Results 11 to 20 of about 2,199,003 (334)

Machine Learning to Compute Implied Volatility from European/American Options Considering Dividend Yield

open access: yesProceedings, 2020
Computing implied volatility from observed option prices is a frequent and challenging task in finance, even more in the presence of dividends. In this work, we employ a data-driven machine learning approach to determine the Black–Scholes implied ...
Shuaiqiang Liu   +3 more
doaj   +1 more source

American options in an imperfect complete market with default

open access: yesESAIM: Proceedings and Surveys, 2018
We study pricing and hedging for American options in an imperfect market model with default, where the imperfections are taken into account via the nonlinearity of the wealth dynamics. The payoff is given by an RCLL adapted process (ξt).
Dumitrescu Roxana   +2 more
doaj   +1 more source

Option Pricing, Zero Lower Bound, and COVID-19

open access: yesRisks, 2021
This paper provides a quantitative assessment of equity options priced at the Zero Lower Bound, i.e., when interest rates are set essentially to zero. We obtain closed form formulas for American options when the Zero Lower Bound policy holds.
Giacomo Morelli, Lea Petrella
doaj   +1 more source

Pricing European and American Options under Heston Model using Discontinuous Galerkin Finite Elements

open access: yes, 2020
This paper deals with pricing of European and American options, when the underlying asset price follows Heston model, via the interior penalty discontinuous Galerkin finite element method (dGFEM).
Karasözen, Bülent   +2 more
core   +1 more source

Supervised Machine Learning with Control Variates for American Option Pricing

open access: yesFoundations 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   +3 more
doaj   +1 more source

Neural Network Pricing of American Put Options

open access: yesRisks, 2020
In this study, we use Neural Networks (NNs) to price American put options. We propose two NN models—a simple one and a more complex one—and we discuss the performance of two NN models with the Least-Squares Monte Carlo (LSM) method.
Raquel M. Gaspar   +2 more
doaj   +1 more source

Projection and Contraction Method for Pricing American Bond Options

open access: yesMathematics, 2023
In this paper, an effective numerical method is proposed for a linear complementarity problem (LCP) arising in the valuation of American bond options under the Cox–Ingersoll–Ross (CIR) model.
Qi Zhang   +4 more
doaj   +1 more source

New Methods with Capped Options for Pricing American Options

open access: yesJournal of Applied Mathematics, 2014
We propose two new methods: improved binomial methods and improved least square MonteCarlo methods (LSM), for pricing American options. These two methods are developed using the nice capped options which have closed-form formulas.
Dongya Deng, Cuiye Peng
doaj   +1 more source

Pricing the exotic: Path-dependent American options with stochastic barriers

open access: yesLatin American Journal of Central Banking, 2021
We develop a novel pricing strategy that approximates the value of an American option with exotic features through a portfolio of European options with different maturities. Among our findings, we show that: (i) our model is numerically robust in pricing
Alejandro Rojas-Bernal   +1 more
doaj   +1 more source

American put options with regime-switching volatility [PDF]

open access: yesSeonmul yeongu
We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the ...
Bong-Gyu Jang, Hyeng Keun Koo
doaj   +1 more source

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