Common applications of asset-pricing models in practice rely on recalibrating model parameters periodically for effective risk management. Yet, these model parameters are often assumed to be constant over time, thereby countering the notion of readjusting these values. A possible solution to this problem is to recalibrate at times where observed market prices cannot realistically match model prices based on parameter values at those times. This dissertation aims to test the effectiveness of a possible algorithm which can be used in optimally identifying such times. An overview is provided of the recently proposed particle filter with accelerated adaptation which has demonstrated rapid time detection for changes in parameter values and has been applied to regime-shifting and stochastic volatility models. Numerical and graphical evidence of parameter and volatility estimation will be provided under regime-shifting parameters for the Heston (1993) stochastic volatility model. The filter demonstrates rapid adaptation in estimating parameter values and accurate estimation of the volatility process. Furthermore, we provide a discussion for possible extensions towards a metric for optimal recalibration times.

Regulations require banks to hold a minimum amount of capital for market risk resulting from their trading operations and prescribe two approaches to calculating this minimum capital requirement: (i) a Standardised Approach (SA); and (ii) an Internal Models Approach (IMA). The global financial crisis of 2008 highlighted flaws in the Basel 2 regulatory framework used by banks to calculate market risk capital charges for trading operations. In 2009, Basel 2.5 was introduced to deal with some but not all of the flaws of Basel 2. Both Basel 2 and 2.5 use the Value at Risk (VaR) risk measure as the basis to determine IMA capital charges. From 2022 onwards, Basel 2.5 will be replaced by the Fundamental Review of the Trading Book (FRTB), a new framework for calculating market risk capital charges for trading operations. The FRTB replaces VaR with the Expected Shortfall (ES) risk measure in the IMA and introduces a new SA. This dissertation investigates the impact the FRTB will have on market risk capital charges for portfolios of linear South African interbank interest rate products. Capital charges are calculated for these portfolios under the Basel 2, Basel 2.5 and FRTB regulatory frameworks. A comparison and analysis of the resulting capital charges is then presented.

We tackle the question of whether Trade and Quote data from high-frequency finance are representative of discrete connected events, or whether these measurements can still be faithfully represented as random samples of some underlying Brownian diffusion in the context of modelling correlation dynamics. In particular, if the implicit notion of instantaneous correlation dynamics that are independent of the time-scale a reasonable assumption. To this end, we apply kernel averaging non-uniform fast Fourier transforms in the context of the Malliavin-Mancino integrated and instantaneous volatility estimators to speed up the estimators. We demonstrate the implicit time-scale investigated by the estimator by comparing it to the theoretical Epps effect arising from asynchrony. We compare the Malliavin-Mancino and Cuchiero-Teichmann Fourier instantaneous estimators and demonstrate the relationship between the instantaneous Epps effect and the cutting frequencies in the Fourier estimators. We find that using the previous tick interpolation in the Cuchiero-Teichmann estimator results in unstable estimates when dealing with asynchrony, while the ability to bypass the time domain with the Malliavin-Mancino estimator allows it to produce stable estimates and is therefore better suited for ultra high-frequency finance. We derive the Epps effect arising from asynchrony and provide a refined approach to correct the effect. We compare methods to correct for the Epps effect arising from asynchrony when the underlying process is a Brownian diffusion, and when the underlying process is from discrete connected events (proxied using a D-type Hawkes process). We design three experiments using the Epps effect to discriminate the underlying processes. These experiments demonstrate that using a Hawkes representation recovers the empiricism reported in the literature under simulation conditions that cannot be achieved when using a Brownian representation. The experiments are applied to Trade and Quote data from the Johannesburg Stock Exchange and the evidence suggests that the empirical measurements are from a system of discrete connected events where correlations are an emergent property of the time-scale rather than an instantaneous quantity that exists at all time-scales.

As a consequence of the 2007 financial crisis, the market has shifted towards a multi-curve approach in modelling the prevailing interest rate environment. Currently, there is a reliance on the assumption of deterministic- or constant-basis spreads. This assumption is too simplistic to describe the modern multi-curve environment and serves as the motivation for this work. A stochastic-basis framework, presented by Mercurio and Xie (2012), with one- and two-factor OIS short-rate models is reviewed and implemented in order to analyse the effect of the inclusion of stochastic-basis in the pricing of interest rate derivatives. In order to preclude the existence of negative spreads in the model, a constraint on the spread model parameters is necessary. The inclusion of stochastic-basis results in a clear shift in the terminal distributions of FRA and swap rates. In spite of this, stochastic-basis is found to have a negligible effect on cap/floor and swaption prices for the admissible spread model parameters. To overcome challenges surrounding parameter estimation under the framework, a rudimentary calibration procedure is developed, where the spread model parameters are estimated from historical data; and the OIS rate model parameters are calibrated to a market swaption volatility surface.

