Browsing by Author "Baker, Christopher"

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    Mixed Monte Carlo in the foreign exchange market
    (2017) Baker, Christopher; McWalter, Thomas; Searle Silverman, Searle; Maze, Sheldon
    The stochastic differential equation (SDE) describing the spot FX rate is of central importance to modelling FX derivatives. A Monte Carlo estimate of the discounted individual payoffs of FX derivatives is taken to arrive at the price, provided there does not exist a closed form solution for the price. One propagates the FX spot rate through time under risk-neutral dynamics to realise the before-mentioned payoffs. A drawback to Monte Carlo becomes evident when the model dynamics become more complicated, such as when more dimensions are added to the dynamics of the model. These additional dimensions can be stochastic volatility and/or stochastic domestic and foreign short rates. This dissertation describes the calibration of such a model using mixed Monte Carlo, as described in Cozma and Reisinger (2015), to both model-generated and market data. Profit and loss analysis of hedging FX derivatives using the mixed Monte Carlo method is conducted when hedging against both model-generated and market data .
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    Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models
    (2021-01-04) Feng, Yu; Rudd, Ralph; Baker, Christopher; Mashalaba, Qaphela; Mavuso, Melusi; Schlögl, Erik
    We focus on two particular aspects of model risk: the inability of a chosen model to fit observed market prices at a given point in time (calibration error) and the model risk due to the recalibration of model parameters (in contradiction to the model assumptions). In this context, we use relative entropy as a pre-metric in order to quantify these two sources of model risk in a common framework, and consider the trade-offs between them when choosing a model and the frequency with which to recalibrate to the market. We illustrate this approach by applying it to the seminal Black/Scholes model and its extension to stochastic volatility, while using option data for Apple (AAPL) and Google (GOOG). We find that recalibrating a model more frequently simply shifts model risk from one type to another, without any substantial reduction of aggregate model risk. Furthermore, moving to a more complicated stochastic model is seen to be counterproductive if one requires a high degree of robustness, for example, as quantified by a 99% quantile of aggregate model risk.