web of Science ®Google Scholar
• Kappou, K. , Brooks, C. and Ward, C., The S\ &P500 index effect reconsidered: Evidence from overnight and intraday stock price performance and volume. J. Bank. Finance, 2010, 34 , 116–126.
   Web of Science ®Google Scholar
• Kim, K.J., Financial time series forecasting using support vector machines. Neurocomputing, 2003, 55 , 307–319.
   Web of Science ®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 &P500. FAU Discussion Papers in Economics (13), University of Erlangen-Nürnberg, 2017.
   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
• Larsson, S. , Lindberg, C. and Warfheimer, M., Optimal closing of a pair trade with a model containing jumps. Appl. Math. Czech, 2013, 58 , 249–268.
   Web of Science ®Google Scholar
• Leifeld, P., texreg: Conversion of statistical model output in R to HTML tables. J. Stat. Softw., 2013, 55 , 1–24.
   Web of Science ®Google Scholar
• Leung, T. and Li, X., Optimal Mean Reversion Trading: Mathematical Analysis and Practical Applications, 2015 (World Scientific: Singapore).
   Google Scholar
• Liu, B. , Chang, L.B. and Geman, H., Intraday pairs trading strategies on high frequency data: The case of oil companies. Quant. Finance, 2017, 17 , 87–100.
   Web of Science ®Google Scholar
• Lucia, J.J. and Schwartz, E.S., Electricity prices and power derivatives: Evidence from the Nordic power exchange. Rev. Deriv. Res., 2002, 5 , 5–50.
   Google Scholar
• Lux, T. and Marchesi, M., Volatility clustering in financial markets: A microsimulation of interacting agents. Int. J. Theor. Appl. Finance, 2000, 3 , 675–702.
   Google Scholar
• Mai, H., Drift estimation for jump diffusions: Time-continuous and high-frequency observations. PhD Thesis, Humboldt-Universität zu, Berlin, 2012.
   Google Scholar
• Mai, H., Efficient maximum likelihood estimation for Lévy-driven Ornstein-Uhlenbeck processes. Bernoulli, 2014, 20 , 919–957.
   Web of Science ®Google Scholar
• Martens, M., Measuring and forecasting S &P 500 index-futures volatility using high-frequency data. J. Futures Markets, 2002, 22 , 497–518.
   Web of Science ®Google Scholar
• Merton, R.C., Option pricing when underlying stock returns are discontinuous. J. Financ. Econ., 1976, 3 , 125–144.
   Web of Science ®Google Scholar
• Merton, R.C., Continuous-time Finance, 1992 (Wiley-Blackwell: Cambridge).
   Google Scholar
• Meyer-Brandis, T. and Tankov, P., Multi-factor jump-diffusion models of electricity prices. Int. J. Theor. Appl. Finance, 2008, 11 , 503–528.
   Google Scholar
• Miao, G.J., High frequency and dynamic pairs trading based on statistical arbitrage using a two-stage correlation and cointegration approach. Int. J. Econ. Finance, 2014, 6 , 96–110.
   Google Scholar
• Mina, J. and Xiao, J.Y., Return to RiskMetrics: The evolution of a standard. RiskMetrics Group, 2001.
   Google Scholar
• Ng, H.S. , Lam, K.P. and Lam, S.S. (eds.), Incremental Genetic Fuzzy Expert Trading System for Derivatives Market Timing, 2003 (Wiley: London).
   Google Scholar
• Ou, J.A. and Penman, S.H., Financial statement analysis and the prediction of stock returns. J. Account. Econ., 1989, 11 , 295–329.
   Web of Science ®Google Scholar
• Peterson, B.G. and Carl, P., Performance analytics: Econometric tools for performance and risk analysis. R package, 2014.
   Google Scholar
• Pfaff, B. and McNeil, A., QRM: Provides R-Language code to examine quantitative risk management concepts. R package, 2016.
   Google Scholar
• Platen, E. and Bruti-Liberati, N., Numerical Solution of Stochastic Differential Equations with Jumps in Finance, 2010 (Springer Verlag: Berlin).
