Applied Computational Intelligence for finance and economics

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

Computational Finance

Course "Computational Finance" gives an overview different partial differential equations arising in mathematical finance, their derivation procedures and numerical solution methods. In the computer labs students acquire practical skills for computing the prices of various financial options.BeSt programmi toetusel loodud e-kursuse "Computational Finance" õppematerjalid. Kursuse sisuks on finantsoptsioonide hindu kirjeldavate võrrandite tuletamine, nende lahendamiseks erinevate numbriliste meetodite konstrueerimine ning arvutiprogrammina realiseerimine. Materjalid sisaldavad loengukonspekti, praktikumide juhendeid ning näitelahendusi programmeerimiskeeles Python

Computational Finance

With the availability of new and more comprehensive financial market data, making headlines of massive public interest due to recent periods of extreme volatility and crashes, the field of computational finance is evolving ever faster thanks to significant advances made theoretically, and to the massive increase in accessible computational resources. This volume includes a wide variety of theoretical and empirical contributions that address a range of issues and topics related to computational finance. It collects contributions on the use of new and innovative techniques for modeling financial asset returns and volatility, on the use of novel computational methods for pricing, hedging, the risk management of financial instruments, and on the use of new high-dimensional or high-frequency data in multivariate applications in today’s complex world. The papers develop new multivariate models for financial returns and novel techniques for pricing derivatives in such flexible models, examine how pricing and hedging techniques can be used to assess the challenges faced by insurance companies, pension plan participants, and market participants in general, by changing the regulatory requirements. Additionally, they consider the issues related to high-frequency trading and statistical arbitrage in particular, and explore the use of such data to asses risk and volatility in financial markets

Kullback-Leibler simplex

This technical reference presents the functional structure and the algorithmic implementation of KL (Kullback-Leibler) simplex. It details the simplex approximation and fusion. The KL simplex is fundamental, robust, adaptive an informatics agent for computational research in economics, finance, game and mechanism. From this perspective the study provides comprehensive results to facilitate future work in such areas.KL divergence; second-order perceptron; informatics agent; simplex projection and fusion; computational economics-game-finance-mechanism

Monte Carlo evaluation of sensitivities in computational finance

In computational finance, Monte Carlo simulation is used to compute the correct prices for financial options. More important, however, is the ability to compute the so-called "Greeks'', the first and second order derivatives of the prices with respect to input parameters such as the current asset price, interest rate and level of volatility.\ud \ud This paper discusses the three main approaches to computing Greeks: finite difference, likelihood ratio method (LRM) and pathwise sensitivity calculation. The last of these has an adjoint implementation with a computational cost which is independent of the number of first derivatives to be calculated. We explain how the practical development of adjoint codes is greatly assisted by using Algorithmic Differentiation, and in particular discuss the performance achieved by the FADBAD++ software package which is based on templates and operator overloading within C++.\ud \ud The pathwise approach is not applicable when the financial payoff function is not differentiable, and even when the payoff is differentiable, the use of scripting in real-world implementations means it can be very difficult in practice to evaluate the derivative of very complex financial products. A new idea is presented to address these limitations by combining the adjoint pathwise approach for the stochastic path evolution with LRM for the payoff evaluation

Computational Finance Models

The author discusses his involvement in developing computational finance software. These computational finance models attempt to model the randomness of a stock\u27s price. At a fixed future time, a stock\u27s price is modeled as a random variable with a normal distribution centered about the current price adjusted with a simple growth multiplier. The standard deviation of this normal distribution depends on the length of time into the future one peers and the volatility of the market. As the market becomes more volatile and we look further ahead, the less likely the stock will have a price near the adjusted current price. Implementing these ideas requires a tool borrowed from physics called the Brownian motion. In a sense, a stock\u27s price is modeled as a point fluctuating about in dollar space . Hence a financial modeler can no more predict what price a stock will have at a given instance in time than a physicist can predict where a particular air molecule might be

Malliavin calculus in finance

This article is an introduction to Malliavin Calculus for practitioners. We treat one specific application to the calculation of greeks in Finance. We consider also the kernel density method to compute greeks and an extension of the Vega index called the local vega index.Malliavin claculus, computational finance, Greeks, Monte Carlo methods, kernel density method