Results 21 to 30 of about 1,033 (104)
A note on the option price and ‘Mass at zero in the uncorrelated SABR model and implied volatility asymptotics’ [PDF]
Quantitative Finance, 2020 Gulisashvili et al. [Quant. Finance, 2018, 18(10), 1753-1765] provide a small-time asymptotics for the mass at zero under the uncorrelated stochastic-alpha-beta-rho (SABR) model by approximating the integrated variance with a moment-matched lognormal distribution.
Jaehyuk Choi, Lixin Wu
openaire +3 more sources
A Non-Gaussian Option Pricing Model with Skew [PDF]
, 2004 Closed form option pricing formulae explaining skew and smile are obtained within a parsimonious non-Gaussian framework. We extend the non-Gaussian option pricing model of L.
Borland, L., Bouchaud, J. P.
core +3 more sources
Calibrating and completing the volatility cube in the SABR Model [PDF]
, 2011 This report describes the calibration and completion of the volatility cube in the SABR model. The description is based on a project done for Assenagon GmbH in Munich. However, we use fictitious market data which resembles realistic market data. The problem posed by our client is formulated in section 1.
Dimitroff, G., de Kock, J.
openaire +2 more sources
A Stochastic Volatility LIBOR Market Model with a Closed Form Solution [PDF]
, 2008 Since its initial publication the SABR model has gained widespread use across asset classes and it has now become the standard pricing framework used in the market to quote interest rate products sensitive to the non flat strike-structure of the market
Nada, Hazim, Nada, Hazim
core +1 more source
Volatility Swap Under the SABR Model [PDF]
, 2013 The SABR model is shortly presented and the volatility swap explained. The fair value for a volatility swap is then computed using the usual theory in financial mathematics. An analytical solution using confluent hypergeometric functions is found. The solution is then verified using Rama Cont's functional calculus.
openaire +2 more sources
The Equivalent Constant-Elasticity-of-Variance (CEV) Volatility of the Stochastic-Alpha-Beta-Rho (SABR) Model [PDF]
SSRN Electronic Journal, 2019 This study presents new analytic approximations of the stochastic-alpha-beta-rho (SABR) model. Unlike existing studies that focus on the equivalent Black-Scholes (BS) volatility, we instead derive the equivalent constant-elasticity-of-variance (CEV) volatility.
Jaehyuk Choi, Lixin Wu
openaire +4 more sources
Target volatility option pricing in lognormal fractional SABR model
, 2018 We examine in this article the pricing of target volatility options in the lognormal fractional SABR model. A decomposition formula by Ito's calculus yields a theoretical replicating strategy for the target volatility option, assuming the accessibilities of all variance swaps and swaptions.
Alos, Elisa +3 more
openaire +2 more sources
Polar Coordinates for the 3/2 Stochastic Volatility Model
Mathematical Finance, Volume 35, Issue 3, Page 708-723, July 2025.ABSTRACT The 3/2 stochastic volatility model is a continuous positive process s with a correlated infinitesimal variance process ν$\nu $. The exact definition is provided in the Introduction immediately below. By inspecting the geometry associated with this model, we discover an explicit smooth map ψ$ \psi $ from (R+)2$({\mathbb{R}}^+)^2 $ to the ...
Paul Nekoranik
wiley +1 more source
Rough PDEs for Local Stochastic Volatility Models
Mathematical Finance, Volume 35, Issue 3, Page 661-681, July 2025.ABSTRACT In this work, we introduce a novel pricing methodology in general, possibly non‐Markovian local stochastic volatility (LSV) models. We observe that by conditioning the LSV dynamics on the Brownian motion that drives the volatility, one obtains a time‐inhomogeneous Markov process. Using tools from rough path theory, we describe how to precisely
Peter Bank +3 more
wiley +1 more source
Fast Quantization of Stochastic Volatility Models
, 2017 Recursive Marginal Quantization (RMQ) allows fast approximation of solutions to stochastic differential equations in one-dimension. When applied to two factor models, RMQ is inefficient due to the fact that the optimization problem is usually performed ...
Kienitz, Joerg +3 more
core +1 more source