50,045 research outputs found
Introduction to the special issue on neural networks in financial engineering
There are several phases that an emerging field goes through before it reaches maturity, and computational finance is no exception. There is usually a trigger for the birth of the field. In our case, new techniques such as neural networks, significant progress in computing technology, and the need for results that rely on more realistic assumptions inspired new researchers to revisit the traditional problems of finance, problems that have often been tackled by introducing simplifying assumptions in the past. The result has been a wealth of new approaches to these time-honored problems, with significant improvements in many cases
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
Spartan Random Processes in Time Series Modeling
A Spartan random process (SRP) is used to estimate the correlation structure
of time series and to predict (extrapolate) the data values. SRP's are
motivated from statistical physics, and they can be viewed as Ginzburg-Landau
models. The temporal correlations of the SRP are modeled in terms of
`interactions' between the field values. Model parameter inference employs the
computationally fast modified method of moments, which is based on matching
sample energy moments with the respective stochastic constraints. The
parameters thus inferred are then compared with those obtained by means of the
maximum likelihood method. The performance of the Spartan predictor (SP) is
investigated using real time series of the quarterly S&P 500 index. SP
prediction errors are compared with those of the Kolmogorov-Wiener predictor.
Two predictors, one of which explicit, are derived and used for extrapolation.
The performance of the predictors is similarly evaluated.Comment: 10 pages, 3 figures, Proceedings of APFA
Copulas in finance and insurance
Copulas provide a potential useful modeling tool to represent the dependence structure
among variables and to generate joint distributions by combining given marginal
distributions. Simulations play a relevant role in finance and insurance. They are used to
replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so
on. Using copulas, it is easy to construct and simulate from multivariate distributions based
on almost any choice of marginals and any type of dependence structure. In this paper we
outline recent contributions of statistical modeling using copulas in finance and insurance.
We review issues related to the notion of copulas, copula families, copula-based dynamic and
static dependence structure, copulas and latent factor models and simulation of copulas.
Finally, we outline hot topics in copulas with a special focus on model selection and
goodness-of-fit testing
Econometrics in R: Past, Present, and Future
Recently, computational methods and software have been receiving more attention in the econometrics literature, emphasizing that they are integral components of modern econometric research. This has also promoted the development of many new econometrics software packages written in R and made available on the Comprehensive R Archive Network. This special volume on "Econometrics in R" features a selection of these recent activities that includes packages for econometric analysis of cross-section, time series and panel data. This introduction to the special volume highlights the contents of the contributions and embeds them into a brief overview of other past, present, and future projects for econometrics in R.
Banking the unbanked: the Mzansi intervention in South Africa:
Purpose
This paper aims to understand household’s latent behaviour decision making in accessing financial services. In this analysis we look at the determinants of the choice of the pre-entry Mzansi account by consumers in South Africa.
Design/methodology/approach
We use 102 variables, grouped in the following categories: basic literacy, understanding financial terms, targets for financial advice, desired financial education and financial perception. Employing a computationally efficient variable selection algorithm we study which variables can satisfactorily explain the choice of a Mzansi account.
Findings
The Mzansi intervention is appealing to individuals with basic but insufficient financial education. Aspirations seem to be very influential in revealing the choice of financial services and to this end Mzansi is perceived as a pre-entry account not meeting the aspirations of individuals aiming to climb up the financial services ladder. We find that Mzansi holders view the account mainly as a vehicle for receiving payments, but on the other hand are debt-averse and inclined to save. Hence although there is at present no concrete evidence that the Mzansi intervention increases access to finance via diversification (i.e. by recruiting customers into higher level accounts and services) our analysis shows that this is very likely to be the case.
Originality/value
The issue of demand side constraints on access to finance have been largely ignored in the theoretical and empirical literature. This paper undertakes some preliminary steps in addressing this gap
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