A method to price American-style option contracts in a limited information framework is introduced. The pricing methodology is based on sequential Monte Carlo techniques, as presented in Doucet, de Freitas, and Gordon's text "Sequential Monte Carlo Methods in Practice", and the least-squares...

We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic...

We argue that Islamic principles, in particular the avoidance of ribā and gharar should be applied with respect to real economic value rather than to monetary value in terms of conventional currency. In order to reconcile monetary value with economic value, we propose a reference currency...

We present an embarrassingly simple method for supervised learning of SABR model's European option price function based on lookup table or rote machine learning. Performance in time domain is comparable to generally used analytic approximations utilized in financial industry. However, unlike the...

Artificial Neural Networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between...

We show how to estimate affine term structure models from a panel of noisy bond yields using simulated maximum likelihood based on importance sampling. We approximate the likelihood function of the state-space representation of the model by correcting the likelihood function of a Gaussian...

We consider the subject of approximating tail probabilities in the general compound renewal process framework, where severity data are assumed to follow a heavy-tailed law (in that only the first moment is assumed to exist). By using weak convergence of compound renewal processes to Lévy...

In this article, we build on Chernobai et al. [1]'s procedure for modelling left-truncated data via a compound non-homogeneous Poisson process. The contribution we make is that we modify the fitting process introduced so that it is systematically applicable in the context of data that is not...

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A...

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...