Option pricing and trading with artificial neural networks and advanced parametric models with implied parameters

2005 ◽  
Author(s):  
A.C. Panayiotis ◽  
M.H. Spiros ◽  
C. Chris
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Option Pricing ◽  
Parametric Models ◽  
Artificial Neural

System identification of a robot arm with extended Kalman filter and artificial neural networks

2019 ◽  
Vol 13 (2) ◽  
pp. 135-150 ◽  
Author(s):  
Sabine Horvath ◽  
Hans Neuner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Industrial Robot ◽  
Parametric Models ◽  
Limiting Factor ◽  
General Description ◽  
Robot Arm ◽  
Artificial Neural

Abstract The development of an algorithm to describe a dynamic system and to predict its future behaviour in further consequence is the aim of the present study. Non parametric models provide a general description of object dynamics and artificial neural networks (ANN), which are a very flexible and universal learning method, belong to it. However, the standard estimation procedures for ANN like Levenberg-Marquardt (LM) do not consider that data is observed and consequently is uncertain. The combination with the extended Kalman filter (EKF) enables the consideration of the uncertainty in the estimation process. The analogies between EKF and LM are discussed and thereon the advantages of the EKF are outlined. The integration of ANN into EKF will be evaluated on an industrial robot arm. At first, a simplified model is determined; the ANN describes the robot position deviations as a function of the joint encoder values. The robot reference positions are measured by a laser tracker. In order to compare it with the robot outputs, the observations need to be transformed to the robot frame and the offset between the end-effector and the robot flange has to be determined. A method to estimate both parameters simultaneously is developed and the results are verified on basis of simulated data. This paper comprises two novel approaches. First, uncertainty is considered in the ANN estimation on basis of the combination with the EKF. Considering the full covariance matrix of the robot deviations leads to a better prediction of the robot’s behaviour. Second, an integrated transformation and lever arm determination is introduced and the robot’s repeatability presents the limiting factor of the achievable parameter uncertainty.

scholarly journals Formulation Of A Rational Option Pricing Model using Artificial Neural Networks

2020 ◽  
Author(s):  
Kaustubh yadav ◽  
Anubhuti yadav
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Option Pricing ◽  
Numerical Solutions ◽  
Options Pricing ◽  
Volatility Model ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Efficient Alternative ◽  
Artificial Neural

This paper inquires on the options pricing modeling using Artificial Neural Networks to price Apple(AAPL) European Call Options. Our model is based on the premise that Artificial Neural Networks can be used as functional approximators and can be used as an alternative to the numerical methods to some extent, for a faster and an efficient solution. This paper provides a neural network solution for two financial models, the BlackScholes-Merton model, and the calibrated-Heston Stochastic Volatility Model, we evaluate our predictions using the existing numerical solutions for the same, the analytic solution for the Black-Scholes equation, COS-Model for Heston’s Stochastic Volatility Model and Standard Heston-Quasi analytic formula. The aim of this study is to find a viable time-efficient alternative to existing quantitative models for option pricing.

scholarly journals Użyteczność modeli parametrycznych i sztucznych sieci neuronowych w prognozowaniu kosztów produkcji

2017 ◽  
pp. 87-112
Author(s):  
Zbigniew Leszczyński ◽  
Tomasz Jasiński
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Parametric Estimation ◽  
Parametric Models ◽  
Production Costs ◽  
Production Environment ◽  
Production Lines ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Nonparametric Estimates

Użyteczność modeli parametrycznych i sztucznych sieci neuronowych w prognozowaniu kosztów produkcji The aim of the paper is to analyze parametric models and artificial neural networks in terms of their suitability as estimation tools of the production costs. Estimated production costs are a fundamental determinant of the decision-making process by costs engineers relating to design and management costs of new products, infrastructure projects and production lines. The first part of the paper presents a con- ceptual framework for the construction of a model of production costs parametric estimation, multidimensional with linear and nonlinear dependency. It then discusses the nature and use of artificial neural networks as nonparametric estimates of production costs. In both parts of the article, an empirical study is conducted with the use of adequate statistical methods and artificial neurons. This study presents proce- dures for construction of models of parametric and nonparametric estimation of production costs and discusses their advantages and disadvantages. It also presents the application and usefulness of both models for estimating production costs in production environment.

scholarly journals COMPARATIVE ANALYSIS OF METHODS FOR SIMULATING THE WELL OPERATION WITH ELECTRIC SUBMERSIBLE PUMP INSTALLATIONS

2019 ◽  
Vol 16 (32) ◽  
pp. 621-632
Author(s):  
Vladimir V. BUKHTOYAROV ◽  
Vadim S. TYNCHENKO ◽  
Eduard A. PETROVSKIY ◽  
Fedor A. Buryukin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hybrid Approach ◽  
Parametric Models ◽  
Performance Parameters ◽  
Submersible Pump ◽  
Combined Models ◽  
Artificial Neural ◽  
Electric Submersible Pump ◽  
Non Parametric

This article presents the research results of parametric and non-parametric identification methods of the technological models of well operation using electric submersible pump installations. The use of a hybrid approach is proposed, combining parametric and non-parametric models to obtain accurate models that allow the prediction of well performance parameters. Studies of simulation methods under conditions of interference effect of different level, which are typical for signaling channels of real data management, control systems, and measuring instruments, have been conducted. The combined models proposed have been constructed with the help of the Rosenblatt–Parzen non-parametric regression, parametric models with automatic adaptation of parameters and artificial neural networks. Such combined models have been shown to possess essential generalizing possibilities, allowing for smoothing of parametrical data and the restoration of initial dependences with a significantly smaller error in relation to the disturbing interference. The developed methods and models were implemented for research purposes in the software system, which allows a complex simulation of changes in parameters during well operation using the electric submersible pump installations. To evaluate the results’ statistical significance, methods of statistical processing have been applied using ANOVA. The results demonstrate that for an effective solution to the problem of the process simulation of well operation and to ensure high adaptability of the models, the combined approach is the most effective method. Models on the basis of artificial neural networks after adjustment allow us to improve the efficiency of the solution to the prediction problem and at the same time have necessary flexibility for adaptation of the computational structure under the conditions of changing performance parameters. The parametric block of models allows us to use a priori information about dependences of performance parameters and to identify reasonably accurate the drift of parameters under the conditions of instability of the process under study.

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