Merton's model views equity as a call option on the asset of the firm. Thus the asset is partially observed through the equity. Then using nonlinear filtering an explicit expression for likelihood ratio for underlying parameters in terms of the nonlinear filter is obtained. As the evolution of the filter itself depends on the parameters in question, this does not permit direct maximum likelihood estimation, but does pave the way for the 'Expectation-Maximization' method for estimating parameters
In the first chapter of this thesis, I propose a nonlinear filtering method to estimate latent proce...
Abstract In this paper a new, information-based approach for modelling the dynamic evolution of a po...
This paper proposes a systematic method for forecasting default probabilities for financial firms wi...
Lending money has been one of the basic activities of banks for centuries. However, credit evaluatio...
The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models de...
One critical difficulty in implementing structural credit risk models is that the underlying asset v...
One critical di#culty in implementing Merton's (1974) credit risk model is that the underlying ...
A discrete time nonlinear filter is used to estimate the volatility in a financial model. New filter...
We study reduced-form portfolio credit risk models where the default intensities of the firms in a g...
This thesis proposes a novel credit risk model which deals with incomplete information on the firm's...
We consider a reduced-form credit risk model where default intensities and interest rate are functio...
To use an intensity-based model for portfolio credit risk using a collection of hidden Markov-modula...
In this article we discuss an intensity-based model for portfolio credit risk using a collection of ...
Moody’s KMV method is a popular commercial implementation of the structural credit risk model pionee...
In the first chapter of this thesis, I propose a nonlinear filtering method to estimate latent proce...
In the first chapter of this thesis, I propose a nonlinear filtering method to estimate latent proce...
Abstract In this paper a new, information-based approach for modelling the dynamic evolution of a po...
This paper proposes a systematic method for forecasting default probabilities for financial firms wi...
Lending money has been one of the basic activities of banks for centuries. However, credit evaluatio...
The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models de...
One critical difficulty in implementing structural credit risk models is that the underlying asset v...
One critical di#culty in implementing Merton's (1974) credit risk model is that the underlying ...
A discrete time nonlinear filter is used to estimate the volatility in a financial model. New filter...
We study reduced-form portfolio credit risk models where the default intensities of the firms in a g...
This thesis proposes a novel credit risk model which deals with incomplete information on the firm's...
We consider a reduced-form credit risk model where default intensities and interest rate are functio...
To use an intensity-based model for portfolio credit risk using a collection of hidden Markov-modula...
In this article we discuss an intensity-based model for portfolio credit risk using a collection of ...
Moody’s KMV method is a popular commercial implementation of the structural credit risk model pionee...
In the first chapter of this thesis, I propose a nonlinear filtering method to estimate latent proce...
In the first chapter of this thesis, I propose a nonlinear filtering method to estimate latent proce...
Abstract In this paper a new, information-based approach for modelling the dynamic evolution of a po...
This paper proposes a systematic method for forecasting default probabilities for financial firms wi...