The Impact of Oil Price and Oil Volatility Index (OVX) on the Exchange Rate in Sub-Saharan Africa:Evidence from Oil Importing/Exporting Countries

The Theory demonstrates that oil price and oil volatility (OVX) are significant determinants of economic activity; however, studies seldom consider both variables in the oil–exchange rate nexus and ignore the distributional heterogeneity of the exchange rate. We investigate their joint effect and employ both the quantile regression and Markov switching models to address this. We differentiate between positive/negative shocks and control for the effect of the global financial crisis in 2008 and the COVID-19 pandemic in 2020. We observe that OVX shocks significantly impact the exchange rate for all countries whereas, oil price shocks only affect the exchange rate of oil importing countries. Rising (falling) OVX causes the local currency to depreciate (appreciate). The impact of rising or falling OVX is the same for oil importing and oil exporting countries whereas the impact of rising and falling oil price varies. The impact of oil price and OVX on exchange rate is affected by market conditions. The exchange rate responds to oil price and OVX mostly at lower quantiles (bearish markets) for all countries, which reveals investors sensitivity. In contrast, a weak to no significant response is observed at the higher quantiles (bullish market). Our results are robust in model selection (Markov switching models)

Measures of Volatility, Crises, Sentiment and the Role of U.S. ‘Fear’ Index (VIX) on Herding in BRICS (2007–2021)

We look into determinants (volatility, crises, sentiment and the U.S. ‘fear’ index) of herding using BRICS as our sample. Investors herd selectively to crises and herding is a short-lived phenomenon. Herding was highest during the global financial crisis (only China was affected). There was no herding during the European debt crisis and COVID. With regard to the relationship between volatility and CSAD (cross sectional absolute deviation)/herding, a lower CSAD (movement in a specific direction) brings about less volatility. However, a high volatility amplifies herding (reduces CSAD), especially in China. Russia and South Africa are unresponsive to volatility levels (low/high) and herding. We also observe volatility heterogeneity. Different volatility measures have different effects on different markets. There is limited evidence to suggest that sentiment (based on principal component) Granger causes herding/CSAD. Herding is a period and market variant and unrelated to crises. The U.S. ‘fear’ index has a short-lived, limited effect on CSAD/herding (during COVID only) for all countries except China. In addition, Granger causality analysis indicates a two-way relationship between the U.S. ‘fear’ index and CSAD/herding, unrelated to crises

Issues in asset pricing, liquidity, information efficiency, asymmetric information and trading systems

Market microstructure is a relatively new area in finance which emerged as a result of inconsistency between actual and expected prices due to a variety of frictions (mainly trading frictions and asymmetric information) and the realisation that the trading process through which investors' demand is ultimately translated into orders and volumes is of greater importance in price formation than it was originally thought. Despite increased research in the area of liquidity, asset pricing, asymmetric information and trading systems, all subfields in the area of market microstructure, there are a number of questions that remain unanswered such as the effect of different trading systems on systematic liquidity, informational efficiency or components of the spread. This thesis aims at shedding light on those questions by providing a detailed empirical investigation of the effect of trading systems on systematic liquidity, pricing, informational efficiency, volatility and bid-ask spread decomposition mainly with respect to the UK market (FTSEIOO and FTSE250) and to a less extent with respect to the Greek market. Those two markets are at different levels of development/sophistication and are negatively correlated.The aims of this thesis are outlined in chapter one with chapter two providing a detailed review of the theoretical literature relevant to this study. Chapter three is the first empirical chapter and tests for the presence of a common underlying liquidity factor (systematic liquidity) and its effect on pricing for FTSE100 and FTSE250 stocks under different trading regimes. Results show the presence of commonality for FTSE100 and FTSE250 stocks although commonality is weaker for FTSE250 stocks and its role on pricing is reduced. Chapter four investigates the same issues with respect to the Greek market and we find that commonality appears to be stronger in some periods while it is reduced to zero for other periods. Chapter five focuses on the effect that changes in the trading systems can have on informational efficiency and volatility primarily with respect to FTSE100 and FTSE250. Different methodologies and data are employed for this purpose and produce similar results. We find that order driven markets are more responsive to incoming information when compared to quote driven markets. Volatility has a greater impact on the spread when the market is quote driven. We also examined if automated trading increased informational efficiency with respect to the Greek market. The results obtained indicated that the effect of automation was positive. Finally the last chapter focused on the effect of different trading systems on the components of the spread and their determinants. Our main finding is that the asymmetric component of the spread is higher under a quote driven market. Also stock volatility appears to affect the asymmetric component to a greater extent when the market is quote driven. We believe that the main justification for those findings is affirmative quotation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo