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An Empirical Analysis of the Swaption Cube

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  • Anders B. Trolle
  • Eduardo S. Schwartz

Abstract

We use a comprehensive database of inter-dealer quotes to conduct the first empirical analysis of the dynamics of the swaption cube. Using a model independent approach, we establish a set of stylized facts regarding the cross-sectional and time-series variation of conditional volatility and skewness of the swap rate distributions implied by the swaption cube. We then develop and estimate a dynamic term structure model that is consistent with these stylized facts, and use it to infer volatility and skewness of the risk-neutral and physical swap rate distributions. Finally, we investigate the fundamental drivers of these distributions. In particular, we find that volatility, volatility risk premia, skewness, and skewness risk premia are significantly related to the characteristics of agents' belief distributions for the macroeconomy, with GDP beliefs the most important factor in the USD market, and inflation beliefs the most important factor in the EUR market. This is consistent with differences in monetary policy objectives in the two markets.

Suggested Citation

  • Anders B. Trolle & Eduardo S. Schwartz, 2010. "An Empirical Analysis of the Swaption Cube," NBER Working Papers 16549, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:16549
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    1. Collin-Dufresne, Pierre & Goldstein, Robert S. & Jones, Christopher S., 2009. "Can interest rate volatility be extracted from the cross section of bond yields?," Journal of Financial Economics, Elsevier, vol. 94(1), pages 47-66, October.
    2. Beber, Alessandro & Brandt, Michael W., 2006. "The effect of macroeconomic news on beliefs and preferences: Evidence from the options market," Journal of Monetary Economics, Elsevier, vol. 53(8), pages 1997-2039, November.
    3. Frank de Jong & Joost Driessen & Antoon Pelsser, 2004. "On the Information in the Interest Rate Term Structure and Option Prices," Review of Derivatives Research, Springer, vol. 7(2), pages 99-127, August.
    4. Pierre Collin‐Dufresne & Robert S. Goldstein, 2002. "Do Bonds Span the Fixed Income Markets? Theory and Evidence for Unspanned Stochastic Volatility," Journal of Finance, American Finance Association, vol. 57(4), pages 1685-1730, August.
    5. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    6. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
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    Cited by:

    1. Kwai S. Leung & Hon Y. Ng & Hoi Y. Wong, 2014. "Stochastic Skew in the Interest Rate Cap Market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(12), pages 1146-1169, December.
    2. Linus Kaisajuntti & Joanne Kennedy, 2014. "Stochastic volatility for interest rate derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 14(3), pages 457-480, March.
    3. Marc Chataigner & Stéphane Crépey & Jiang Pu, 2020. "Nowcasting Networks," Post-Print hal-03910123, HAL.
    4. Marc Chataigner & Stephane Crepey & Jiang Pu, 2020. "Nowcasting Networks," Papers 2011.13687, arXiv.org.

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    More about this item

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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