The financial crisis in 2007 highlighted the credit and liquidity risk present in interbank (LIBOR) rates, and resulted in changes to the pricing and valuation of financial instruments. The shift to Overnight Indexed Swap (OIS) discounting and multi-curve framework led to changes in the construction of interest rate zero curves, with the OIS curve being central to this methodology. Developed markets, such as the European (EUR), were able to adopt this framework due to the existence of a liquid OIS market. In the case of the South African (ZAR) market, the lack of such tradeable instruments poses the issue of how to construct or infer the OIS curve. Jakarasi et al. (2015) proposed a method to infer the OIS curve through the statistical relationship between SAFEX ROD and 3M JIBAR. The extension of the statistical relationship used by Jakarasi et al. (2015) to more statistically rigorous models, capable of capturing more information relating to the relationship between the rates, arises from the expected cointegrating relationship exhibited between rates. This dissertation investigates the implementation of such statistical models to infer the OIS curve in the ZAR market.

Includes bibliographical references / Forecasting stock performance has long been one of the primary objectives of financial practitioners. Literature has shown that the classical linear approach to modelling the interactions among company-specific factors and its stock market re- turns in time have become less suited for capturing the movements of the stock market. Hence, attempts to predict the performance of a stock have become associated with additional layers of complexity. This has led to the adoption of non-linear approaches to forecast stock performance. This dissertation explores the performance of some non-linear models in the South African market. These were classification and regression trees (CART), logistic regression and a random forest approach com- pared against a linear regression model. Moreover, a hybrid model between CART and logistic regression was considered. The models fell into two categories (i.e., static and dynamic models). Using a set of classification and portfolio performance metrics it was found that that a dynamic modelling approach outperformed a static approach. Overall, the logistic and linear regression models dominated in terms of performance against the tree-based models and hybrid approaches. The results also demonstrated that a hybrid approach offered an improvement over a stand-alone CART.

The modelling of the short rate offers many advantages, with the models explored in this dissertation all offering closed-form, analytic formulae for bond prices and for options on bonds. Often, a vital primary condition is for a model to be calibrated to the initial term structure and to recover the bond prices observed in the market – that is, to be calibrated to the initial yield curve. Under the two exogenous models explored in this dissertation, the Hull-White and the CIR++, the effect of increasing the volatility parameter of the SDE increases the mean of the short rate. Increasing volatility of an SDE is a common approach to stress testing a model, as such, the consequences of bumping volatility in a calibrated model is a vital concern. The Hull-White model and CIR++ model were calibrated to market data, with the former being able to match the observed cap prices, while the latter failed, displaying an upper bound on cap prices. Investigating this, under CIR++ model, bond option prices are shown to not be straightforward increasing functions of the volatility parameter. In fact, for high volatility, bond option prices display an upper limit before decreasing, thus providing a limit to the level of cap prices too. This dissertation points to the reason residing in the underlying CIR model from which the CIR++ is based on, and the manner in which the model is extended

The Monte Carlo method (MC) is a common numerical technique used to approximate an expectation that does not have an analytical solution. For certain problems, MC can be inefficient. Many techniques exist to improve the efficiency of MC methods. The Multilevel Monte Carlo (ML) technique developed Giles (2008) is one such method. It relies on approximating the payoff at different levels of accuracy and using a telescoping sum of these approximations to compute the ML estimator. This dissertation summarises the ML technique and its implementation. To start with, the framework is applied to a European call option. Results show that the efficiency of the method is up to 13 times faster than crude MC. Then an American put option is priced within the ML framework using two pricing methods. The Least Squares Monte Carlo method (LSM) estimates an optimal exercise strategy at finitely many instances, and consequently a lower bound price for the option. The dual method finds an optimal martingale, and consequently an upper bound for the price. Although the pricing results are quite close to the corresponding crude MC method, the efficiency produces mixed results. The LSM method performs poorly within an ML framework, while the dual approach is enhanced.

A method to price American options under a stochastic volatility framework is introduced which is based on Rambharat and Brockwell (2010). We price American options under the Heston and Bates stochastic volatility models where volatility is assumed to be a latent process. The pricing algorithm is based on the least-squares Monte Carlo approach made popular by Longstaff and Schwartz (2001). Information about the volatility of the underlying asset is used to assist in solving the pricing problem. Since volatility is assumed to be a latent, a particle filter is used to estimate the filtering distribution of volatility. A summary vector is constructed which captures the essential features of the filtering distribution. At each time step before maturity, the elements of the summary vector and the current share price are used as explanatory variables in a regression function which estimates the continuation value of the option. Estimating the continuation value assists in finding the optimal time to exercise the option. This pricing approach is benchmarked against a method which assumes volatility is observable. Furthermore, our pricing approach is compared to simpler methods which do not use particle filtering. Results from our numerical experiments suggest the proposed approach produces accurate option prices.

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