1,009
Views
13
CrossRef citations to date
0
Altmetric
Research Papers

A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns

&
Pages 1727-1740 | Received 17 May 2018, Accepted 15 Feb 2019, Published online: 19 Mar 2019

References

  • Aït-Sahalia, Y. and Jacod, J. High-Frequency Financial Econometrics, 2014 (Princeton University Press: Princeton, NJ).
  • Alexander, C., Market Models: A Guide to Financial Data Analysis, 2001 (John Wiley & Sons: Chichester).
  • Altay, S., Colaneri, K. and Eksi, Z., Pairs trading under drift uncertainty and risk penalization. Working Paper, Vienna University of Technology, 2017.
  • Andersen, T.G., Bollerslev, T. and Das, A., Variance-ratio statistics and high-frequency data: Testing for changes in intraday volatility patterns. J. Finance, 2001, 56, 305–327. doi: 10.1111/0022-1082.00326
  • Ang, A. and Bekaert, G., International asset allocation with regime shifts. Rev. Financ. Stud., 2002, 15, 1137–1187. doi: 10.1093/rfs/15.4.1137
  • Avellaneda, M. and Lee, J.H., Statistical arbitrage in the US equities market. Quant. Finance, 2010, 10, 761–782. doi: 10.1080/14697680903124632
  • Bai, Y. and Wu, L., Analytic value function for optimal regime-switching pairs trading rules. Quant. Finance, 2018, 18, 637–654. doi: 10.1080/14697688.2017.1336281
  • Baronyan, S.R., Boduroğlu, İ.İ. and Şener, E., Investigation of stochastic pairs trading strategies under different volatility regimes. Manch. Sch., 2010, 78, 114–134. doi: 10.1111/j.1467-9957.2010.02204.x
  • Bee, M. and Gatti, G., An improved pairs trading strategy based on switching regime volatility. Working Paper, University of Trento, 2015.
  • Bertram, W.K., Analytic solutions for optimal statistical arbitrage trading. Phys. A: Stat. Mech. Appl., 2010, 389, 2234–2243. doi: 10.1016/j.physa.2010.01.045
  • Bertram, W.K., Optimal trading strategies for Itô diffusion processes. Phys. A: Stat. Mech. Appl., 2009, 388, 2865–2873. doi: 10.1016/j.physa.2009.04.004
  • Bock, M. and Mestel, R., A regime-switching relative value arbitrage rule. In Operations Research Proceedings 2008, edited by B. Fleischmann, K.H. Borgwardt, R. Klein and A. Tuma, pp. 9–14, 2009 (Springer: Berlin, Germany and Heidelberg, Germany).
  • Bollerslev, T., Litvinova, J. and Tauchen, G., Leverage and volatility feedback effects in high-frequency data. J. Financ. Econom., 2006, 4, 353–384. doi: 10.1093/jjfinec/nbj014
  • Bollinger, J., Using Bollinger bands. Stocks Commodities, 1992, 10, 47–51.
  • Bollinger, J., Bollinger on Bollinger Bands, 2001 (McGraw-Hill: New York, NY).
  • Bouchaud, J.P., Matacz, A. and Potters, M., Leverage effect in financial markets: The retarded volatility model. Phys. Rev. Lett., 2001, 87, 1–4. doi: 10.1103/PhysRevLett.87.228701
  • Cai, J., A Markov model of switching-regime ARCH. J. Bus. Econ. Stat., 1994, 12, 309–316.
  • Cai, N. and Kou, S.G., Option pricing under a mixed-exponential jump diffusion model. Manage. Sci., 2011, 57, 2067–2081. doi: 10.1287/mnsc.1110.1393
  • 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. doi: 10.1080/13504860500117503
  • Cartea, A., Gan, L. and Jaimungal, S., Trading cointegrated assets with price impact. Math. Finance, forthcoming, 2018.
  • Chang, K.L., Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model. Econ. Model., 2009, 26, 1283–1299. doi: 10.1016/j.econmod.2009.06.003
  • Chen, H., Chen, S., Chen, Z. and Li, F., Empirical investigation of an equity pairs trading strategy. Manage. Sci., 2017, 65, 370–389.
  • Chen, S.S., Predicting the bear stock market: Macroeconomic variables as leading indicators. J. Bank. Finance, 2009, 33, 211–223. doi: 10.1016/j.jbankfin.2008.07.013
  • Chevallier, J. and Goutte, S., On the estimation of regime-switching Lévy models. Stud. Nonlinear Dyn. Econom., 2017, 21, 3–29.
  • 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, Germany and Heidelberg, Germany).
  • Cont, R. and Mancini, C., Nonparametric tests for pathwise properties of semimartingales. Bernoulli, 2011, 17, 781–813. doi: 10.3150/10-BEJ293
