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A quantitative mirror on the Euribor market using implied probability density functions

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  • de Vincent-Humphreys, Rupert
  • Puigvert Gutiérrez, Josep Maria

Abstract

This paper presents a set of probability density functions for Euribor outturns in three months’ time, estimated from the prices of options on Euribor futures. It is the first official and freely available dataset to span the complete history of Euribor futures options, thus comprising over ten years of daily data, from 13 January 1999 onwards. Time series of the statistical moments of these option-implied probability density functions are documented until April 2010. Particular attention is given to how these probability density functions, and their associated summary statistics, reacted to the unfolding financial crisis between 2007 and 2009. In doing so, it shows how option-implied probability density functions could be used to contribute to monetary policy and financial stability analysis. JEL Classification: C13, C14, G12, G13

Suggested Citation

  • de Vincent-Humphreys, Rupert & Puigvert Gutiérrez, Josep Maria, 2010. "A quantitative mirror on the Euribor market using implied probability density functions," Working Paper Series 1281, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20101281
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    References listed on IDEAS

    as
    1. Michael Rockinger & S. Coutant & Eric Jondeau, 1998. "Reading Interest Rate and Bond Futures Options' Smiles: How PIBOR and National Operators Appreciated the 1997 French Snap Election," Working Papers hal-00601499, HAL.
    2. Neuhaus, Holger, 1995. "The information content of derivatives for monetary policy: Implied volatilities and probabilities," Discussion Paper Series 1: Economic Studies 1995,03e, Deutsche Bundesbank.
    3. Bhupinder Bahra, 1997. "Implied risk-neutral probability density functions from option prices: theory and application," Bank of England working papers 66, Bank of England.
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    Citations

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    Cited by:

    1. Jukka Sihvonen & Sami Vähämaa, 2014. "Forward‐Looking Monetary Policy Rules and Option‐Implied Interest Rate Expectations," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(4), pages 346-373, April.
    2. Wolf, Elias & Montes-Galdón, Carlos & Paredes, Joan, 2024. "Conditional density forecasting: a tempered importance sampling approach," VfS Annual Conference 2024 (Berlin): Upcoming Labor Market Challenges 302442, Verein für Socialpolitik / German Economic Association.
    3. Abderrahmen Aloulou & Younes Boujelbene, 2019. "Dynamic analysis of implied risk neutral density," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 12(1), pages 39-58.
    4. Vergote, Olivier & Puigvert Gutiérrez, Josep Maria, 2012. "Interest rate expectations and uncertainty during ECB Governing Council days: Evidence from intraday implied densities of 3-month EURIBOR," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2804-2823.
    5. Michelle Lewis, 2012. "Market Perceptions of Exchange Rate Risk," Reserve Bank of New Zealand Analytical Notes series AN2012/12, Reserve Bank of New Zealand.
    6. Montes-Galdón, Carlos & Paredes, Joan & Wolf, Elias, 2022. "Conditional density forecasting: a tempered importance sampling approach," Working Paper Series 2754, European Central Bank.

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    More about this item

    Keywords

    financial; financial market; options; probability density functions;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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