In this paper we 'update' the option implied probability of default (option iPoD) approach recently suggested in the literature. First, a numerically more stable objective function for the estimation of the risk neutral density is derived whose integrals can be solved analytically. Second, it is...

In this paper we 'update' the option implied probability of default (option iPoD) approach recently suggested in the literature. First, a numerically more stable objective function for the estimation of the risk neutral density is derived whose integrals can be solved analytically. Second, it is...

In this paper we ‘update’ the option implied probability of default (option iPoD) approach recently suggested in the literature. First, a numerically more stable objective function for the estimation of the risk neutral density is derived whose integrals can be solved analytically. Second,...

We analyze American put options in a hyper-exponential jump-diffusion model. Our contribution is threefold. Firstly, by following a maturity randomization approach, we solve the partial integro-differential equation and obtain a tight lower bound for the American option price. Secondly, our...

We develop a new efficient and analytically tractable method for estimation of parametric volatility models that is robust to price-level jumps and generally has good finite sample properties. The method entails first integrating intra-day data into the Realized Laplace Transform of volatility,...

This paper presents a new option pricing approach for all underlying assets that precisely fits the market data. We obtain the probability density function of the underlying asset without any external parameter. The density function for a given expiration date is uniquely determined by the...

In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate...

We present a new approach to identifying asset price bubbles based on options data. Given their forward-looking nature, options are ideal instruments with which to investigate market expectations about the future evolution of asset prices, which are key to understanding price bubbles. By...

Classical quantitative finance models such as the Geometric Brownian Motion or its later extensions such as local or stochastic volatility models do not make sense when seen from a physics-based perspective, as they are all equivalent to a negative mass oscillator with a noise. This paper...

The standard shifted lognormal model, defined by just two parameters, provides a remarkably good fit to the market implied volatilities of VIX options.Inspired by an analytic approximation derived by Lee and Wang, we propose a simple, intuitive extension that provides better empirical fits while...