نوسانات قیمت نفت، نرخ برابری نوسانات و بازار سهام غنا: مفهوم مدیریت پرتفوی و توقف اثربخشی

This study attempts to contribute to the literature on stock markets and energy prices by examining the dynamic volatility and volatility transmission between oil and Ghanaian stock market returns in a multivariate setting using the recently developed VAR–GARCH, VAR–AGARCH and DCC–GARCH frameworks. In turn, the models' results are used to compute and analyze the optimal weights and hedge ratios for oil-stock portfolio holdings. For comparison purposes and to put the paper more in the perspective of West Africa, the Nigerian stock market is also included in the analysis. Our findings point to the existence of significant volatility spillover and interdependence between oil and the two stock market returns. While spillover effects are stronger for Nigeria, the transmission of volatility is much more apparent from oil to stock than from stock to oil in the case of Ghana. Also, the study demonstrates evidence of short-term predictability in oil and stock price changes through time and reveals that conditional volatility changes more rapidly as result of substantial effects of past volatility rather than past news/shocks for all market returns. Moreover, we show that there is a slightly more effective hedge in the two stock markets under the DCC–GARCH framework (our preferred model) compared to the other two models, although hedging effectiveness is much greater for Ghana. On the whole, our findings for optimal hedge ratios are consistent with other studies and particularly the view that oil assets should be an integral part of a diversified portfolio of stocks and suggest that a better understanding of volatility links is crucial for portfolio management in the presence of oil price risk. Finally, the existence of multivariate asymmetric effects and dynamic conditional correlations as revealed by the VAR–AGARCH and DCC–GARCH models make it clear that the assumptions of symmetric effects and constant conditional correlations are not supported empirically.

Following the 1970s oil price shocks and the seminal work of Hamilton (1983) establishing oil price shocks as a factor contributing to recession in the United States, studies on the connections between oil price and the macro-economy have become of significant interest to financial practitioners, market participants and researchers. In particular, much attention has been attracted to the relationships between oil price shocks and stock markets, with the bulk of this literature focusing on developed countries. This interest has been fueled by the potential impacts of oil price changes on stock prices through their effects on corporate cash-flows and earnings, particularly being forced by the context of spectacular oil price fluctuations over the last years. In fact, Lin and Wesseh (2013c) note that fuel prices are subject to fluctuation making conventional sources of energy risky from a cost perspective. Reflecting on insights from the theory of equity valuation, since corporate cash-flows and discount rate reflect economic conditions such as inflation, interest rates, production costs, income, economic growth, and market confidence, etc., which can be influenced by oil shocks, stock prices may react significantly to patterns in oil price changes (Arouri et al., 2012). This study is therefore an attempt to add to the scarce literature (see Section 3) on developing countries and emerging markets especially in the context of Africa. To this end, we investigate the connections between oil prices and the stock market of Ghana, a country viewed as one of the economies that are going to be among the strongest growing in Africa. For comparison purposes and to put the paper more in the perspective of West Africa,1 we also include the Nigerian stock market in the analysis. It is no doubt that most Sub-Saharan African countries suffer serious research vacuum and the literature seems to pay no attention to these countries despite their dependency on oil and considerable growth records. Our analysis is particularly motivated by the lack of related studies for Ghana. Indeed, studies of this nature are crucial for developing policy options in Africa (see Wesseh and Niu, 2012 and Wesseh et al., 2013). The present paper has two major objectives: (1) To examine own conditional volatility for oil price returns and stock market returns and conditional cross series volatility transmission between oil price returns and stock market returns of Ghana and Nigeria, and (2) To use the estimated results to compute the weights of the series in an optimal portfolio of Ghana and Nigeria, and the optimal hedge ratios that minimize overall risk for holding the series in portfolios without affecting the expected returns in the two countries. Indeed, such an analysis is important for building accurate asset pricing models, generating accurate forecasts of the volatility of all markets, and evaluating the oil risk exposure via value-at-risk calculation. In addition, empirical insights from such analysis are equally crucial for hedging strategies and derivatives management (see Arouri et al., 2012, Ewing et al., 2002 and VO, 2009). At the empirical stage, we employ more recent and robust econometric techniques that examine shock and volatility. In particular, we apply three multivariate vector autoregressive-generalized autoregressive conditional heteroscedasticity models, VAR–GARCH developed by Ling and McAleer (2003), VAR–AGARCH proposed by McAleer et al. (2009) capable of capturing asymmetric relationships between returns, and the DCC–GARCH model proposed by Engle (2002). The models adopted offer the possibility to explore the conditional volatility dynamics of the series considered as well as the conditional cross effects and volatility spillover between series. They also provide meaningful estimates of the model's parameters with fewer computational complications than several other multivariate GARCH specifications. Furthermore, findings from the model can be used to analyze the diversification and hedging effectiveness across oil asset and markets equity. While a number of authors have applied the VAR–GARCH approach to various economic issues (e.g. Arouri et al., 2012, Chan et al., 2005, Chang et al., 2011, Hammoudeh et al., 2009 and Hammoudeh et al., 2010), to our knowledge, this study will be the first to apply the VAR–AGARCH model to emerging stock markets. The remainder of the article is organized as follows. Section 2 discusses the significance of oil prices to Ghanaian stock market in particular and economy in general and gives an overview of the Ghanaian stock market. Section 3 presents findings of previous works on the links between oil price and stock markets. Section 4 provides summary statistics of the dataset and its stochastic properties. Our empirical methodology and estimation technique is introduced and discussed in Section 5. Section 6 presents and discusses the obtained results. Section 7 draws the conclusion.

