Masters Degrees (Statistics and Actuarial Science)

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Browsing Masters Degrees (Statistics and Actuarial Science) by browse.metadata.advisor "Alfeus, Mesias"

Now showing 1 - 4 of 4
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    Analysis to indicate the impact Hindsight Bias have on the outcome when forecasting of stock in the South African equity market
    (Stellenbosch : Stellenbosch University, 2023-12) Heyneke, Anton Lafrass; Conradie, Willie; Alfeus, Mesias; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY: A novel Artificial Neural Network (ANN) framework presented in this study has the ability to mimic the effect that cognitive biases, specifically hindsight bias has on the financial market. This study investigates how hindsight bias influences models and their outcomes. During this study the hindsight bias effect will be measured within a South African context. The decisions that people make when faced with uncertainty are characterized by heuristic judgments and cognitive biases. If these characteristics are systematic and confirmed through research and literature related to this topic, it would form a quintessential part to the explanation of the behaviour of financial markets. This research presents a methodology that could be used to model the impact of cognitive biases on the financial markets. In this study, an ANN will be used as a stand-in for the decision-making process of an investor. It is important to note that the selection of the companies, on which the ANN will be trained, validated and tested, demonstrated cognitive bias during the study's preparation. Though there are many cognitive biases that have been identified in the literature on behavioural finance, this study will concentrate solely on the impact of hindsight bias. On financial markets, hindsight bias manifests when outcomes seem more predictable after they have already happened. This study attempts and succeeds – to some degree - to replicate the return characteristics of the ten chosen companies for the assessment period from 2010 to 2021. The study described here may still be subject to various cognitive biases and systemic behavioural errors in addition to the hindsight bias. The further application of this technique will stimulate further research with respect to the influence of investor behaviour on financial markets.
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    Enhancing realised volatility prediction in emerging markets
    (Stellenbosch : Stellenbosch University, 2023-12) Maphatsoe, Phuthehang; Alfeus, Mesias; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY: This research assignment introduces a comprehensive framework aimed at improving the accuracy of realised volatility forecasts within the context of the South African financial market. The fundamental approach is rooted in the utilisation of high-frequency data and the employment of volatility models that effectively capture the inherent high persistence commonly observed in financial markets. The study is particularly centred on the evaluation of four distinct models: the Heterogeneous AutoRegressive (HAR), Generalised AutoRegressive Conditional Heteroscedasticity (realGARCH), R.ecurrent Conditional Heteroskedasticity (RECH), and the R.ough Fractional Stochastic Volatility (RFSV) models. Furthermore, the study extends these models to incorporate the South African implied volatility (IV), referred to as the South African Volatility Index (SAVI), as an exogenous variable, with the expectation that this augmentation will further refine the accuracy of volatility estimations. These selected models are intentionally designed to capture the intricate dynamics and long-range dependencies that are evident within financial time series, characteristics often overlooked by conventional forecasting methods. The empirical investigation is based on the examination of four key financial indices within the South African market. The findings of this extensive analysis highlight the distinctive performance of each model in terms of capturing long-term volatility patterns. Notably, the HAR model emerges as the most adept at capturing these enduring patterns, while the realGARCH, R.ECH, and RFSV models also display commendable performance, albeit to varying degrees. Furthermore, the inclusion of the SAVI as an exogenous variable is found to enhance the empirical fit and predictive capacity of the models. This enhancement is particularly evident when assessing forecasting accuracy across both one-day and multi-period horizons. These results affirm the effectiveness of the chosen models and provide valuable insights into their suitability for modelling the South African financial market's unique characteristics. In a broader context, this study offers essential insights into realised volatility forecasting within the South African financial market. The practical implications of these findings are substantial, as they provide practitioners and investors with the knowledge required to make well-informed decisions.
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    Modern portfolio optimisation under regime switching
    (Stellenbosch : Stellenbosch University, 2022-04) Steenkamp, Cara Yvette; Alfeus, Mesias; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY: The main objective of this assignment is to consider modern portfolio optimisation under regimes. Unobservable regimes are assumed to be modulated by a time-change Markov process. These models are well-known as Markov regime switching models. The Markov regime switching models are applied to portfolios that consist of a lagged model and a factor model. The lagged model represents a portfolio of 20 stocks which have been lagged by a day and then classified into regimes whereas the factor model uses 5 different global risk factors or measures namely, the 3 Fama-French factors, VIX and a spread between the 3-month JIBAR rate and SAFEX overnight rate to estimate the unobserved regimes for the portfolio. This assignment considers two regimes. Two regimes are classified representing the bull and bear markets, periods when the financial market is doing well and when the market is on a downturn respectively. Thereafter, optimisation is performed by taking the estimated regimes into account and obtaining the optimal portfolio allocations. Optimisation methods such as Sharpe ratio method and risk budget method are investigated. For each of these optimisation methods the portfolios were rebalanced to evaluate the financial markets at the start of the new investment period, classify it either into a new regime or remaining in the current state and then adjusting the portfolio weights. Portfolio optimisation including the regimes are then compared to classical modern portfolio optimisation without regimes consideration. Results show that portfolio optimisation with regimes obtained the highest Sharpe ratio, indicating the economic benefit of inclusion of regime switching characteristics in modern portfolio optimisation.
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    Roll-over risk in the South African interest rate market
    (Stellenbosch : Stellenbosch University, 2022-12) von Ahlften, Edela; Alfeus, Mesias; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY: This research assignment entails applying and extending the framework of Alfeus et al. (2020) to conduct an empirical analysis of the presence of roll-over risk in the emerging South African interest market. Alfeus et al. (2020) started from the observation that the swap basis spreads persistent in the market since the Global Financial Crisis of 2008 contradict classic arbitrage arguments and developed a framework where the spreads persist as a result of the presence of roll-over risk. This is the risk one might not be able to refinance (“roll over”) debt at the prevailing market rate when funding longer-term lending by shorter-term borrowing. Roll-over risk is explicitly modelled as a spread added to the overnight borrowing cost and can be modelled as a single risk by calibrating to Overnight Index Swaps (OIS), Interest Rate Swaps (IRS), and basis swaps, or separated into a “credit downgrade risk” and a “funding liquidity risk” component by adding Credit Default Swaps (CDS) to the set of calibration instruments. In South Africa, interest rate swaps referencing the 3-month Johannesburg Interbank Average Rate (JIBAR) are liquidly traded; there is, however, neither a liquid OIS nor CDS market. The framework of Alfeus et al. (2020) is therefore implemented in the South African market by first estimating the South African rand (ZAR) OIS curve, then calibrating the model to this curve and interest rate swaps referencing the 3-month JIBAR. The modelling from Alfeus et al. (2020) is extended by utilizing a term structure model with deterministic jumps when specifying the stochastic dynamics for the model variables.