prikaz prve stranice dokumenta Strojno učenje za vrednovanje slučajnih zahtjeva
No public access
master's thesis
Strojno učenje za vrednovanje slučajnih zahtjeva
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:916331
Abramović, Hrvoje
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Abramović, H. (2022). Strojno učenje za vrednovanje slučajnih zahtjeva (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:916331

Abramović, Hrvoje. "Strojno učenje za vrednovanje slučajnih zahtjeva." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:916331

Abramović, Hrvoje. "Strojno učenje za vrednovanje slučajnih zahtjeva." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:916331

Abramović, H. (2022). 'Strojno učenje za vrednovanje slučajnih zahtjeva', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 19 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:916331

Abramović H. Strojno učenje za vrednovanje slučajnih zahtjeva [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 December 19] Available at: https://urn.nsk.hr/urn:nbn:hr:168:916331

H. Abramović, "Strojno učenje za vrednovanje slučajnih zahtjeva", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:916331

Metadata
TitleStrojno učenje za vrednovanje slučajnih zahtjeva
Title (english)Machine Learning for Contingent Claims Pricing
AuthorHrvoje Abramović
MentorZvonko Kostanjčar (mentor)
Committee memberZvonko Kostanjčar (predsjednik povjerenstva)
Committee memberDražen Jurišić (član povjerenstva)
Committee memberStjepan Begušić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Diplomski rad se bavi primjenom metoda strojnog učenja, točnije LSTM-ova, u svrhu vrednovanja opcija. Ideja je predvidjeti buduću volatilnost i onda provjeriti koje su opcije na tržištu krivo procijenjene gledajući njihovu impliciranu volatilnosti. Model je treniran nad povjesnim podacima S\&P 500 indeksa, te je uspio nadmašiti jednostavan heuristički pristup. Nadalje, model pokazuje najbolje rezultate kada mu se na ulaz, uz povijesnu volatilnost, dovedu i povrati. Pokazalo se i da model radi bolje predikcije nad dužim vremenskim okvirima, što je vjerojatno uzrokovano svojstvom volatilnosti da se dugoročno uvijek vraća na prosječnu vrijednost. Radom su obuhvaćeni i uvodi u opcije, vrednovanje opcija, volatilnost, strategije za trgovanje volatilnošću, te pregled raznih metoda strojnog učenja za vrednovanje opcija. Na kraju je predstavljen i kompletan model za trgovanje volatilnošću pomoću predikcija LSTM modela.
Abstract (english)
This thesis explores the use of machine learning models, namely long-short term memory networks, in the context of option pricing. This is done by trying to forecast future realized volatility, and then determine which options in the market are overpriced or underpriced in terms of their implied volatility. An LSTM model was trained using the historical S\&P 500 data and managed to outperform a simple benchmark model based on historical volatility. It is also shown that the model performs best when returns are passed to the network alongside historical volatility. Furthermore, it is shown that the model performs better with longer forecasting horizon, probably due to the mean reverting property of volatility. Throughout the thesis, introductions to options, option pricing, volatility properties, and volatility trading strategies are presented, as well as an overview of different machine learning approaches used to price options. In the end, a volatility trading pipeline which employs the LSTM model’s prediction is presented, with a brief guideline for further development.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:916331
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
Public note
Created on2023-01-14 08:17:00