The purpose of this paper is to contribute to the literature on stock markets and energy prices by examining volatility, shocks and inter-shock and volatility transmission between the oil market and Ghanaian stock market index as compared to Nigeria. In turn the results are used to estimate the risk-minimizing hedge ratios to assess the hedge effectiveness between market returns and to calculate the optimal portfolio weights for favoring markets. Our analysis is based on three multivariate vector autoregressive–generalized autoregressive conditional heteroscedasticity models, VAR–GARCH developed by Ling and McAleer (2003), VAR–AGARCH proposed by McAleer et al. (2009) capable of capturing asymmetric relationship between returns, and DCC–GARCH mode introduced by Engle (2002). The empirical applications to weekly data document several findings. For instance, the study demonstrates evidence of short-term predictability in oil and stock price changes through time and reveals that conditional volatility changes more rapidly as result of substantial effects of past volatility rather than past news for both market returns. Significant interdependence and spillover in the conditional volatility between oil and both stock market returns is detected. While there appears to be stronger spillover effects for Nigeria, the transmission of volatility is much more apparent from oil to stock than from stock to oil in the case of Ghana. Significance of the multivariate asymmetric effects and the two DCC parameters for all market returns as revealed by the VAR–AGARCH and DCC–GARCH models make it clear that the assumptions of symmetric effects and dynamic conditional correlations are not supported empirically. Notwithstanding, diagnostic tests performed on the standardized returns and standardized squared returns suggest that all three models give reasonable approximation to heteroskedasticity and are flexible enough to capture the dynamics of oil and stock returns. Turning to our examination of optimal weights and hedge ratios, we find that optimal portfolios in Ghana and Nigeria should have stocks outweighing oil assets and that the stock investment risk can be hedged with relatively low hedging costs by taking a short position in the oil futures, options and swaps markets. In particular, we show that there is a slightly more effective hedge in the stock market under DCC–GARCH framework compared to the other two models. Comparing Ghana and Nigeria, we find that hedging is more effective in Ghana. On the whole, our findings for optimal hedge ratios are consistent with other studies and particularly the view that oil assets should be an integral part of a diversified portfolio of stocks and help increase the risk-adjusted performance of the hedged portfolio. Referencing results obtained for the oil-exporting Nigeria, Ghana being a future potential oil-exporter is likely to face even higher volatility spillovers and hedging difficulties in the future. Knowing this, Ghanaian policy makers and market practitioners should well in advance orchestrate measures to lower oil risk. For instance, there would be a need to create a more diversified energy structure especially in the transport and agricultural sectors. Acknowledgements The paper is supported by Newhuadu Business School Research Fund, the China Sustainable Energy Program (G-1305-18257), National Social Science Foundation of China (Grant No.12&ZD059), and Ministry of Education Grant No. 10JBG013).