   Google Scholar
• QuantQuote , QuantQuote market data and software, 2016. Available online at: https://www.quantquote.com/
   Google Scholar
• R CoreTeam , stats: A language and environment for statistical computing. R package, 2017.
   Google Scholar
• Rad, H. , Low, R.K.Y. and Faff, R.W., The profitability of pairs trading strategies: Distance, cointegration and copula methods. Quant. Finance, 2016, 1–18.
   Web of Science ®Google Scholar
• Riedel, C. and Wagner, N., Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns. J. Int. Financ. Mark. I., 2015, 39 , 53–64.
   Web of Science ®Google Scholar
• Rmetrics Core Team, Wuertz, D. , Setz, T. and Chalabi, Y., fBasics: Rmetrics - Markets and basic statistics. R package, 2014.
   Google Scholar
• Rmetrics Core Team, Wuertz, D. , Setz, T. and Chalabi, Y., timeSeries: Rmetrics - Financial time series objects. R package, 2015.
   Google Scholar
• Ryan, J.A., quantmod: Quantitative financial modelling framework. R package, 2016.
   Google Scholar
• Ryan, J.A. and Ulrich, J.M., xts: eXtensible time series. R package, 2014.
   Google Scholar
• Saffi, P.A.C. and Sigurdsson, K., Price efficiency and short selling. Rev. Financ. Stud., 2010, 24 , 821–852.
   Web of Science ®Google Scholar
• Seifert, J. and Uhrig-Homburg, M., Modelling jumps in electricity prices: Theory and empirical evidence. Rev. Deriv. Res., 2007, 10 , 59–85.
   Google Scholar
• Soetaert, K., rootSolve: Nonlinear root finding, equilibrium and steady-state analysis of ordinary differential equations. R package, 2009.
   Google Scholar
• S &P 500 , Dow Jones Indices, Equity S &P 500, 2015. Available online at: http://us.spindices.com/indices/equity/sp-500
   Google Scholar
• Stübinger, J. and Bredthauer, J., Statistical arbitrage pairs trading with high-frequency data. Int. J. Econ. Financ. Iss., 2017, 7 (4), 650–662.
   Google Scholar
• Stübinger, J. , Mangold, B. and Krauß, C., Statistical arbitrage with vine copulas. FAU Discussion Papers in Economics (13), University of Erlangen-Nürnberg, 2016.
   Google Scholar
• Trapletti, A. and Hornik, K., tseries: Time series analysis and computational finance. R package, 2017.
   Google Scholar
• Tsiakas, I., Overnight information and stochastic volatility: A study of European and US stock exchanges. J. Bank. Finance, 2008, 32 , 251–268.
   Web of Science ®Google Scholar
• Ulrich, J., TTR: Technical trading rules. R package, 2016.
   Google Scholar
• Venables, W.N. and Ripley, B.D., Modern Applied Statistics with S, 4th ed., 2002 (Springer: New York).
   Google Scholar
• Vidyamurthy, G., Pairs Trading: Quantitative Methods and Analysis, 2004 (John Wiley & Sons: Hoboken, NJ).
   Google Scholar
• Villaplana, P., Pricing power derivatives: A two-factor jump-diffusion approach. Working Paper, Universitat Pompeu Fabra, 2003.
   Google Scholar
• Wickham, H., readxl: Read Excel files. R package, 2016.
   Google Scholar
• Wickham, H. and Francois, R., dplyr: A grammar of data manipulation. R package, 2016.
   Google Scholar
• Wickham, H. , Hester, J. and Francois, R., readr: Read tabular data. R package, 2016.
   Google Scholar
• Zeileis, A., Object-oriented computation of Sandwich estimators. J. Stat. Softw., 2006, 16 , 1–16.
   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
• Zeileis, A. and Hothorn, T., Diagnostic checking in regression relationships. R News, 2002, 2 , 7–10.
   Google Scholar
• Zeng, Z. and Lee, C.G., Pairs trading: Optimal thresholds and profitability. Quant. Finance, 2014, 14 , 1881–1893.
   Web of Science ®Google Scholar