Compare Of Linear And Neural Networks Models For Estimating And Forecasting Black-Scholes Option Pricing Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F13%3A43870718" target="_blank" >RIV/70883521:28120/13:43870718 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.ufu.utb.cz/sbornik/proceedings2013.pdf" target="_blank" >http://www.ufu.utb.cz/sbornik/proceedings2013.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Compare Of Linear And Neural Networks Models For Estimating And Forecasting Black-Scholes Option Pricing Model

Popis výsledku v původním jazyce

The Black-Scholes formula is a well-known model for pricing and hedging derivative securities. Interesting hypothetical questions that can be raised are: If option pricing model had not been developed, could a technique like neural networks have learnt the nonlinear form of the Black-Scholes type model to yield the fair value of an option? Could the networks have learnt to produce efficient implied volatility estimates? The results in this article from a simplified neural networks approach are rather encouraging, but more for volatility outputs than for call prices. This article will evaluate the performance of alternative neural network models relative to the standard linear model for forecasting relatively complex artificially generated time series.The article shows that relatively simple feedforward neural nets outperform the linear models in some cases, or do not do worse than the linear models.

Název v anglickém jazyce

Compare Of Linear And Neural Networks Models For Estimating And Forecasting Black-Scholes Option Pricing Model

Popis výsledku anglicky

The Black-Scholes formula is a well-known model for pricing and hedging derivative securities. Interesting hypothetical questions that can be raised are: If option pricing model had not been developed, could a technique like neural networks have learnt the nonlinear form of the Black-Scholes type model to yield the fair value of an option? Could the networks have learnt to produce efficient implied volatility estimates? The results in this article from a simplified neural networks approach are rather encouraging, but more for volatility outputs than for call prices. This article will evaluate the performance of alternative neural network models relative to the standard linear model for forecasting relatively complex artificially generated time series.The article shows that relatively simple feedforward neural nets outperform the linear models in some cases, or do not do worse than the linear models.

Druh

D - Stať ve sborníku

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

—

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 6th International Scientific Conference Finance and the performance of firms in science, education, and practice

ISBN

978-80-7454-246-6

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

72-84

Název nakladatele

Univerzita Tomáše Bati ve Zlíně, Fakulta managementu a ekonomiky

Místo vydání

Zlín

Místo konání akce

Zlín

Datum konání akce

25. 4. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000329435800006