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

remarks on a computational estimator for the barrier option pricing in an iot scenario

Abstract The importance of derivatives in financial markets has known an exponential growth in the last decades, especially in risk management and speculation fields: this explains researchers' interest in answering questions about this kind of contracts. In particular, in this paper we restrict our attention on European vanilla and barrier options, and we propose a statistical procedure to solve efficiently the problem of determining the no arbitrage price of this type of derivatives in an IoT context: starting form an Internet of Things (IoT) data flow, an IoT system takes information from several sources and stores it into a suitable database; this information is used in our estimation problem. Our scheme is based on some strong assumptions about the market model, in particular the completeness of the market, the log-normality of the underlying asset with a constant volatility. We conclude this paper with an application of our framework to a real case

A Sequential Monte Carlo Approach for the pricing of barrier option in a Stochastic Volatility Model

In this paper we propose a numerical scheme to estimate the price of a barrier option in a general framework. More precisely, we extend a classical Sequential Monte Carlo approach, developed under the hypothesis of deterministic volatility, to Stochastic Volatility models, in order to improve the efficiency of Standard Monte Carlo techniques in the case of barrier options whose underlying approaches the barriers. The paper concludes with the application of our procedure to two case studies in a SABR model

Monte Carlo methods for barrier options.

This thesis focuses the attention on a very common class of Monte Carlo methods to price a barrier option, named standard Monte Carlo methods, and on their issues: the bias and the high variance. In order to overcome these issues, in this thesis we describe a particular class of statistical procedures, named Bayesian Monte Carlo methods. The thesis is divided into two parts: in the first part we present the main Bayesian Monte Carlo methods under the Black-Scholes model, in the second part we generalize these schemes under the assumption of stochastic volatility. As supported by numerical experiments, a Bayesian Sequential Monte Carlo estimator is unbiased and has a lower variance than a standard Monte Carlo one

An application of the one-factor HullWhite model in an IoT financial scenario

View references (17) In this paper we describe a financial data flow in an IoT scenario, which takes information from external databases and performs the following data operations: (i) analysis; (ii) check; (iii) filtering; (iv) reporting. In this way, financial institutions are able to offer traders better possibilities thanks to the knowledge of the different market variables. In particular, in our case data are used to determine the value of a bond in a Hull–White model; the dimension of datasets suggests us to implement parallel techniques in a statistical software. As a conclusion, we apply our framework to a real cas

Pricing estimation of a barrier option in an IoT scenario

IoT systems are able to manage very great amount of different types of data. In our paper we propose a mobile app which uses data processed by an IoT framework to estimate the price of a European barrier price. This software is based on an algorithm: in input it receives the values of maturity, strike price, interest rate, barrier level and in output it gives the value of the price. The algorithm implements a mathematical procedure involving numerical and statistical issues, as quadrature formulas and statistical tests. The validity of our methodology is verified by applying it to a real case

Remarks on a financial inverse problem by means of Monte Carlo Methods

Estimating the price of a barrier option is a typical inverse problem. In this paper we present a numerical and statistical framework for a market with risk-free interest rate and a risk asset, described by a Geometric Brownian Motion (GBM). After approximating the risk asset with a numerical method, we find the final option price by following an approach based on sequential Monte Carlo methods. All theoretical results are applied to the case of an option whose underlying is a real stoc

A computational method for the European option price in an Internet of Things framework

In an Internet of Things (IoT) scenario, sensors and devices are able to: (i) extract information from real; (ii) storage them into a database; (iii) use this information for inferring results by the implementation of very efficient algorithms. In this paper we present a computational schema in which, in order to price financial European options, an IoT framework processes data, as financial assets, expiration day, interest rate, strike price. In particular, this procedure involves mathematical tools for the estimation of the volatility and for the calculation of the no arbitrage price. The mathematical model is translated into an algorithm that is implemented in a mobile software. To test its validity, we apply our schema to a real case

A Sequential Monte Carlo Approach for the pricing of barrier option in a Stochastic Volatility Model

In this paper we propose a numerical scheme to estimate the price of a barrier option in a general framework. More precisely, we extend a classical Sequential Monte Carlo approach, developed under the hypothesis of deterministic volatility, to Stochastic Volatility models, in order to improve the efficiency of Standard Monte Carlo techniques in the case of barrier options whose underlying approaches the barriers. The paper concludes with the application of our procedure to two case studies under a SABR model

IoT application for the estimation of option price

In this paper, we develop an app for the estimation of European option price. We assume that in our market model all the assumptions of the Black-Scholes model are valid, in particular the absence of arbitrages opportunities and the log normality of the risk asset: they let us obtain an explicit and simple pricing expression, where the unknown terms are the volatility of the risk asset and the normal distribution. The first value is measured with the Average True Range, while the second one is calculated by using a Romberg quadrature formula