This paper derives exact formulas for retrieving risk neutral moments of future payoffs of any order from generic European-style option prices. It also provides an exact formula for retrieving the expected quadratic variation of the stock market implied by European option prices, which nowadays...

This paper suggests using a multilayer artificial neural network (ANN) method, known as deep learning ANN, to predict the probability of default (PD) within the survival analysis framework. Deep learning ANN structures consider hidden interconnections among the covariates determining the PD...

This paper presents a new numerical method for pricing American call options when the volatility of the price of the underlying stock is stochastic. By exploiting a log-linear relationship of the optimal exercise boundary with respect to volatility changes, we derive an integral representation...

In this paper we introduce a new method of pricing an American call option by approximating its early exercise boundary based on Chebyshev polynomial functions. We implement the method to price options under the standard assumptions of the Black-Scholes model, for European options, and under...

This paper derives a general equilibrium option-pricing model for a European call assuming that the economy is exogenously driven by a dividend process following Hamilton's (1989) Markov regime switching model. The derived formula is used to investigate if the European call option prices are...

In this paper we introduce a pricing model for a European call option when the price of the underlying stock (asset) follows a random walk with Markov Chain type of shifts in the drift and volatility parameters according to the regime that the stock market lies in, at a given period of time. We...

In this paper we present a new method of approximating the risk neutral density (RND) from option prices based on the C-type Gram-Charlier series expansion (GCSE) of a probability density function. The exponential form of this type of GCSE guarantees that it will always give positive values of...

Based on a large micro data set of loan accounts consisting of restructured and non-restructured loans we investigate the ability of loan-specific and macroeconomic covariates to predict the probability of default (PD). We seek to investigate whether differences in the PD between the two...

In this paper a Bayesian approach to unit root testing for panel data models is proposed based on the comparison of stationary autoregressive models with and without individual determinist trends, with their counterpart models with unit autoregressive roots. This is done under cross-sectional...