Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 21 for “"Heston model"”.
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Asymptotics of the Rough Heston Model
… of a new generation of stochastic volatility models. Such models are able to capture a wide range of stylised facts that classical models simply do not. While these models have sound mathematical underpinnings, they are difficult to implement, largely due to the fact that fractional Brownian …
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Parameter estimation of a bivariate diffusion process : the Heston model
… research is to estimate the parameters on the Heston (1993) model, which models the movement of asset prices assuming that the asset price volatility is stochastic. The paper concentrates on estimating these parameters by approximating the transitional probabilities of the diffusion process …
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Parametric Estimation of the Heston Model under the Indirect Observability Framework
Estimating parameters in a given stochastic model from a discrete dataset has wide applications in various scientific studies. However, it is also common that the available data are not generated from the stochastic model under investigation, but come from some other sources. For instance, realized …
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Two dimensional COS method for pricing early-exercise and discrete barrier options under the Heston Model
… and barrier options under the dynamics of the Heston stochastic volatility model. The two-dimensional nature of the Heston model makes the pricing of these options problematic, as the risk-neutral expectations need to be calculated at each exercise/observation date along a continuum of the two …
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Applications of Gaussian Process Regression to the Pricing and Hedging of Exotic Derivatives
… exotic options under stochastic volatility models like the Heston model. The purpose of this research is to apply the Gaussian Process Regression (GPR) method to the pricing and hedging of exotic options under the Black-Scholes and Heston model. GPR is a supervised machine learning technique …
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Break-Even Volatility
… to MATLAB. We extend the methodology to the Heston model by changing the reference model in the hedging process. Resultantly, the need to employ characteristic function pricing methods arises to calculate the Heston model sensitivities. The break-even volatility solution is then found by …
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The Lifted Heston Stochastic Volatility Model
… the market, without resorting to non-Markovian models? We show that, in terms of skew, the Heston model cannot match the market at both long and short maturities simultaneously. We introduce Abi Jaber (2019)'s Lifted Heston model and explain how to price options with it using both the cosine …
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A review of current Rough Volatility Methods
… those wishing to implement these techniques. The models of rough dynamics are built upon the fractional Brownian Motion and its associated powerlaw kernel. One such model is called the Rough Heston, an extension of the Classical Heston model, and is the main model of focus for this dissertation. …
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Calibrating the Hurst Parameter for Rough Volatility Models with Application in the South African Market
… 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 calibration …
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Model Misspecification and the Hedging of Exotic Options
Asset pricing models are well established and have been used extensively by practitioners both for pricing options as well as for hedging them. Though Black-Scholes is the original and most commonly communicated asset pricing model, alternative asset pricing models which incorporate additional …
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Pricing stochastic volatility models using random grids
… others. If one method is used to calibrate the model to market conditions, but another method is used to price the asset, the results obtained may be inconsistent. This dissertation addresses the fundamental problem of this bias that is introduced when calibrating and pricing options using …
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Essays on financial econometrics : cojump detection and density forecasting
… one week, two weeks to one month. We use the Heston model which incorporates stochastic volatility to extract risk-neutral densities from option prices. From historical high-frequency returns, we use the HAR-RV model to calculate realised variances and lognormal price densities. We use a …
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Model Calibration with Machine Learning
… the application of neural networks to financial model calibration. It provides an introduction to the mathematics of basic neural networks and training algorithms. Two simplified experiments based on the Black-Scholes and constant elasticity of variance models are used to demonstrate the …
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Application of Effective Markovian Projection to SABR and Heston Models
Model flexibility is often at odds with tractable pricing, and models with tractable pricing often lack flexibility. This poses an issue when calibrating a model to market data where tractability and flexibility are both required. We investigate an approach that allows one model to be projected …
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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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Optimal choices: mean field games with controlled jumps and optimality in a stochastic volatility model
… applied to a simple illiquid inter-bank market model, where the banks can adjust their reserves only at the jump times of some given Poisson processes with a common constant intensity, and some numerical results are provided. In the second part a stochastic optimization problem is presented. …
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Variable Annuity -- Laps Behavior
… a comprehensive exploration of mathematical models for Variable Annuities (VAs), focusing on the dynamics of policyholder behavior and the implications for pricing and risk management. VAs are complex financial instruments offering various guarantees, such as minimum death and living …
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Volatility Model Pricing and Calibration with Neural Networks using Bayesian Optimisation
Stochastic Alpha, Beta, Rho (SABR) and Heston Volatility models have been used in the financial industry due to their ability to price options as a function of time to maturity and moneyness. Implied volatilities for these models are accurately estimated using a numerical integration approach, …
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Approximating the Heston-Hull-White Model
The hybrid Heston-Hull-White (HHW) model combines the Heston (1993) stochastic volatility and Hull and White (1990) short rate models. Compared to stochastic volatility models, hybrid models improve upon the pricing and hedging of longdated options and equity-interest rate hybrid claims. When the …
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Stochastic modelling of new phenomena in financial markets
… in financial markets, to which stochastic models have to be adapted. This dissertation presents two new methodologies, one for modeling the “basis spread”, and the other for “rough volatility”. The former gained prominence during the GFC and continues to persist, while the latter has become …
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