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Threshold Autoregressive Modeling In Finance: The Price Differences Of Equivalent Assets

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  • Pradeep K. Yadav
  • Peter F. Pope
  • Krishna Paudyal

Abstract

Threshold autoregressive (TAR) models condition the first moment of a time series on lagged information using a step-function-type nonlinear structure. TAR techniques are expected to be relevant in financial time-series modeling in situations where deviations of prices from equilibrium values depend on discrete transaction costs and where market regulators follow intervention rules based on threshold values of control variables. an important finance application is in modeling the difference in prices of equivalent assets in the presence of transaction costs. the focus of this paper is on motivating the use of TAR models in this context and on the statistical estimation and testing procedures. the procedures are illustrated by modeling the difference between the prices of an index futures contract and the equivalent underlying cash index. It is found that the hypothesis of linearity is conclusively rejected in favor of threshold nonlinearity and that the estimated thresholds are largely consistent with arbitrage-related transaction costs. Copyright 1994 Blackwell Publishers.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Mathematical Finance.

Volume (Year): 4 (1994)
Issue (Month): 2 ()
Pages: 205-221

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Handle: RePEc:bla:mathfi:v:4:y:1994:i:2:p:205-221

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Cited by:
  1. Theissen, Erik, 2011. "Price discovery in spot and futures markets: A reconsideration," CFR Working Papers 09-17 [rev.], University of Cologne, Centre for Financial Research (CFR).
  2. van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001.
  3. Gaul, Jürgen & Theissen, Erik, 2008. "A partially linear approach to modelling the dynamics of spot and futures prices," CFS Working Paper Series 2008/12, Center for Financial Studies (CFS).
  4. Pavlidis Efthymios G & Paya Ivan & Peel David A, 2010. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-40, May.
  5. Conlin Lizieri & Steven Satchell & Elaine Worzala & Roberto Dacco', 1998. "Real Interest Regimes and Real Estate Performance: A Comparison of UK and US Markets," Journal of Real Estate Research, American Real Estate Society, vol. 16(3), pages 339-356.
  6. Chan, W.S. & Cheung, S.H., 2005. "A bivariate threshold time series model for analyzing Australian interest rates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(5), pages 429-437.
  7. repec:ntu:ntugeo:vol2-iss1-14-042 is not listed on IDEAS
  8. Zhang, Qi & Cai, Charlie X. & Keasey, Kevin, 2013. "Market reaction to earnings news: A unified test of information risk and transaction costs," Journal of Accounting and Economics, Elsevier, vol. 56(2), pages 251-266.
  9. Tse, Yiuman, 2001. "Index arbitrage with heterogeneous investors: A smooth transition error correction analysis," Journal of Banking & Finance, Elsevier, vol. 25(10), pages 1829-1855, October.
  10. Canto, Bea & Kräussl, Roman, 2007. "Electronic trading systems and intraday non-linear dynamics: An examination of the FTSE 100 cash and futures returns," CFS Working Paper Series 2007/20, Center for Financial Studies (CFS).
  11. Li, Ming-Yuan Leon, 2008. "Clarifying the dynamics of the relationship between option and stock markets using the threshold vector error correction model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 511-520.
  12. van Dijk, D.J.C. & Franses, Ph.H.B.F., 1997. "Nonlinear Error-Correction Models for Interest Rates in The Netherlands," Econometric Institute Research Papers EI 9704-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  13. Geoffrey F. Loudon & Wing H. Watt & Pradeep K. Yadav, 2000. "An empirical analysis of alternative parametric ARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 117-136.

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