Pair Trading in Tehran Stock Exchange based on Smooth Transition GARCH Model

Document Type : Original Article


1 Assistant Professor of Finance, Faculty of Management, University of Tehran, Iran. corresponding author

2 2. Assistant Professor of Finance, Faculty of Management, University of Tehran, Iran.

3 Master of Finance, University of Tehran, Tehran, Iran.


In this research, we use a pair trading strategy to make a profit in an emerging market. This is a statistical arbitrage strategy used for similar assets with dissimilar valuations. In the present study, smooth transition heteroskedastic models are used with the second-order logistic function for producing thresholds as trading entry and exit signals. For generating upper and lower bounds, we apply the rolling window approach and one-step-ahead quantile forecasting. Markov chain Monte Carlo sampling method is used for optimizing the parameters. Also, passive strategy in the out-of-sample period is used to compare the profits. The population consists of 36 daily stock returns in Tehran Stock Exchange. Then, we select ten pairs from these stocks and use Minimum Square Distance method, and five pairs from one industrial sector. Finally, we see strategy1 and 2 have positive returns in the out-of-sample period, and they produce higher returns than passive strategy.


Anderson, H.M, Nam, K., &Vahid, F. (1999). Asymmetric nonlinear smooth
transition GARCH models. Nonlinear Time Series Analysis of Economic and
Financial Data, 191-207.
Bollerslev, T. (1986). Generalized autoregressive conditional
heteroskedasticity. Journal of Econometrics, 307–327.
Cathy W. S. Chen, Monica M. C. Weng, Toshiaki Watanabe . (2017). Bayesian
forecasting of Value-at-Risk based on variant smooth transition heteroskedastic
models. Statistics and Its Interface, 451 – 470.
Cathy W.S. Chen, Zona Wang, Songsak Sriboonchitta, Sangyeol Lee. (2016).
Pair trading based on quantile forecasting of smooth transition GARCH
models. North American Journal of Economics and Finance, 38–55.
Chan, K. , & Tong, H. (1986). On estimating thresholds in autoregressive
models. Journal of Time Series Analysis, 178–190.
Chen, C. W. (2006). On a threshold heteroscedastic model. International
Journal of Forecasting, 73–89.
Chen, C. W. S., Chen, M., & Chen, S. Y. (2014). Pairs trading via three-regime
threshold autoregressive GARCH models. In Huynh et al. (Eds.) Modeling
Dependence in Econometrics, Advances in Intelligent Systems and Computing.
Switzerland: Springer International Publishing, 127–140.
Chivers, C. (2015). General Markov Chain Monte Carlo for Bayesian
Inference using adaptive Metropolis-Hastings sampling. website:
Elliott, R. J., van der Hoek, J., & Malcolm, W. P. (2005). Pairs trading.
Quantitative Finance, 271–276.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with
estimates of variance of United Kingdom inflation. Econometrica, 987–1008.
Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs trading:
performance of a relative value trading arbitrage rule. The Review of Financial
Studies, 797–827.
28 Iranian Journal of Finance
Gerlach, R., & Chen, C. W. S. (2008). Bayesian inference and model
comparison for asymmetric smooth transition heteroskedastic models. Statistics
and Computing, 391–408.
Girma, P. B. (1999). Risk arbitrage opportunities in petroleum futures spreads .
Journal of Futures Markets, 931–955.
González-Rivera, G. (1998). Smooth-transition GARCH models. Studies in
Nonlinear Dynamics & Econometrics, 61-78.
HP Mashele,SE Terblanche & JH Venter. (2015). Pairs trading on the
Johannesburg Stock Exchange. Investment Analysts Journal, 13-26.
Jorion, P. (1997). Value at risk: the new benchmark for controlling market risk.
Irwin Professional Pub.
Jung, Y. C. (2016). Relative performance of VIXC vs. GARCH in predicting
realised volatility change. Investment Analysts Journal, S1-S16.
Krauss, C. (2016). Statistical arbitrage pairs trading strategies: review and
outlook. Journal of Economic Surveys, 513–545.
Liu, J., & Timmermann, A. (2013). Optimal convergence trade strategies.
Review of Financial Studies, 1048–1086.
Martin, A. D., Quinn, K. M., & Park, J. H. (2011). MCMCpack: Markov chain
monte carlo in R.
Perlin, M. S. (2009). Evaluation of pairs-trading strategy at the Brazilian
financial market. Journal of Derivatives and Hedge Funds, 122–136.
Ross, S. (1976). The arbitrage theory of capital asset pricing . Journal of
Economic Theory, 341–360.
Skiena, S. (n.d.). Pair trading. Department of Computer Science (p. Lecture
23). State University of New York: Stony Brook, NY 11794-4400.
Teräsvirta, T. (1994). Specification, estimation, and evaluation of smooth
transition autoregressive models. Journal of the American Statistical
Association, 208–218.
Vidyamurthy, G. (2004). Pairs trading: Quantitative method and analysis.
Hoboken, New Jersey: John Wiley and Sons.