Modelling and forecasting volatility in the fishing industry: a case study of Western Cape Fisheries
The Western Cape Fishing industry has been a subject of discussion in numerous papers, in which the thrust has been to seek ways of sustaining the significantly fluctuating business. Common risk factors have been identified and strategies for managing the fishing business in turbulent periods have been proposed over the years. A closer examination of previous literature as well as empirical evidence indicate that the business has less to do to control or minimize the impact of most of its external factors, which include the Government imposed Total Allowable Catch (TAC) limit, the variability in natural marine populations, environmental factors and fuel price oscillations. In the interest of curbing the variability component which is borne by the internal factors, this study brings on board a quantitative dimension to the evaluation of the four commonly cited internal factors, namely; Earnings Per Share (EPS), Margin of Safety (MOS), Free Cash-Flow (FCF) and the Net-Worth (NW) on volatility of the fishing business. The performance of five large JSE-listed fishing firms: Brimstone, Oceana, Premier Fishing, Sea Harvest and Irvin & Johnson, is investigated with the view of modelling and forecasting their volatilities. Initially, the comparison of volatility forecasts from symmetric and asymmetric GARCH-family models is employed. The results of competing models are tested using cross-validation of mean error measures and the Superior Predictive Ability (SPA) and Model Confidence Set (MCS) tests. Later, a Vector Autoregressive (VAR) model is applied to assess the impact of the four commonly cited internal factors on volatility. The research analysis results reveal a generally high volatility of the Western Cape fishing sector stocks. When univariate GARCH models are applied, the asymmetric GARCH-family models (EGARCH and GJR), with fat tails, appear dominant in the sets of competing models for all stocks, which highlights evidence of the leverage effect in the sector. However, GARCH (1,1), outperformed its counterparts in modelling and forecasting Irvin & Johnson (AVI) and Oceana (OCE) stocks. In the VAR modelling process, the Granger-causality tests indicate limited causal-relationship between EPS, MOS, FCF and the company Net-worth with the companies’ volatility measures. The variance decomposition of the 10-year ahead forecast of volatility indicates that volatility lag, free cash flow and networth have the largest contribution on volatility in the long-run, followed by margin of safety. In view of the above observations, the research discusses recommendations to the Western Cape fishing business to improve business returns and sustainability.
Dissertation submitted in partial fulfillment of the requirements for the degree of Masters of Management in Finance and Investments (MMFI) in the Graduate School of Business Administration University of the Witwatersrand 2017.
Nzombe, Jotham (2017) Modelling and forecasting volatility in the fishing industry: a case study of Western Cape Fisheries, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/23217>