Using Neural Networks to Forecast the Implied Volatility: the Case of S&P100XEO

Currently the most popular method of estimating volatility is the implied volatility. It is calculated using the Black-Scholes option price formula, and is considered by traders to be a significant factor in signaling price movements in the underlying market. A trader is able to establish the proper strategic position in anticipation of changes in market trends if she/he could   accurately forecast future volatility. There is an abundance of ways to compute the volatility. For two decades neural networks has been developed to forecast future volatility, using past volatilities and other options market factors. In this article a network is created for this purpose whose performance demonstrates the value of neural networks as a predictive tool in volatility analysis

Identifikacija nelinearnog strukturnog ponašanja pomoću digitalne povratne neuronske mreže

Dynamical systems contain nonlinear relations which are difficult to model with conventional techniques. Hence, efficient nonlinear models are needed for system analysis, optimization, simulation and diagnosis of nonlinear systems. In recent years, computational-intelligence techniques such as neural networks, fuzzy logic and combined neuro-fuzzy systems algorithms have become very effective tools in the field of structural identification. The problem of the identification consists of choosing an identification model and adjusting the parameters in an way that the response of the model approximates the response of the real system to the same input. This paper investigates the identification of a nonlinear system by Digital Recurrent Neural Network (DRNN). A dynamic backpropagation algorithm is employed to adapt weights and biases of the DRNN. Mathematical model based on experimental data is developed. Results of simulations show that the application of the DRN for the identification of complex nonlinear structural behaviour gives satisfactory results.Dinamički sustavi sadrže nelinearne veze koje se teško modeliraju konvencionalnim tehnikama. Nelinearni modeli su neophodni za analizu sustava, optimizaciju, simulaciju i dijagnostiku nelinearnih sustava. Prethodnih godina, tehnike računalne inteligencije kao što su neuralne mreže, fuzzy logika i kombinirani neuro-fuzzy sustavi postaju efikasni alati u identifikaciji nelinearnih objekata. Problem identifikacije se sastoji od izbora identifikacijskog modela i prilagođavanja parametara tako da odziv modela aproksimira odziv realnog sustava za isti ulaz.Ovaj rad proučava identifikaciju nelinearnih sustava pomoću digitalne povratne neuronske mreže. Dinamički algoritam s propagacijom pogreške unazad se primjenjuje za adaptaciju težina i pragova osjetljivosti DRNN. Matematički model se razvija na bazi eksperimentalnih podataka. Rezultati simulacija pokazuju da primjena DRN u identifikaciji kompleksnog nelinearnog strukturnog ponašanja daje zadovoljavajuće rezultate

Tehnike računarske inteligencije u modeliranju i identifikaciji indikatora ponašanja brane

Indikatori ponašanja brane su relevantne veličine, čijim se praćenjem utvrđuje da li je stvarno stanje brane u eksploataciji u saglasnosti sa onim što je predviđeno i očekivano u fazi projektovanja. Veličine koje se prate treba da se kreću u nekom unapred definisanom opsegu koji garantuje stanje stabilnosti brane. U ovoj disertaciji su predloženi različiti pristupi modeliranja i parametarske identifikacije indikatora ponašanja brane, poput horizontalnih pomeranja i nivoa vode u pijezometrima, tehnikama računarske inteligencije. Prvi pristup je da se linearno preslikavanje uzročnih veličina u indikatore ponašanja, koje se koristi kod višestruke linearne regresije, zameni nelinearnim. Drugi pristup, predložen u ovom radu, zasniva se na primeni postupka parametarske identifikacije nelinearnih sistema. Horizontalna pomeranja i nivoi vode u pijezometrima su nelinearne, složene funkcije uzročnih veličina, pa je za njihovo modeliranje korišćena NARX (Nonlinear Auto Regresive eXogenous- nelinearni auto-regresioni model sa spoljašnjim ulazom) struktura, kojom je opisana široka klasa nelinearnih dinamičkih procesa. Predloženi pristupi formiranja modela primenjeni su za modeliranje i parametarsku identifikaciju horizontalnih pomeranja tačaka brane Bočac, kao i nivoa vode u pijezometrima brana Đerdap II i Prvonek. Nelinearni modeli zasnovani na tehnikama računarske inteligencije implementirani su korišćenjem programskog jezika Java i programskog paketa Matlab. Tehnike računarske inteligencije korišćene u ovom radu su višeslojni perceptron, RBF (RBF - Radial Basis Function – radijalna osnovna funkcija) neuronska mreža i ANFIS (ANFIS - Adaptive-Network-Based Fuzzy Inference System - fazi sistem za zaključivanje zasnovan na adaptivnoj mreži). Nedostajući podaci u skupu merenja mogu biti uzrok problema u okviru procesa učenja i loših performansi dobijenih modela. U cilju nadomeštanja nedostajućih podataka korišćene su tehnike iz domena matematičke statistike. Prisustvo autlajera u mernim podacima ima veliki uticaj na predviđanja podataka koji nedostaju, pa je njihovo prisustvo posebno analizirano. Takođe je analiziran i problem optimizacije ulazno-izlaznih modela, koji podrazumeva određivanje broja prediktora i dimenzije regresionog vektora, kao i broja parametara neuronskih mreža i neuro-fazi sistema. Performanse modela, formiranih na osnovu predloženog koncepta, poređeni su sa rezultatima dobijenim drugim metodama modeliranja istih indikatora ponašanja prikazanim u relevantoj literaturi objavljenoj u poslednjih nekoliko godina. Na osnovu rezultata zaključeno je da je moguće kreirati i obučiti modele zasnovane na tehnikama računarske inteligencije koji će sa velikom preciznošću predviđati bitne indikatore ponašanja brane.The dam behavior indicators are relevant factors whose monitoring indicates whether the actual operational state of the dam is in accordance with what is expected and anticipated in the design phase. Such indicators should move in a predefined range, in order to guarantee stability of the dam. This dissertation proposes different approaches to modeling and parametric identification of the dam behavior indicators, such as radial displacements or piezometric water levels, using the techniques of artificial intelligence. The first approach is to replace linear mapping of causal variables into behavior indicators, which is used in multiple linear regression, with nonlinear. The second approach proposed in this paper is based on applying the method of parametric nonlinear system identification. Radial displacements and piezometric water levels are nonlinear, complex functions of causal variables, so for their modeling NARX (Nonlinear Auto Regresive eXogenous), which is employed to describe a wide class of nonlinear dynamic systems, is used. These proposed approaches are used for modeling and parametric identification of radial displacements of dam Bočac, and piezometric water levels of dams Iron Gate II and Prvonek. Nonlinear models based on artificial intelligence techniques have been implemented using the Java programming language and MATLAB. Artificial intelligence techniques used in this work are the multilayer perceptron, RBF (Radial Basis Function) neural network and ANFIS (Adaptive-Network-Based Fuzzy Inference System). The presence of missing data in a set of measurements may be causing problems in the learning process and the poor performance of the obtained models. In order to predict the missing data, the techniques of mathematical statistics have been used. Outliers present in a set of measurements have a big effect on the prediction of missing data, and their presence is specifically analyzed. The problem of optimizing the inputoutput model, which involves determining the number of predictors and dimensions of the regression vector, and the number of parameters of neural networks and neuro-fuzzy systems, is also analyzed. The performance of the models, formed on the basis of the proposed concept, are compared with those obtained by other methods of modeling the same behavioral indicators presented in relevant accompanying literature published in the last few years. Based on the results, it was concluded that it is possible to create and train models based on computational intelligence techniques to predict with great accuracy the essential dam behavior indicators

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology