Modelling Rating Transitions
AbstractThe time-continuous discrete-state Markov process is a model for rating transitions. One parameter, namely the intensity to migrate to an adjacent rating state, implies an ordinal rating to have an intuitive metric. State-specific intensities generalize the state-stationarity. Observing Markov processes from a multiplicative intensity model, the maximum likelihood parameter estimators for both models can be written as a martingale transform of the processes that count transitions between the rating states. A Taylor expansion reveals consistency and asymptotic normality of the parameter estimates, resulting in a chi-square-distributed likelihood ratio of state-stationarity and the state-specific model. This extents to time-stationarity. Simulations contrast the asymptotic results with finite samples. An application to a sufficiently large set of credit rating histories shows that the one-parameter model can be a good starting point. --
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Bibliographic InfoPaper provided by Verein für Socialpolitik / German Economic Association in its series Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis with number 48698.
Date of creation: 2011
Date of revision:
Rating; Metricality; Multiple Markov process; Counting process; Likelihood ratio;
Find related papers by JEL classification:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
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