This article studies the impact of long memory on volatility modelling and option pricing. We propose a general discrete-time pricing framework based on affine multi-component volatility models that admit ARCH(ccc) representations. This not only nests a large variety of option pricing models...

This paper introduces a method based on the use of various linear and nonlinear state space models that uses non-synchronous data to extract global stochastic financial trends (GST). These models are specifically constructed to take advantage of the intraday arrival of closing information coming...

We introduce a new strategy for the prediction of linear temporal aggregates, we call it "hybrid", and study its performance using asymptotic theory. This scheme consists of carrying out model parameter estimation with data sampled at the highest available frequency and the subsequent prediction...

This paper provides implementation details and application examples of the asymptotic error evaluation formulas introduced in the reference [GO14a] concerning three different approaches to the forecasting of linear temporal aggregates using estimated linear processes. The first two techniques...

Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a...