IDEAS home Printed from https://ideas.repec.org/p/zbw/bubdp1/4172.html
   My bibliography  Save this paper

The Empirical Performance of Option Based Densities of Foreign Exchange

Author

Listed:
  • Keller, Joachim G.
  • Craig, Ben R.

Abstract

Risk neutral densities (RND) can be used to forecast the price of the underlying basis for the option, or it may be used to price other derivates based on the same sequence. The method adopted in this paper to calculate the RND is to firts estimate daily the diffusion process of the underlying futures contract for foreign exchange, based on the price of the American puts and calls reported on the Chicago Mercentile Exchange for the end of the day. This process implies a risk neutral density for each point of time in the future on each day. I order to estimate the diffusion process we need methods of calculating the prices of American options that are fast and accurate. The numercial problems posed by American options are tough. We solve the pricing of American options by using higher order lattices combined with smoothing the value function of the American Option at the boundaries in order to mitigate the non-differentiability of both the payoff boundary at expiration and the early exercise boundary. By calculating the price of an American option quickly, we can estimate the diffusion process by minimizing the squared distance between the calculated prices and the observed prices in the data. This paper also tests wheter the densities provided from American options provide a good forecasting tool. We use a non-parametric test of the densities that depends on inverse probabilities. A problem with the use of these tests in the past has been the time series nature of the transformed variables when the forecasting windows overlap. The inverse probability of the realized thirty day ahead spot at time t is correlated with the corresponding inverse probability at time t-1, because the development of the spot rate untill t shares twenty-nine days of history. We modify the tests based on the inverse probability function to account for this correlation between our random variables that are uniform distributed under the null hypothesis. We find that the densities based on the American option prices for foreign exchange do considerably well for the thirty to sixty day time horizon, but less well for the shorter horizons. The most sophisticated single state model of the diggusion process did best at the one-hundred-eighty day horizon.

Suggested Citation

  • Keller, Joachim G. & Craig, Ben R., 2002. "The Empirical Performance of Option Based Densities of Foreign Exchange," Discussion Paper Series 1: Economic Studies 2002,07, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdp1:4172
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/19564/1/200207dkp.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    2. Jackwerth, Jens Carsten & Rubinstein, Mark, 1996. "Recovering Probability Distributions from Option Prices," Journal of Finance, American Finance Association, vol. 51(5), pages 1611-1632, December.
    3. Breeden, Douglas T & Litzenberger, Robert H, 1978. "Prices of State-contingent Claims Implicit in Option Prices," The Journal of Business, University of Chicago Press, vol. 51(4), pages 621-651, October.
    4. Figlewski, Stephen & Gao, Bin, 1999. "The adaptive mesh model: a new approach to efficient option pricing," Journal of Financial Economics, Elsevier, vol. 53(3), pages 313-351, September.
    5. Clements, Michael P. & Smith, Jeremy, 2001. "Evaluating forecasts from SETAR models of exchange rates," Journal of International Money and Finance, Elsevier, vol. 20(1), pages 133-148, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ben R. Craig & Ernst Glatzer & Joachim G. Keller & Martin Scheicher, 2003. "The forecasting performance of German stock option densities," Working Papers (Old Series) 0312, Federal Reserve Bank of Cleveland.
    2. Gabriela De Raaij & Burkhard Raunig, 2005. "Evaluating density forecasts from models of stock market returns," The European Journal of Finance, Taylor & Francis Journals, vol. 11(2), pages 151-166.
    3. repec:onb:oenbwp:y::i:61:b:1 is not listed on IDEAS

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ivanova, Vesela & Puigvert Gutiérrez, Josep Maria, 2014. "Interest rate forecasts, state price densities and risk premium from Euribor options," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 210-223.
    2. Ben R. Craig & Joachim G. Keller, 2004. "The forecast ability of risk-neutral densities of foreign exchange," Working Papers (Old Series) 0409, Federal Reserve Bank of Cleveland.
    3. Ben R. Craig & Ernst Glatzer & Joachim G. Keller & Martin Scheicher, 2003. "The forecasting performance of German stock option densities," Working Papers (Old Series) 0312, Federal Reserve Bank of Cleveland.
    4. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    5. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    6. Peter Carr & Liuren Wu, 2014. "Static Hedging of Standard Options," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 3-46.
    7. Carole Bernard & Oleg Bondarenko & Steven Vanduffel, 2021. "A model-free approach to multivariate option pricing," Review of Derivatives Research, Springer, vol. 24(2), pages 135-155, July.
    8. Ricardo Crisóstomo, 2021. "Estimating real‐world probabilities: A forward‐looking behavioral framework," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(11), pages 1797-1823, November.
    9. Monteiro, Ana Margarida & Tutuncu, Reha H. & Vicente, Luis N., 2008. "Recovering risk-neutral probability density functions from options prices using cubic splines and ensuring nonnegativity," European Journal of Operational Research, Elsevier, vol. 187(2), pages 525-542, June.
    10. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    11. Jens Hilscher & Alon Raviv & Ricardo Reis, 2022. "Inflating Away the Public Debt? An Empirical Assessment," The Review of Financial Studies, Society for Financial Studies, vol. 35(3), pages 1553-1595.
    12. Lambrinoudakis, Costas & Skiadopoulos, George & Gkionis, Konstantinos, 2019. "Capital structure and financial flexibility: Expectations of future shocks," Journal of Banking & Finance, Elsevier, vol. 104(C), pages 1-18.
    13. Jarno Talponen, 2013. "Matching distributions: Asset pricing with density shape correction," Papers 1312.4227, arXiv.org, revised Mar 2018.
    14. Wolfgang Karl Härdle & Yarema Okhrin & Weining Wang, 2015. "Uniform Confidence Bands for Pricing Kernels," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 376-413.
    15. Hardeep Singh Mundi, 2023. "Risk neutral variances to compute expected returns using data from S&P BSE 100 firms—a replication study," Management Review Quarterly, Springer, vol. 73(1), pages 215-230, February.
    16. Chen, Ren-Raw & Hsieh, Pei-lin & Huang, Jeffrey, 2018. "Crash risk and risk neutral densities," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 162-189.
    17. Refet Gürkaynak & Justin Wolfers, 2005. "Macroeconomic Derivatives: An Initial Analysis of Market-Based Macro Forecasts, Uncertainty, and Risk," NBER Chapters, in: NBER International Seminar on Macroeconomics 2005, pages 11-50, National Bureau of Economic Research, Inc.
    18. Detlefsen, Kai & Härdle, Wolfgang Karl & Moro, Rouslan A., 2007. "Empirical pricing kernels and investor preferences," SFB 649 Discussion Papers 2007-017, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. Kliger, Doron & Levy, Ori, 2008. "Mood impacts on probability weighting functions: "Large-gamble" evidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 37(4), pages 1397-1411, August.
    20. Bakshi, Gurdip & Madan, Dilip & Panayotov, George, 2010. "Returns of claims on the upside and the viability of U-shaped pricing kernels," Journal of Financial Economics, Elsevier, vol. 97(1), pages 130-154, July.

    More about this item

    Keywords

    Risk-neutral densities from option prices; American exchange rate options; Evaluating Density Forecasts; Pentionomial tree; Density evaluation;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • F31 - International Economics - - International Finance - - - Foreign Exchange

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:bubdp1:4172. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/dbbgvde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.