Review of modern numerical methods for a simple vanilla option pricing problem

Option pricing is a very attractive issue of financial engineering and optimization. The problem of determining the fair price of an option arises from the assumptions made under a given financial market model. The increasing complexity of these market assumptions contributes to the popularity of the numerical treatment of option valuation. Therefore, the pricing and hedging of plain vanilla options under the Black–Scholes model usually serve as a bench-mark for the development of new numerical pricing approaches and methods designed for advanced option pricing models. The objective of the paper is to present and compare the methodological concepts for the valuation of simple vanilla options using the relatively modern numerical techniques in this issue which arise from the discontinuous Galerkin method, the wavelet approach and the fuzzy transform technique. A theoretical comparison is accompanied by an empirical study based on the numerical verification of simple vanilla option prices. The resulting numerical schemes represent a particularly effective option pricing tool that enables some features of options that are depend-ent on the discretization of the computational domain as well as the order of the polynomial approximation to be captured better

Investment Risk Appraisal

Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. This approach may account for what occurs most of the time in the market, but the picture it presents does not reflect the reality, as the major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we discuss the grounds for classical asset pricing model revision and argue that the demand for relaxed assumptions appeals for another approach to modelling the market environment

Determination of economic systems behaviour under uncertainty

The paper discuses systems of difference equations with fuzzy parameters and presents some solution procedures with the purpose to study the dynamic behaviour of economic systems in case of uncertainty. The trajectories of the endogenous variables are evaluated firstly at contiguous moments of time, and then, simultaneously. The relations between different solutions are shown. The author also consider essential to provide an algorithm for computing the exact α-cuts of the obtained solution

Soft computing in investment appraisal

Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. Such approach accounts for what occurs most of the time in the market, but does not reflect the reality, as major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies traded on the London Stock Exchange and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we suggest a specific evolutionary algorithm to train a fuzzy neural net - the bidirectional incremental evolution will automatically identify the complexity of the problem and correspondingly adapt the parameters of the fuzzy network

Valuation of real estate investments through Fuzzy Logic

This paper aims to outline the application of Fuzzy Logic in real estate investment. In literature, there is a wide theoretical background on real estate investment decisions, but there has been a lack of empirical support in this regard. For this reason, the paper would fill the gap between theory and practice. The fuzzy logic system is adopted to evaluate the situations of a real estate market with imprecise and vague information. To highlight the applicability of the Possibility Theory, we proceeded to reconsider an example of property investment evaluation through fuzzy logic. The case study concerns the purchase of an office building. The results obtained with Fuzzy Logic have been also compared with those arising from a deterministic approach through the use of crisp numbers

Application of Adaptive Νeuro-Fuzzy Inference System in Interest Rates Effects on Stock Returns

In the current study we examine the effects of interest rate changes on common stock returns of Greek banking sector. We examine the Generalized Autoregressive Heteroskedasticity (GARCH) process and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The conclusions of our findings are that the changes of interest rates, based on GARCH model, are insignificant on common stock returns during the period we examine. On the other hand, with ANFIS we can get the rules and in each case we can have positive or negative effects depending on the conditions and the firing rules of inputs, which information is not possible to be retrieved with the traditional econometric modelling. Furthermore we examine the forecasting performance of both models and we conclude that ANFIS outperforms GARCH model in both in-sample and out-of-sample periods

The utilization of coffee waste into fired clay brick

The rapid growth of coffee industry is accompanied by a staggering increase in the amount of agriculture waste produced. In coffee producing countries, coffee wastes constitute a source of severe contamination and a serious environmental problem. In this study, the investigation on the possibility to utilize the coffee waste (CW) incorporated into the fired clay brick was carried out. The main purpose of this study is to determine the physical, mechanical properties and leach ability test of bricks incorporated with different percentages of CW. In this methodology, control brick (CB) and three different percentages of coffee waste brick (CWB) (1%, 3% and 5%) were manufactured and fired at 1050 °C. Physical and mechanical properties including shrinkage, density and compressive strength were reported and discussed. Additionally, leaching of heavy metals from manufactured clay brick was tested by using Toxicity Characteristics Leaching Procedure (TCLP). The results reported that with the incorporation of CW, the shrinkage increased linearly but still comply with minimum standard below 8% and good quality of brick was manufactured. Meanwhile, the results showed that density value decreased up to 30% from the normal brick with increased percentages of CW. The decreased compressive strength value of all the manufactured brick is still complies with minimum standard. On the other hand, heavy metals concentration leach out from different percentages of coffee waste brick is not exceeding the limit of 5 mg/L allowed by United States Environmental Protection Agency (USEPA). As a conclusion, the incorporation of CW into fired clay brick gives some advantages to the brick properties and also provides alternative solution on disposing the CW. In addition, the CW could also be a potential of low cost waste additive for the production of a brick