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The Economic Value of Technical Trading Rules: A Non-parametric Utility-based Approach

Author

Listed:
  • Hans Dewachter

    (K.U.Leuven and Erasmus University Rotterdam)

  • Marco Lyrio

    (K.U.Leuven, C.E.S., International Economics)

Abstract

We adapt Brandt's (1999) nonparametric approach to determine the optimal portfolio choice of a risk averse foreign exchange investor who uses moving average trading signals as the information instrument for investment opportunities. Additionally, we assess the economic value of the estimated optimal trading rules based on the investor's preferences. The approach consists of a conditional generalized method of moments (GMM) applied to the conditional Euler optimality conditions. The method presents two main advantages: (i) it avoids ad hoc specifications of statistical models used to explain return predictability; and (ii) it implicitly incorporates all return moments in the investor's expected utility maximization problem. We apply the procedure to different moving average trading rules for the German mark- U.S. dollar exchange rate for the period 1973-2001. We find that technical trading rules are partially recovered and that the estimated optimal trading rules represent a significant economic value for the investor.

Suggested Citation

  • Hans Dewachter & Marco Lyrio, 2002. "The Economic Value of Technical Trading Rules: A Non-parametric Utility-based Approach," International Economics Working Papers Series ces0203, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, International Economics.
    • Handle: RePEc:kul:kulwps:ces0203
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    Citations

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    Cited by:

    1. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    2. Isakov, Dusan & Marti, Didier, 2011. "Technical Analysis with a Long-Term Perspective: Trading Strategies and Market Timing Ability," FSES Working Papers 421, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    3. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
    4. Lijun Wang & Haizhong An & Xiaohua Xia & Xiaojia Liu & Xiaoqi Sun & Xuan Huang, 2014. "Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, May.
    5. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    6. Dewachter, Hans & Lyrio, Marco, 2006. "The cost of technical trading rules in the Forex market: A utility-based evaluation," Journal of International Money and Finance, Elsevier, vol. 25(7), pages 1072-1089, November.
    7. Harrathi Nizar & Alhoshan Hamed M., 2020. "Validity of the Expectations Hypothesis of the Term Structure of Interest Rates: The Case of Saudi Arabia," Review of Middle East Economics and Finance, De Gruyter, vol. 16(1), pages 1-18, April.
    8. Joachim Inkmann & Zhen Shi, 2015. "Parametric Portfolio Policies in the Surplus Consumption Ratio," International Review of Finance, International Review of Finance Ltd., vol. 15(2), pages 257-282, June.

    More about this item

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    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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