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Profitability of Trading in the Direction of Asset Price Jumps - Analysis of Multiple Assets and Frequencies

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  • Milan Fičura

Abstract

Profitability of a trading system based on the momentum-like effects of asset price jumps was tested on four currency markets (EUR/USD, GBP/USD, USD/CHF and USD/JPY) and three futures markets (Light Crude Oil, E-Mini S&P 500 and VIX), on 7 frequencies (1-minute to 1-day), over a period of more than 20 years. The proposed trading system entered long and short trades in the direction of asset price jumps and held the positions for a fixed horizon, optimized on the in-sample period. The system achieved statistically significant out-sample profits for the USD/CHF, EUR/USD and GBP/USD exchange rates, especially on the 15-minute, 30-minute and 1-hour frequencies, with expected returns of up to 20-30% p.a., including transaction costs. On the 1-day frequency, on the USD/JPY and on the three analysed futures markets, only insignificant profits or losses were achieved. On the 1-minute frequency, the system ended with a loss for all of the assets.

Suggested Citation

  • Milan Fičura, 2019. "Profitability of Trading in the Direction of Asset Price Jumps - Analysis of Multiple Assets and Frequencies," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(4), pages 385-401.
    • Handle: RePEc:prg:jnlpep:v:2019:y:2019:i:4:id:703:p:385-401
    DOI: 10.18267/j.pep.703
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    References listed on IDEAS

    as
    1. Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
    2. repec:hal:journl:peer-00741630 is not listed on IDEAS
    3. Hanousek Jan & Kočenda Evžen & Novotný Jan, 2012. "The identification of price jumps," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 53-77, January.
    4. Jiang, George J. & Oomen, Roel C.A., 2008. "Testing for jumps when asset prices are observed with noise-a "swap variance" approach," Journal of Econometrics, Elsevier, vol. 144(2), pages 352-370, June.
    5. Novotný, Jan & Petrov, Dmitri & Urga, Giovanni, 2015. "Trading price jump clusters in foreign exchange markets," Journal of Financial Markets, Elsevier, vol. 24(C), pages 66-92.
    6. repec:taf:jnlbes:v:30:y:2012:i:2:p:242-255 is not listed on IDEAS
    7. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    asset price jumps; L-estimator; high-frequency trading; momentum trading;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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