IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v2y2019i2p16-227d221321.html
   My bibliography  Save this article

Foreign Exchange Expectation Errors and Filtration Enlargements

Author

Listed:
  • Pedro L. P. Chaim

    (FEA-RP/USP, Ribeirão Preto 14040-950, Brazil
    Current address: Av. dos Bandeirantes 3900, Ribeirão Preto 14040-950, Brazil.
    These authors contributed equally to this work.)

  • Márcio P. Laurini

    (FEA-RP/USP, Ribeirão Preto 14040-950, Brazil
    These authors contributed equally to this work.)

Abstract

Extrapolations of future market forward rates are a better predictor of the 30-days ahead BRL-USD exchange rate than forecasts from the Central Bank Focus survey of Brazilian market participants. This is puzzling because market participants observe forward rates as they submit predictions, and thus these agents perform biased forecasts even though they have access to a set of unbiased forecasts, consistent with a martingale process for the exchange rate. We argue that this rational conundrum can be explained by a mechanism through which new information enlarges the information set (a filtration), changing the underlying measure and inducing a drift into the martingale process, turning the process into a strict local martingale and generating a forecast bias. Empirical results suggest that Focus survey forecasts indeed display characteristics of a strict local martingale, while spot exchange rates and forward rates are consistent with a martingale process.

Suggested Citation

  • Pedro L. P. Chaim & Márcio P. Laurini, 2019. "Foreign Exchange Expectation Errors and Filtration Enlargements," Stats, MDPI, vol. 2(2), pages 1-16, April.
  • Handle: RePEc:gam:jstats:v:2:y:2019:i:2:p:16-227:d:221321
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/2/2/16/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-905X/2/2/16/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    2. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2011. "Small sample properties of alternative tests for martingale difference hypothesis," Economics Letters, Elsevier, vol. 110(2), pages 151-154, February.
    3. Manuel Dominguez & Ignacio Lobato, 2003. "Testing the Martingale Difference Hypothesis," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 351-377.
    4. Baldeaux, Jan & Ignatieva, Katja & Platen, Eckhard, 2018. "Detecting money market bubbles," Journal of Banking & Finance, Elsevier, vol. 87(C), pages 369-379.
    5. Ai[diaeresis]t-Sahalia, Yacine & Kimmel, Robert, 2007. "Maximum likelihood estimation of stochastic volatility models," Journal of Financial Economics, Elsevier, vol. 83(2), pages 413-452, February.
    6. Jiang, George J. & Knight, John L., 1997. "A Nonparametric Approach to the Estimation of Diffusion Processes, With an Application to a Short-Term Interest Rate Model," Econometric Theory, Cambridge University Press, vol. 13(5), pages 615-645, October.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    9. Leif Andersen & Vladimir Piterbarg, 2007. "Moment explosions in stochastic volatility models," Finance and Stochastics, Springer, vol. 11(1), pages 29-50, January.
    10. Philip Protter & Aditi Dandapani, 2019. "Strict Local Martingales and the Khasminskii test for Explosions," Papers 1903.02383, arXiv.org.
    11. Chaim, Pedro & Laurini, Márcio P., 2019. "Is Bitcoin a bubble?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 222-232.
    12. Petteri Piiroinen & Lassi Roininen & Tobias Schoden & Martin Simon, 2018. "Asset Price Bubbles: An Option-based Indicator," Papers 1805.07403, arXiv.org, revised Jul 2018.
    Full references (including those not matched with items 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. Cai, Lili & Swanson, Norman R., 2011. "In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982-2008," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 743-764, September.
    2. Chaim, Pedro & Laurini, Márcio P., 2019. "Is Bitcoin a bubble?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 222-232.
    3. Márcio P. Laurini & Pedro Chaim, 2021. "Brazilian stock market bubble in the 2010s," SN Business & Economics, Springer, vol. 1(1), pages 1-19, January.
    4. Chen, Bin & Song, Zhaogang, 2013. "Testing whether the underlying continuous-time process follows a diffusion: An infinitesimal operator-based approach," Journal of Econometrics, Elsevier, vol. 173(1), pages 83-107.
    5. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2010. "Properties of Foreign Exchange Risk Premia," MPRA Paper 21302, University Library of Munich, Germany.
    6. Park, Yang-Ho, 2016. "The effects of asymmetric volatility and jumps on the pricing of VIX derivatives," Journal of Econometrics, Elsevier, vol. 192(1), pages 313-328.
    7. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    8. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    9. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    10. Andrei Cozma & Christoph Reisinger, 2015. "A mixed Monte Carlo and PDE variance reduction method for foreign exchange options under the Heston-CIR model," Papers 1509.01479, arXiv.org, revised Apr 2016.
    11. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.
    12. Cui, Yiran & del Baño Rollin, Sebastian & Germano, Guido, 2017. "Full and fast calibration of the Heston stochastic volatility model," European Journal of Operational Research, Elsevier, vol. 263(2), pages 625-638.
    13. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    14. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    15. Almut Veraart & Luitgard Veraart, 2012. "Stochastic volatility and stochastic leverage," Annals of Finance, Springer, vol. 8(2), pages 205-233, May.
    16. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    17. Heejoon Han & Myung D. Park, 2013. "Comparison of Realized Measure and Implied Volatility in Forecasting Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 522-533, September.
    18. Nicolas Langrené & Geoffrey Lee & Zili Zhu, 2016. "Switching To Nonaffine Stochastic Volatility: A Closed-Form Expansion For The Inverse Gamma Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1-37, August.
    19. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    20. Abdullah Almansour and Margaret Insley, 2016. "The Impact of Stochastic Extraction Cost on the Value of an Exhaustible Resource: An Application to the Alberta Oil Sands," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).

    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:gam:jstats:v:2:y:2019:i:2:p:16-227:d:221321. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.