Formulation Of A Rational Option Pricing Model using Artificial Neural Networks

2020 ◽  
Author(s):  
Kaustubh Yadav
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Option Pricing ◽  
Pricing Model ◽  
Option Pricing Model ◽  
Artificial Neural

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 Estimation of Residential Property Market Price: Comparison of Artificial Neural Networks and Hedonic Pricing Model

2020 ◽  
Vol 4 (6) ◽  
pp. 530-538
Author(s):  
Michaela Štubňová ◽  
Marta Urbaníková ◽  
Jarmila Hudáková ◽  
Viera Papcunová
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Estate ◽  
Learning Algorithm ◽  
Market Price ◽  
Residential Property ◽  
Pricing Model ◽  
Property Market ◽  
Price Estimation ◽  
Artificial Neural

The correct real estate property price estimation is significant not only in the real estate market but also in the banking sector for collateral loans and the insurance sector for property insurance. The paper focuses on both traditional and advanced methods for real estate property valuation. Attention is paid to the analysis of the accuracy of valuation models. From traditional methods, a regression model is used for residential property price estimation, which represents the hedonic approach. Modern advanced valuation methods are represented by the artificial neural network, which is one of the soft computing techniques. The results of both methods in residential property market price estimation are compared. The analysis is performed using data on residential properties sold on the real estate market in the city of Nitra in the Slovak Republic. To estimate the residential property prices, artificial neural networks trained with the Levenberg-Marquart learning algorithm, the Bayesian Regularization learning algorithm, and the Scaled Conjugate Gradient learning algorithm, and the regression pricing model are used. Among the constructed neural networks, the best results are achieved with networks trained with the Regularization learning algorithm with two hidden layers. Its performance is compared with the performance of the regression pricing model, and it can state that artificial neural networks can considerably improve prediction accuracy in the estimation of residential property market price. Doi: 10.28991/esj-2020-01250 Full Text: PDF

scholarly journals Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach

2021 ◽  
pp. 29-55
Author(s):  
Roberto Louis Forestal ◽  
Shih-Ming Pi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Food System ◽  
Commodity Price ◽  
Optimal Scaling ◽  
Economic Approach ◽  
Pricing Model ◽  
Input Output ◽  
Ecological Economic ◽  
Artificial Neural

This research paper employs input-output pricing model based on ecological-economic approach to investigate the impacts of internal factors as well as external forces on agriculture commodities. To empirically test our model, we select two different methodologies such as the optimal scaling regression with nonlinear transformations and feedforward artificial neural networks. Our sample includes data related to price of agriculture and energy commodities (cocoa, coffee and crude oil), production of crops and livestock, emissions of greenhouse gases (GHG) from agriculture from 1961 to 2019. Results find a bidirectional relationship between cocoa price and coffee price explaining by the fact that commodity-dependent countries often use kindred production landscapes and similar supply chain management when dealing with coffee and cocoa. Therefore, effect of supply side shocks may be transmitted from one market to another. We also present evidence that greenhouse gas emissions have strong effect on commodity price, thus we encourage an integrated approach including both concrete technological and proactive managerial measures in order to mitigate global warming impacts on the food system. We believe that these findings will be of interest to commodity producers, asset managers and academics who look a better understanding of the dynamics of commodity markets. JEL classification numbers: C50, Q02, Q57. Keywords: Agriculture commodity, Input-output pricing model, Ecological-economic approach, Artificial neural networks, Optimal scaling regression.

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