Estimation of Default Risk using Stochastic Volatility Models: The case of Heston-CIR Model
George Jumbe , Ravi Gor
Default Risk, Stochastic volatility model, Heston-CIR model, MJD Merton model, the Merton model, MKMV model.
The volatility of an asset is a key component to pricing options. Stochastic volatility models were developed out of a need to modify the Black Scholes model for pricing options, which failed to effectively take the fact that the volatility of the price of the underlying security can change into account. The Black Scholes model instead makes the simplifying assumption that the volatility of the underlying security was constant. Stochastic volatility models correct for this by allowing the price volatility of the underlying security to fluctuate as a random variable. By allowing the price to vary, the stochastic volatility models improved the accuracy of calculations and forecasts. This study uses the stochastic volatility model: the Heston-CIR model, which is a combination of the stochastic volatility model discussed in Heston and the stochastic volatility model driven by Cox-Ingersoll-Ross (CIR) processes to predict the default risk and compare the results with the Merton jump diffusion (MJD), the traditional Merton and the Moody’s KMV (MKMV) models. Results show that, the Heston-CIR model predicts accurately the default risk as compared to other models.
Article Details
Unique Paper ID: 159842

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 618 - 626
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