Browsing by Department "African Inst. of Fin. Markets and Risk Mngnt"
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- ItemOpen AccessHow deep is your model? Network topology selection from a model validation perspective(2022-01-03) Nowaczyk, Nikolai; Kienitz, Jörg; Acar, Sarp K; Liang, QianDeep learning is a powerful tool, which is becoming increasingly popular in financial modeling. However, model validation requirements such as SR 11-7 pose a significant obstacle to the deployment of neural networks in a bank’s production system. Their typically high number of (hyper-)parameters poses a particular challenge to model selection, benchmarking and documentation. We present a simple grid based method together with an open source implementation and show how this pragmatically satisfies model validation requirements. We illustrate the method by learning the option pricing formula in the Black–Scholes and the Heston model.
- ItemOpen AccessQuantifying 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, ErikWe 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.
- ItemOpen AccessRecursive marginal quantization: extensions and applications in finance(2018) Rudd, Ralph; Kienitz, Jorg; Platen, EckhardQuantization techniques have been used in many challenging finance applications, including pricing claims with path dependence and early exercise features, stochastic optimal control, filtering problems and the efficient calibration of large derivative books. Recursive marginal quantization of an Euler scheme has recently been proposed as an efficient numerical method for evaluating functionals of solutions of stochastic differential equations. This algorithm is generalized and it is shown that it is possible to perform recursive marginal quantization for two higher-order schemes: the Milstein scheme and a simplified weak-order 2.0 scheme. Furthermore, the recursive marginal quantization algorithm is extended by showing how absorption and reflection at the zero boundary may be incorporated. Numerical evidence is provided of the improved weak-order convergence and computational efficiency for the geometric Brownian motion and constant elasticity of variance models by pricing European, Bermudan and barrier options. The current theoretical error bound is extended to apply to the proposed higher-order methods. When applied to two-factor models, recursive marginal quantization becomes computationally inefficient as the optimization problem usually requires stochastic methods, for example, the randomized Lloyd’s algorithm or Competitive Learning Vector Quantization. To address this, a new algorithm is proposed that allows recursive marginal quantization to be applied to two-factor stochastic volatility models while retaining the efficiency of the original Newton-Raphson gradientdescent technique. The proposed method is illustrated for European options on the Heston and Stein-Stein models and for various exotic options on the popular SABR model. Finally, the recursive marginal quantization algorithm, and improvements, are applied outside the traditional risk-neutral pricing framework by pricing long-dated contracts using the benchmark approach. The growth-optimal portfolio, the central object of the benchmark approach, is modelled using the time-dependent constant elasticity of variance model. Analytic European option prices are derived that generalize the current formulae in the literature. The time-dependent constant elasticity of variance model is then combined with a 3/2 stochastic short rate model to price zerocoupon bonds and zero-coupon bond options, thereby showing the departure from risk-neutral pricing.
- ItemOpen AccessThe impact of a change in sovereign credit ratings on stock market volatility: A comparison of emerging and developed countries(2018) Govender, Sharlene; Charteris, Ailie; Alhassan, Abdul LatifSovereign credit ratings affect a country’s financial well-being. The financial markets, at large, have become quite topical within the public space, as well as policy makers and academics. This area has been examined in detail, especially after the global financial crisis of 2008. Rating agencies have been under great scrutiny against their issued ratings and accused of favouring developed economies over developing ones by providing higher ratings to the former. Using a panel of emerging and developed countries over a period of ten years (June 2007 – June 2017), this study examines whether a change in sovereign credit ratings by one of the big three rating agencies has an effect on the volatility of the stock market. This dissertation makes use of an event study over various estimation windows, and the findings depict that changes in sovereign credit ratings do have an effect on stock market volatility. Rating downgrades tend to increase volatility whilst upgrades tend to decrease volatility. Countries that have lower ratings, classified as emerging economies, are no less sensitive to rating changes compared to developed markets and both observe a significant effect on volatility when there is a change in credit ratings. The credit rating agency that had the greatest impact on the volatility of the stock market in response to a rating change is S&P. This was for both upgrades and downgrades. Fitch and Moody’s did not elicit any significant findings. This shows that the market is more responsive to an announcement by S&P than the other agencies. An understanding of the actual effect of this volatility in the equity stock market will have implications for investors, governments, pension funds and asset holders by providing them with country risk assessments and giving them the ability to rebalance their portfolios as required. It also has an impact in determining the cost of capital and evaluating investments, which affect asset allocation decisions. This study has important information, which could help contribute to credit rating agencies’ understanding of the implications that their issued ratings have on the stock market and their contribution to volatility within the market place. The policy implications of this study could affect institutions, especially the Basel committee and banking institutions whom are highly affected by the policies set out by Basel.
- ItemOpen AccessThe Influence of Financing Structure on Performance of MSMEs in South African: "The Valley of Death"(2018) Seroka, Ngwanatau; Alhassan, Abdul LatifPrevious researchers, especially on large enterprises, have revealed that debt financing structure influences enterprise performance. Though the issue has been extensively researched, micro, small, and medium-sized enterprises (MSMEs) have traditionally been operating differently as compared to large enterprises in terms of their financial decisions, ownership and management style, and behaviour. Therefore, this study will explore the gaps encountered by all MSMEs to grow their businesses. These include forms and type of industry, firm size, asset tangibility, and a firm’s current assets in relation to its current liabilities and profitability level. The study examines the influence of financing structures on performance of micro, small and medium-sized enterprises (MSMEs) in South Africa. The ordinary least squares (OLS) technique of measurement is applied to examine the effects of financing structure on performance across various industrial sectors in the years 2013, 2014 and 2015. The findings in this study indicate an increase in the use of leverage to drive the influence of total debt on performance in all industrial sectors of MSMEs in South Africa. From the cross-sectional regression analysis, the results show that financing structure has a negative effect on the profitability of MSMEs, although not absolutely. The findings show that the size of the enterprise, asset tangibility, and the ratio of current assets to current liabilities are the most influential of borrowing decisions in total debt, short-term debt, and long-term debt. A significantly negative effect is observed for long-term debt, while short-term debt (STDR) exhibits a significantly positive effect. Thus the influence on MSMEs’ leverage on performance is driven by the usage of short-term debt. The variables of size of the firm, and ratio of current assets to current liabilities, do not have the same effect in all debt levels; the significance is substantially higher for long-term debt than for total debt and short-term debt. On the other hand, our empirical results suggested that transactional costs, and an asymmetric information problem in smaller firms, may lead to a mainly negative influence on size and total debt. The asset structure on profitability observed across the years showed mixed experiences. The ratio of current assets to current liabilities was found to be positive and significant on long-term debt and short-term debt leverage.
- ItemOpen AccessWealth Inequality in South Africa—The Role of Government Policy(2022-05-30) Fortuin, Marlin Jason; Grebe, Gerhard Philip Maree; Makoni, Patricia LindelwaIn South Africa, high levels of wealth inequality have persisted since 1994, to the extent that 1% of the population owns 50% of the wealth. This study examines how macroeconomic policies influenced wealth inequality in South Africa over the period 2010 to 2019 using a behavioural life-cycle model. Despite a decrease in wealth inequality over this period, the extent of this decrease is almost negligible. Results show government’s current policy model to redirect wealth from a very small tax base that is under increasing financial strain is unable to meet wealth redistributive targets. The South African government should change the wealth redistribution policy from redistribution through predominantly lump sums to creating an environment in which private enterprises are able to absorb the labour capital that South Africa possesses. An open labour market would support private and foreign direct investment into the economy, thereby strengthening economic growth and upliftment through increased income and the consequent ability to accumulate wealth.