analysis of a data flow in a financial iot system

Abstract Data retrieving, analysis e management are usually known as complex task in financial contexts. In an Internet of Things (IoT) system data-flow processes represent the knowledge base used in mathematical models for credits and financial products. Several sources such as distributed database systems, portals and local information are generally used as input of inferring models. In this paper we describe an overview of software tools, methodologies and strategies in real data-flow system

Meshless Methods for Option Pricing and Risks Computation

In this thesis we price several financial derivatives by means of radial basis functions. Our main contribution consists in extending the usage of said numerical methods to the pricing of more complex derivatives - such as American and basket options with barriers - and in computing the associated risks. First, we derive the mathematical expressions for the prices and the Greeks of given options; next, we implement the corresponding numerical algorithm in MATLAB and calculate the results. We compare our results to the most common techniques applied in practice such as Finite Differences and Monte Carlo methods. We mostly use real data as input for our examples. We conclude radial basis functions offer a valid alternative to current pricing methods, especially because of the efficiency deriving from the free, direct calculation of risks during the pricing process. Eventually, we provide suggestions for future research by applying radial basis function for an implied volatility surface reconstruction