Paper Info
Title
Data-Driven Neuro ARCH (DDNA) volatility model for Option Pricing on Cloud Resources
Abstract
Due to highly unpredictable nature of financial derivatives market such as options, profiting from these financial products has always been a challenge for investors. One of the sources of challenge is posed by accurate computation of volatility of the underlying assets (such as stocks) of an option. For this research we use the volatility forecast from Data-Driven Neuro ARCH (DDNA) volatility model [1] along with the Monte Carlo (MC) simulations to compute option prices. Since the MC method requires a large number of simulations for better precision, we implement the proposed model on two easily accessible cloud resources (Amazon's elastic map reduce (EMR) and Google's Cloud DataProc (GDP)) using the Hadoop MapReduce paradigm.We show that our model outperforms the existing option pricing models in terms of efficiency and accuracy.This proposed strategy could be used by investors for computing option prices precisely with relative ease, allowing them to value the numerous available option contracts for their investment decisions.
Year
DOI
Venue
2020
10.1109/SSCI47803.2020.9308132
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Keywords
DocType
ISBN
Data-Driven Application,Neuro ARCH model,Financial Option Pricing,MapReduce Approach,Amazon EMR,Google Dataproc
Conference
978-1-7281-2548-0
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Manmohit Singh100.34
Ruppa K. Thulasiram265257.27
A. Thavaneswaran313021.94