  • Cummins, M. and Bucca, A., Quantitative spread trading on crude oil and refined products markets. Quant. Finance, 2012, 12, 1857–1875. doi: 10.1080/14697688.2012.715749
  • Dahlquist, M. and Gray, S.F., Regime-switching and interest rates in the European monetary system. J. Int. Econ., 2000, 50, 399–419. doi: 10.1016/S0022-1996(99)00005-7
  • Do, B., Faff, R. and Hamza, K., A new approach to modeling and estimation for pairs trading. In Proceedings of the Proceedings of 2006 Financial Management Association European Conference, 2006.
  • Do, B. and Faff, R., Does simple pairs trading still work?. Financ. Anal. J., 2010, 66, 83–95. doi: 10.2469/faj.v66.n4.1
  • Elliott, R.J. and Bradrania, R., Estimating a regime switching pairs trading model. Quant. Finance, 2018, 18, 877–883. doi: 10.1080/14697688.2017.1403035
  • Elliott, R.J., van der Hoek, J. and Malcolm, W.P., Pairs trading. Quant. Finance, 2005, 5, 271–276. doi: 10.1080/14697680500149370
  • Endres, S. and Stübinger, J., Optimal trading strategies for Lévy-driven Ornstein–Uhlenbeck processes. Appl. Econ., forthcoming, 2019.
  • Esquivel, M.L. and Mota, P.P., On some auto-induced regime switching double-threshold glued diffusions. J. Stat. Theory Pract., 2014, 8, 760–771. doi: 10.1080/15598608.2013.854184
  • Fischer, T., Krauss, C. and Treichel, A., Machine learning for time series forecasting-a simulation study. FAU Discussion Papers in Economics, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, 2018.
  • 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.
  • 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. doi: 10.1093/rfs/hhj020
  • Göncü, A. and Akyildirim, E., Statistical arbitrage with pairs trading. Int. Rev. Finance, 2016, 16, 307–319. doi: 10.1111/irfi.12074
  • Göncü, A. and Akyildirim, E., A stochastic model for commodity pairs trading. Quant. Finance, 2016, 16, 1843–1857. doi: 10.1080/14697688.2016.1211793
  • Göncü, A., Karahan, M.O. and Kuzubaş, T.U., A comparative goodness-of-fit analysis of distributions of some Lévy processes and Heston model to stock index returns. North Am. J. Econom. Finance, 2016, 36, 69–83. doi: 10.1016/j.najef.2015.12.001
  • Hamilton, J.D, Regime switching models. In Macroeconometrics and Time Series Analysis, pp. 202–209, 2010 (Palgrave Macmillan: London).
  • Hamilton, J.D. and Susmel, R., Autoregressive conditional heteroskedasticity and changes in regime. J. Econom., 1994, 64, 307–333. doi: 10.1016/0304-4076(94)90067-1
  • Hardy, M.R., A regime-switching model of long-term stock returns. N. Am. Actuar. J., 2001, 5, 41–53. doi: 10.1080/10920277.2001.10595984
  • Iacus, S.M., Simulation and Inference for Stochastic Differential Equations, 2008 (Springer Series in Statistics: New York).
  • 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. doi: 10.1016/j.jbankfin.2015.09.005
  • 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. Quant. Finance, 2018. Advance online publication. doi: 10.1080/14697688.2018.1521002.
  • Kou, S., Yu, C. and Zhong, H., Jumps in equity index returns before and during the recent financial crisis: A Bayesian analysis. Manage. Sci., 2017, 63, 988–1010. doi: 10.1287/mnsc.2015.2359
  • 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. doi: 10.1080/00036846.2017.1305097
  • Larsson, S., Lindberg, C. and Warfheimer, M., Optimal closing of a pair trade with a model containing jumps. Appl. Math., 2013, 58, 249–268. doi: 10.1007/s10492-013-0012-8
  • Li, Y. and Nolte, I., High-frequency volatility modelling: A Markov-switching autoregressive conditional intensity model. Working Paper, Lancaster University, 2016.
  • Lintilhac, P.S. and Tourin, A., Model-based pairs trading in the bitcoin markets. Quant. Finance, 2017, 17, 703–716. doi: 10.1080/14697688.2016.1231928
  • 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. doi: 10.1080/14697688.2016.1184304
  • Liu, F., Pantelous, A.A. and von Mettenheim, H.J., Forecasting and trading high frequency volatility on large indices. Quant. Finance, 2018, 18, 737–748. doi: 10.1080/14697688.2017.1414489
