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Showing 1 to 20 of 64 for “"volatility models"”.
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Rough volatility models
So-called rough stochastic volatility models constitute the latest advancement in option price modeling. In contrast to popular bivariate diffusion models such as Heston, here the driving noise of volatility is modeled by a fractional Brownian motion (fBM) with scaling in the rough regime of Hurst …
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Extending volatility models with market sentiment indicators
… forecast and compiled a ranking of the extended models based on their relative performance. We have identified three relevant variables: daily negative tweets, daily Google search volume and weekly Google search volume. These variables improve forecast accuracy of the HAR model se- parately or in …
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Pricing stochastic volatility models using random grids
… the Heston model as well as the stochastic local volatility model. Consistent results are obtained for a call option under the various pricing methods using similar parameters as those used in the random grids paper. More specifically, when using a Heston model, consistent prices are obtained for …
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Pricing with Bivariate Unspanned Stochastic Volatility Models
Unspanned stochastic volatility (USV) models have gained popularity in the literature. USV models contain at least one source of volatility-related risk that cannot be hedged with bonds, referred to as the unspanned volatility factor(s). Bivariate USV models are the simplest case, comprising of one …
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Implementation of Bivariate Unspanned Stochastic Volatility Models
Unspanned stochastic volatility term structure models have gained popularity in the literature. This dissertation focuses on the challenges of implementing the simplest case – bivariate unspanned stochastic volatility models, where there is one state variable controlling the term structure, and one …
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Fractional stochastic volatility models: approximation, calibration and hedging
The area of modeling stochastic volatility using continuous time models has a long history and is always an interesting and vibrant area in financial mathematics, where the dynamic of the asset is a diffusion driven by Brownian motion and the dynamic of the volatility is associated with a diffusion …
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Stochastic Volatility Models for Contingent Claim Pricing and Hedging
… main argument that we emphasise is that novel models of option pricing, as is suggested by Hull and White (1987) [1] and others, must account for the discrepancy observed on the implied volatility curve. To achieve this we also propose that market volatility be modeled as random or stochastic …
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Estimating stochastic volatility models with student-t distributed errors
… the idea of Bollerslev (1987), estimating ARCH models with Student-t distributed errors, to estimating Stochastic Volatility (SV) models with Student-t distributed errors. It is unclear whether Gaussian distributed errors sufficiently account for the observed leptokurtosis in financial time …
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Asymptotics and numerics in rough and local stochastic volatility models
… from the modelling of asset prices. All these models have in common, that they permit the use of rough volatility processes, meaning that the fluctuations of stock prices are modelled via a very irregular process. Besides this underlying structure, statements about asymptotic behaviour play a …
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Extreme-Strike and Small-time Asymptotics for Gaussian Stochastic Volatility Models
<p>Asymptotic behavior of implied volatility is of our interest in this dissertation. For extreme strike, we consider a stochastic volatility asset price model in which the volatility is the absolute value of a continuous Gaussian process with arbitrary prescribed mean and covariance. By exhibiting …
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Stochastic Volatility Models: Option Price Approximation, Asymptotics and Maximum Likelihood Estimation
… option pricing function and extract the latent volatility, thereby overcoming one of the key difficulties in the estimation problem. The method is applied to estimate three popular stochastic volatility models, two of which have not previously been amenable to maximum likelihood estimation with …
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Calibrating the Hurst Parameter for Rough Volatility Models with Application in the South African Market
… calibration of any fractional stochastic volatility model is important for trading and risk management purposes. Under the rough Heston model proposed by El Euch et al. (2019), the Hurst parameter governs the roughness of the volatility process. This dissertation explores the different …
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Monte Carlo Methods for Derivative Pricing of Stochastic Volatility Models Driven by Fractional Brownian Motion
… options as well as variance swaps. Underlying models for price movements are driven by stochastic volatility models driven by fractional Brownian motion with H > 1/2 . These models exhibit a strong autocorrelation in volatility evolution. The models considered are fractional Ornstein Uhlenbeck, …
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Forecasting the South African rand 's variance and covariance using conditional heteroskedastic and realized volatility models
Includes abstract. Includes bibliographical references.
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Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market
… in the Bayesian framework to estimate Stochastic Volatility models using South African financial market data. A single move Gibbs sampler is used to sample parameters from the posterior distribution. Volatility is used as measure of an asset's risk. It is particularly important in risk management, …
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Option pricing with non-constant volatility
… past three decades, researchers have developed models to price options with non-constant asset price volatility. These models can be divided into deterministic volatility models and stochastic volatility models. Deterministic volatility models assume that volatility is determined by some …
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Long-memory stochastic volatility model calibration using deep neural nets
Widespread use of stochastic volatility models in the financial industry is bottlenecked by the complexity and intractability they present. Since the seminal work in quantitative finance by Black et al. and Merton, the infamous Black-Scholes model has been extensively used in the industry for …
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Local Stochastic Volatility—The Hyp-Hyp Model
Volatility modelling is used predominantly in order to explain the volatility smile observed in the market. Stochastic volatility models are mainly used to capture the curvature of a volatility smile while local volatility models generally model the skew. Jackel and Kahl ¨ (2008) present a …
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Path-dependent volatility: an application to the South African market
… have thus far focussed on three classes of volatility models, namely, constant volatility, local volatility and stochastic volatility. Pathdependent volatility models are a lesser known class of models which possess the key characteristic of completeness together with the ability to generate …
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Essays in volatility modelling
… mainly concerns some novel developments in volatility modelling. We first derive the diffusion limits of two recently proposed (discrete time) volatility models. Subsequently, we propose a new model that allows for conditional heteroskedasticity in the volatility of asset returns and …
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