Matches in SemOpenAlex for { <https://semopenalex.org/work/W2981772821> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W2981772821 abstract "This work explores a deep-learning approach to the problem of Parametric Option Pricing. In a first phase, neural networks are used to learn a pricing function starting from a set of prices computed by means of a suitable benchmark method. In a second phase, the method is coupled with a complexity reduction technique in order for it to be scaled up to higher dimensions. The contributions of this work are multiple. On the one hand, it shows the applicability of the neural-network approach to the parametric pricing problem. While few recent works have tackled a similar problem, this thesis shows that solutions can be found to more complex financial products, such as American and basket options. The pricer resulting from this method is fast and accurate, thus comparing favourably against the traditional Monte Carlo or PDE approaches. In addition, it provides results which are comparable to those obtained by recent developments in the use of Chebychev polynomial approximations, but without the constraints imposed by the need of having a fixed grid of Chebychev points. On the other hand, this work shows how to make the neural-net approach scalable to higher-dimensional problems. Indeed, the high number of parameters which can enter the pricing function (model and option parameters and underlying assets) can lead to problems which are not tractable by standard machines due to memory constraints. This thesis proposes to exploit a tensor-train (TT) decomposition which significantly compresses the tensor of prices used to train the neural network. As well as ensuring an accurate representation of the tensor entries, the TT-decomposition allows to retrieve the entries by means of simple products of three-dimensional ten- sors. For this reason, one does not need to store the whole training tensor, but can easily compute only the few entries of the small batch of samples which are needed for stochastic gradient-based methods used in the training of the neural net. The proposed methodology is tested on two practical cases: basket of call options, with up to 25 underlying assets, and American put options." @default.
- W2981772821 created "2019-11-01" @default.
- W2981772821 creator A5042179767 @default.
- W2981772821 date "2019-07-15" @default.
- W2981772821 modified "2023-09-27" @default.
- W2981772821 title "A Machine-learning Approach to Parametric Option Pricing" @default.
- W2981772821 hasPublicationYear "2019" @default.
- W2981772821 type Work @default.
- W2981772821 sameAs 2981772821 @default.
- W2981772821 citedByCount "0" @default.
- W2981772821 crossrefType "journal-article" @default.
- W2981772821 hasAuthorship W2981772821A5042179767 @default.
- W2981772821 hasConcept C101710237 @default.
- W2981772821 hasConcept C105795698 @default.
- W2981772821 hasConcept C117251300 @default.
- W2981772821 hasConcept C126255220 @default.
- W2981772821 hasConcept C13280743 @default.
- W2981772821 hasConcept C14036430 @default.
- W2981772821 hasConcept C149782125 @default.
- W2981772821 hasConcept C154945302 @default.
- W2981772821 hasConcept C177264268 @default.
- W2981772821 hasConcept C185798385 @default.
- W2981772821 hasConcept C194483076 @default.
- W2981772821 hasConcept C19499675 @default.
- W2981772821 hasConcept C199360897 @default.
- W2981772821 hasConcept C205649164 @default.
- W2981772821 hasConcept C33923547 @default.
- W2981772821 hasConcept C41008148 @default.
- W2981772821 hasConcept C48044578 @default.
- W2981772821 hasConcept C50644808 @default.
- W2981772821 hasConcept C77088390 @default.
- W2981772821 hasConcept C78458016 @default.
- W2981772821 hasConcept C86803240 @default.
- W2981772821 hasConceptScore W2981772821C101710237 @default.
- W2981772821 hasConceptScore W2981772821C105795698 @default.
- W2981772821 hasConceptScore W2981772821C117251300 @default.
- W2981772821 hasConceptScore W2981772821C126255220 @default.
- W2981772821 hasConceptScore W2981772821C13280743 @default.
- W2981772821 hasConceptScore W2981772821C14036430 @default.
- W2981772821 hasConceptScore W2981772821C149782125 @default.
- W2981772821 hasConceptScore W2981772821C154945302 @default.
- W2981772821 hasConceptScore W2981772821C177264268 @default.
- W2981772821 hasConceptScore W2981772821C185798385 @default.
- W2981772821 hasConceptScore W2981772821C194483076 @default.
- W2981772821 hasConceptScore W2981772821C19499675 @default.
- W2981772821 hasConceptScore W2981772821C199360897 @default.
- W2981772821 hasConceptScore W2981772821C205649164 @default.
- W2981772821 hasConceptScore W2981772821C33923547 @default.
- W2981772821 hasConceptScore W2981772821C41008148 @default.
- W2981772821 hasConceptScore W2981772821C48044578 @default.
- W2981772821 hasConceptScore W2981772821C50644808 @default.
- W2981772821 hasConceptScore W2981772821C77088390 @default.
- W2981772821 hasConceptScore W2981772821C78458016 @default.
- W2981772821 hasConceptScore W2981772821C86803240 @default.
- W2981772821 hasLocation W29817728211 @default.
- W2981772821 hasOpenAccess W2981772821 @default.
- W2981772821 hasPrimaryLocation W29817728211 @default.
- W2981772821 hasRelatedWork W1603395859 @default.
- W2981772821 hasRelatedWork W17783998 @default.
- W2981772821 hasRelatedWork W1970402960 @default.
- W2981772821 hasRelatedWork W2336789666 @default.
- W2981772821 hasRelatedWork W2352213094 @default.
- W2981772821 hasRelatedWork W2396224275 @default.
- W2981772821 hasRelatedWork W2911009199 @default.
- W2981772821 hasRelatedWork W2923279184 @default.
- W2981772821 hasRelatedWork W3014330595 @default.
- W2981772821 hasRelatedWork W3030384845 @default.
- W2981772821 hasRelatedWork W3122214919 @default.
- W2981772821 hasRelatedWork W3123306925 @default.
- W2981772821 hasRelatedWork W3123821994 @default.
- W2981772821 hasRelatedWork W3123838787 @default.
- W2981772821 hasRelatedWork W3125795862 @default.
- W2981772821 hasRelatedWork W3137670243 @default.
- W2981772821 hasRelatedWork W3139168489 @default.
- W2981772821 hasRelatedWork W3148514650 @default.
- W2981772821 hasRelatedWork W3161391800 @default.
- W2981772821 hasRelatedWork W3164013265 @default.
- W2981772821 isParatext "false" @default.
- W2981772821 isRetracted "false" @default.
- W2981772821 magId "2981772821" @default.
- W2981772821 workType "article" @default.