  • Liu, Z., Waggoner, D.F. and Zha, T., Sources of macroeconomic fluctuations: A regime-switching DSGE approach. Quant. Econom., 2011, 2, 251–301. doi: 10.3982/QE71
  • Mai, H., Drift estimation for jump diffusions: Time-continuous and high-frequency observations. Ph.D. thesis, Humboldt-Universität zu Berlin, 2012.
  • Mai, H., Efficient maximum likelihood estimation for Lévy-driven Ornstein-Uhlenbeck processes. Bernoulli, 2014, 20, 919–957. doi: 10.3150/13-BEJ510
  • Mancini, C., Non-parametric threshold estimation for models with stochastic diffusion coefficient and jumps. Scand. J. Stat., 2009, 36, 270–296. doi: 10.1111/j.1467-9469.2008.00622.x
  • Masuda, H., Approximate self-weighted LAD estimation of discretely observed ergodic Ornstein-Uhlenbeck processes. Electron. J. Stat., 2010, 4, 525–565. doi: 10.1214/10-EJS565
  • 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. doi: 10.5539/ijef.v6n3p96
  • Mota, P.P. and Esquivel, M.L., On a continuous time stock price model with regime switching, delay, and threshold. Quant. Finance, 2014, 14, 1479–1488. doi: 10.1080/14697688.2013.879990
  • Mota, P.P. and Esquivel, M.L., Model selection for stock prices data. J. Appl. Stat., 2016, 43, 2977–2987. doi: 10.1080/02664763.2016.1155205
  • Nath, G.C. and Dalvi, M., Day of the week effect and market efficiency – evidence from Indian equity market using high frequency data of National Stock Exchange. Working paper, Lansdale School of Business, 2004.
  • Pinson, P., Christensen, L.E., Madsen, H., Sørensen, P.E., Donovan, M.H. and Jensen, L.E., Regime-switching modelling of the fluctuations of offshore wind generation. J. Wind Eng. Ind. Aerodyn., 2008, 96, 2327–2347. doi: 10.1016/j.jweia.2008.03.010
  • QuantQuote, QuantQuote market data and software, 2016. Available online at: https://quantquote.com.
  • R Core Team, Stats: A language and environment for statistical computing. R package, 2017.
  • 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. doi: 10.1080/14697688.2016.1164337
  • Ramezani, C.A. and Zeng, Y., Maximum likelihood estimation of asymmetric jump-diffusion processes: Application to security prices. Working paper, California Polytechnic State University, 1998.
  • S&P Dow Jones Indices, S&P Global-Equity S&P 500 index, 2015. Available online at: https://us.spindices.com/indices/equity/sp-500.
  • Stübinger, J., Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quant. Finance, 2018. Advance online publication. doi: 10.1080/14697688.2018.1537503.
  • Stübinger, J. and Bredthauer, J., Statistical arbitrage pairs trading with high-frequency data. Int. J. Econom. Financ. Issues, 2017, 7, 650–662.
  • Stübinger, J. and Endres, S., Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. Quant. Finance, 2018, 18, 1735–1751. doi: 10.1080/14697688.2017.1417624
  • Stübinger, J., Mangold, B. and Krauss, C., Statistical arbitrage with vine copulas. Quant. Finance, 2018, 18, 1831–1849. doi: 10.1080/14697688.2018.1438642
  • Tourin, A. and Yan, R., Dynamic pairs trading using the stochastic control approach. J. Economic Dyn. Control, 2013, 37, 1972–1981. doi: 10.1016/j.jedc.2013.05.010
  • Uehara, Y., Statistical inference for misspecified ergodic Lévy driven stochastic differential equation models. Working Paper, Institute of Statistical Mathematics, Tokyo, 2017.
  • Vidyamurthy, G., Pairs Trading: Quantitative Methods and Analysis, 2004 (John Wiley & Sons: Hoboken, NJ).
  • Yang, J.W., Tsai, S.Y., Shyu, S.D. and Chang, C.C., Pairs trading: The performance of a stochastic spread model with regime switching-evidence from the S&P 500. Int. Rev. Economics Finance, 2016, 43, 139–150. doi: 10.1016/j.iref.2015.10.036
  • Yeo, J. and Papanicolaou, G., Risk control of mean-reversion time in statistical arbitrage. Risk Decis. Anal., 2017, 6, 263–290. doi: 10.3233/RDA-170132
  • Zeng, Z. and Lee, C.G., Pairs trading: Optimal thresholds and profitability. Quant. Finance, 2014, 14, 1881–1893. doi: 10.1080/14697688.2014.917806

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.