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Credit Spread Modeling: Macro-financial versus HOC Approach


  • Sanja Dudakovic

    () (Franklin University Switzerland)


The aim of this paper is to throw light on the relationship between credit spread changes and past changes of U.S. macro-financial variables when invariants do not have Gaussian distribution. The first part presents the empirical analysis which is based on 10-year AAA corporate bond yields and 10-year Treasury bond yields. Explanatory variables include lagged U.S. leading index, Russell 2000 returns, BBB bond price changes interest rate swaps, exchange rates EUR/ USD, Repo rates, S& P 500 returns and S&P 500 volatility, Treasury bill changes, liquidity index-TRSW, LIBOR rates, Moody’s default rates; credit spread volatility and Treasury bills volatility. The proposed dynamical model explains 73% of the U.S. credit spread variance for the period 1999:07-2013:07. The second part of the article introduces the parameter estimation method based on higher order cumulants. It is demonstrated empirically that much of the information about variability of Credit Spread can be extracted from higher order cumulant function (85%).

Suggested Citation

  • Sanja Dudakovic, 2014. "Credit Spread Modeling: Macro-financial versus HOC Approach," Economic Analysis, Institute of Economic Sciences, vol. 47(3-4), pages 53-68.
  • Handle: RePEc:ibg:eajour:v:47:y:2014:i:3-4:p:53-68

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    Credit Spread Modeling; ARMA Parameter estimation; Higher Order Cumulants; Non Gaussian ARMA models; Dynamic regression;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies


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