Results 101 to 110 of about 4,224 (208)

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

, 2015
Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled ...
Warne, David, Lindsay, Kenneth
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Preference-free option pricing with path-dependent volatility: A closed-form approach [PDF]

This paper shows how one can obtain a continuous-time preference-free option pricing model with a path-dependent volatility as the limit of a discrete-time GARCH model.
Steven L. Heston, Saikat Nandi
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The Heston Stochastic-Local Volatility Model: Efficient Monte Carlo Simulation [PDF]

, 2014
In this article we propose an efficient Monte Carlo scheme for simulating the stochastic volatility model of Heston (1993) enhanced by a non-parametric local volatility component. This hybrid model combines the main advantages of the Heston model and the
Oosterlee, C.W. (Kees), Stoep, Anton, Grzelak, Lech Aleksander, Stoep, A.W. (Anton) van der, Oosterlee, Kees, Grzelak, L.A. (Lech Aleksander)   +5 more
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Hybrid machine learning and stochastic volatility models with blockchain data for high-frequency cryptocurrency trading

Discover Analytics
High-frequency cryptocurrency markets, particularly for Bitcoin and Ethereum, are characterized by extreme volatility with daily price fluctuations often surpassing 10%. Traditional stochastic volatility models, such as the Heston model, prove inadequate
Timothy King Avordeh, Christopher Quaidoo, Samuel Arthur   +2 more
doaj   +1 more source

Interpretability in deep learning for finance: A case study for the Heston model

Risk Sciences
Deep learning is a powerful tool whose applications in quantitative finance are growing every day. Yet, artificial neural networks behave as black boxes, and this introduces risks, hindering validation and accountability processes.
Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Sáez de Ocáriz Borde   +3 more
doaj   +1 more source

Supervised Machine Learning with Control Variates for American Option Pricing

Foundations of Computing and Decision Sciences, 2018
In this paper, we make use of a Bayesian (supervised learning) approach in pricing American options via Monte Carlo simulations. We first present Gaussian process regression (Kriging) approach for American options pricing and compare its performance in ...
Mu Gang, Godina Teodor, Maffia Antonio, Sun Yong Chao   +3 more
doaj   +1 more source

Option valuation using Heston model [PDF]

, 2016
학위논문 (석사)-- 서울대학교 대학원 : 산업공학과, 2016. 2. 장우진.Heston 모형은 변동성이 평균회귀 확률과정을 따르면서 기초자산의 가격변동과 상관관계가 있는 확률변동성 모형으로 기하학적 브라운 운동 모형보다 실제 시장 수익률 확률분포함수의 꼬리부분이 로그정규분포보다 더 천천히 감소하는 두꺼운 꼬리 효과를 가진다.
김선도
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On an improved computational solution for the 3D HCIR PDE in finance

Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2019
The aim of this work is to tackle the three–dimensional (3D) Heston– Cox–Ingersoll–Ross (HCIR) time–dependent partial differential equation (PDE) computationally by employing a non–uniform discretization and gathering the finite difference (FD) weighting
Soleymani Fazlollah, Akgül Ali, Akgül Esra Karatas   +2 more
doaj   +1 more source

Time-dependent Heston model [PDF]

, 2014
This work presents an exact solution to the generalized Heston model, where the model parameters are assumed to have linear time dependence The solution for the model in expressed in terms of confluent hypergeometric functions.
openaire   +2 more sources

Malliavin differentiability of the Heston volatility and applications to option pricing

We prove that the Heston volatility is Malliavin differentiable under the classical Novikov condition and give an explicit expression for the derivative.
Alos, Elisa, Ewald, Christian-Oliver
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