DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods

2021 ◽  
Vol 70 ◽  
pp. 43-59
Author(s):  
Ji Hyun Jang ◽  
Jisang Yoon ◽  
Jungeun Kim ◽  
Jinmo Gu ◽  
Ha Young Kim
Keyword(s):  
Deep Learning ◽  
Option Pricing ◽  
Parametric Methods ◽  
Pricing Framework

scholarly journals Bayesian Option Pricing Framework with Stochastic Volatility for FX Data

Risks ◽  
2016 ◽  
Vol 4 (4) ◽  
pp. 51 ◽  
Author(s):  
Ying Wang ◽  
Sai Choy ◽  
Hoi Wong
Keyword(s):  
Option Pricing ◽  
Stochastic Volatility ◽  
Pricing Framework

scholarly journals Risk-Neutrality of RND and Option Pricing within an Entropy Framework

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 836 ◽  
Author(s):  
Xisheng Yu
Keyword(s):  
Option Pricing ◽  
Risk Neutral ◽  
Unique Distribution ◽  
Pricing Framework

This article constructs an entropy pricing framework by incorporating a set of informative risk-neutral moments (RNMs) extracted from the market-available options as constraints. Within the RNM-constrained entropic framework, a unique distribution close enough to the correct one is obtained, and its risk-neutrality is deeply verified based on simulations. Using this resultant risk-neutral distribution (RND), a sample of risk-neutral paths of the underlying price is generated and ultimately the European option’s prices are computed. The pricing performance and analysis in simulations demonstrate that this proposed valuation is comparable to the benchmarks and can produce fairly accurate prices for options.

scholarly journals Smile from the past: A general option pricing framework with multiple volatility and leverage components

2015 ◽  
Vol 187 (2) ◽  
pp. 521-531 ◽  
Author(s):  
Adam A. Majewski ◽  
Giacomo Bormetti ◽  
Fulvio Corsi
Keyword(s):  
Option Pricing ◽  
The Past ◽  
Pricing Framework

An option pricing framework for valuation of football players

2005 ◽  
Vol 14 (3-4) ◽  
pp. 281-295 ◽  
Author(s):  
Radu Tunaru ◽  
Ephraim Clark ◽  
Howard Viney
Keyword(s):  
Option Pricing ◽  
Football Players ◽  
Pricing Framework

scholarly journals Detection and Labeling of Vertebrae using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 2788-2791
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Long Range ◽  
Short Range ◽  
Ct Images ◽  
Field Of View ◽  
Ct Scans ◽  
Parametric Methods ◽  
Conceptual Information ◽  
Image Patches

Inspection, Classification and localization of artificial vertebrae from random CT images is difficult. Normally vertebrates have a similar morphological appearance. Owing to anatomy and hence the subjective field of view of CT scans, the presence of any anchor vertebrae or parametric methods for defining the looks and form can hardly be believed. They suggest a robust and effective method for recognizing and localizing vertebrae that can automatically learn to use both the short range and long-range conceptual information in a controlled manner. Combine a fully convolutionary neural network with an instance memory that preserves information on already segmented vertebrae. This network analyzes image patches iteratively, using the instance memory to scan for and segment the not yet segmented primary vertebra. Every vertebra is measured as wholly or partly at an equal period. This study uses an over dimensional sample of 865 disc-levels from 1115 patients.

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