Pierre J. Venter

In this paper, the use of artificial neural networks (ANNs) is proposed to approximate the option price sensitivities of Johannesburg Stock Exchange (JSE) Top 40 European call options in a classical and a modern multi-curve framework. The ANNs were trained on artificially generated option price data given the illiquid nature of the South African market, and the out-of-sample performance of the optimized ANNs was evaluated using an implied volatility surface constructed from published volatility skews. The results from this paper show that ANNs trained on artificially generated input data are able to accurately approximate the explicit solutions to the respective option price sensitivities of both a classical and a modern multi-curve framework in a real-world out-of-sample application to the South African market.

In this paper, the generalised autoregressive heteroskedasticity (GARCH) model is applied to the pricing of collateralised options in the South African equity market. Symmetric GARCH and nonlinear asymmetric GARCH (AGARCH) models are considered. The models are used to price fully collateralised and zero collateral options (European, Asian, and lookback options). The effect of collateral is illustrated by the difference between zero collateral and fully collateralised option price surfaces. Finally, the effect of asymmetry is shown by the difference between the symmetric and asymmetric GARCH option price surfaces.

In this paper, the Heston–Nandi futures option pricing model is applied to Bitcoin futures options. The model prices are compared to market prices to give an indication of the pricing performance. In addition, a multivariate Bitcoin futures option pricing methodology based on a multivatiate GARCH model is developed. The empirical results show that a symmetric model is a better fit when applied to Bitcoin futures returns, and also produces more accurate option prices compared to market prices for two out of three expiry dates considered.

In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a constructed implied volatility surface. In addition to this, the numerical accuracy of the better performing network was compared to a Monte Carlo simulation in a separate numerical experiment. It was found that the bootstrap aggregating ensemble network outperformed the artificial neural network and produced price estimates within the error bounds of a Monte Carlo simulation when pricing derivatives in a multi-curve framework setting.

The focus of this paper is the foreign exchange (FX) variance risk premium and the modelling of FX volatility indices of both a developed (United States Dollar) and an emerging market (South African Rand). Regarding the methodology, the variance risk premium is estimated as the difference between realised variance and the variance obtained from the volatility index, and different univariate Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models are considered for the modelling of the volatility index. Empirical results indicate that the variance risk premium is negative on average, this is consistent with previous findings in the literature. Furthermore, asymmetric GARCH models outperform the symmetric GARCH model when modelling FX implied volatility indices.

In this paper, a feed-forward artificial neural network (ANN) is used to price Johannesburg Stock Exchange (JSE) Top 40 European call options using a constructed implied volatility surface. The prices generated by the ANN were compared to the prices obtained using the Black-Scholes (BS) model. It was found that the pricing performance of the ANN significantly improves when the number of training samples are increased and that ANNs are able to price European call options in the South African market with a high degree of accuracy.

In this paper, the pricing performance of the generalised autoregressive conditional heteroskedasticity (GARCH) option pricing model is tested when applied to Bitcoin (BTCUSD). In addition, implied volatility indices (30, 60-and 90-days) of BTCUSD and the Cyptocurrency Index (CRIX) are generated by making use of the symmetric GARCH option pricing model. The results indicate that the GARCH option pricing model produces accurate European option prices when compared to market prices and that the BTCUSD and CRIX implied volatility indices are similar when compared, this is consistent with expectations because BTCUSD is highly weighted when calculating the CRIX. Furthermore, the term structure of volatility indices indicate that short-term volatility (30 days) is generally lower when compared to longer maturities. Furthermore, short-term volatility tends to increase to higher levels when compared to 60 and 90 day volatility when large jumps occur in the underlying asset.

Six isolates of Campylobacter with similar non-standard colonial morphologies were identified during studies isolating Campylobacter from bird faeces and rivers in New Zealand. Genomic (16S rRNA gene sequencing and whole genome analysis) and phenotypic (MALDI-TOF analysis and conventional biochemical tests) showed that the isolates form a monophyletic clade with genetic relationships to Campylobacter coli / Campylobacter jejuni and Campylobacter peloridis /Campylobacter amoricus. They may be distinguished from other Campylobacter by their MALDI-TOF spectral pattern, their florid α-haemolysis, their ability to grow anaerobically at 37 °C, and on 2 % NaCl nutrient agar, and their lack of hippuricase. This study shows that these isolates represent a novel species within the genus Campylobacter for which the name Campylobacter novaezeelandiae sp. nov. is proposed. The presence of C. novaezeelandiae in water may be a confounder for freshwater microbial risk assessment as they may not be pathogenic for humans. The type strain is B423b^T (=NZRM 4741^T=ATCC TSD-167^T).