Shipping Investor Sentiment and International Stock Return Predictability

Stock return predictability by investor sentiment has been subject to constant updating, but reaching a decisive conclusion seems rather challenging as academic research relies heavily on US data. We provide fresh evidence on stock return predictability in an international setting and show that shipping investor sentiment is a common leading indicator for financial markets. We establish out-of-sample predictability and demonstrate that investor sentiment is also economically significant in providing utility gains to a mean-variance investor. Finally, we find evidence that the predictive power of sentiment works best when negative forecasts are also taken into account

Investor attention and carbon return: evidence from the EU-ETS

This paper firstly puts forward to employ investor attention obtained from Google trends to explain and forecast carbon futures return in the European Union-Emission Trading Scheme (EU-ETS). Our empirical results show that investor attention is a granger cause to changes in carbon return. Furthermore, investor attention generates both linear and non-linear effects on carbon return. The results demonstrate that investor attention shows excellent explanatory power on carbon return. Moreover, we conduct several out-of-sample forecasts to explore the predictive power of investor attention. The results indicate that incorporating investor attention indeed improve the accuracy of out-of-sample forecasts both in short and long horizons and can generate significant economic values. All results demonstrate that investor attention is a non-negligible pricing factor in carbon market

Investigating the Association between Oil VIX and Equity VIX: Evidence from China

To the best of our knowledge, this is the initial study to investigate the linkages between implied volatilities of global crude oil and Chinese equity markets. At the empirical stage, the bivariate VAR-GARCH model has been adopted to assess the relationship between oil volatility index (OVX) and Chinese equity market volatility index (VXFXI). In addition, we also employ the VAR-AGARCH approach for robustness check. The major findings of our empirical analysis can be summarized as follows. First, we do not find any evidence of return spillover between these two implied volatility indices. Second, there exists a unidirectional volatility spillover running from oil to equity options. The results thus suggest that global oil market embodies a crucial role in predicting the Chinese equity market trends. We finally document that significant portfolio diversification benefits are possible if investors hold options in both the oil and equity markets. Our results are robust in that applications of different methods lead to similar conclusions. The findings have important implication for investors and policymakers who are interested in derivative pricing, portfolio rebalancing and risk management practices.© 2020 World Scientific Publishing Company. Electronic version of a chapter published in Risk Factors and Contagion in Commodity Markets and Stocks Markets, 25-46. https://doi.org/10.1142/9789811210242_0002fi=vertaisarvioitu|en=peerReviewed

Effects of oil prices, food prices and macroeconomic news on Gcc stock markets

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.This thesis is based on three papers examining Gulf Cooperation Council (GCC) financial markets. The member countries of the GCC are Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates. These countries have transitioned from developing to frontier markets over the past ten years, but there is considerable debate about whether GCC economies are efficient or affected by shocks in oil and other commodity markets. The first paper (chapter 2) considers GCC stock market returns and examines how they are affected by oil price shocks using a bivariate VAR-GARCH(1,1) approach. The conclusion of this essay is that GCC economies are more affected by shocks than are other countries considered for comparison purposes. The second paper (chapter 3) discusses how food prices are affected by oil price shocks, and it examines possible parameter shifts between food and oil that result from four recent events, including renewable fuel policies and the financial crisis. The third paper (chapter 4) uses an empirical approach to compare a least squares model and a non-linear Markov switching model to measure the effect of newspaper sentiment on stock market performance. The results indicate that all information is important to stock market investors and that non-linear models are better predictors of stock market performance then linear models when using data from newspaper articles. Chapter 5 offers some final conclusions and remarks

Bunker fuel, commodity prices and shipping market indices following the COVID-19 pandemic. A time-frequency analysis.

This paper deals with the analysis of the evolution of international trade after COVID-19, examining commodity prices, the shipping industry, and the influence of the cost of bunker fuel. To this end, we use techniques based on fractional integration, fractional cointegration VAR (FCVAR) and wavelet analysis. Monthly data relating to heavy fuel oil prices and the shipping market from October 2011 to September 2021 are used. Using fractional integration in the post-break period, a lack of mean reversion is observed in all cases, which means that, for the commodity prices and shipping market indices, a change in trend will be permanent after COVID-19 unless strong measures are carried out by the authorities. Using wavelet analysis, we conclude that the demand shock represented in the indices mentioned above has led the price of fuel oil since the beginning of the pandemic, and bunker fuel is not relevant in determining the cost of maritime transport.post-print2426 K

Sentiment effects in professionally traded markets: evidence from oil and emissions futures

This thesis shows that sentiment has influence in professionally traded oil and emissions markets. The sentiment index of Baker and Wurgler (2006) is adapted for the oil markets and is used to show that sentiment has a positive effect on WTI and Brent crude oil prices. Having established the value of this index in the oil markets it is extended to include the wider energy markets and used to show that sentiment also has an effect in the EU emissions trading scheme (EU ETS). It is found that there is some evidence that decisions of the European Parliament (EP) are associated with a drop in emission allowance (EUA) prices particularly when these decisions occur at times of low sentiment, low news exposure and when they come from non-party political sources. It is found that an increase in volatility of EUA returns is associated with EP decisions made at these times. In order to investigate further the effect of sentiment in the EU ETS, sentiment measured from tweets concerning the emissions market is shown to predict price level and volatility using intra-day data. Bi-directional Granger causality is found between changes in emissions market sentiment and EUA returns, this is especially true for negative sentiment. There is only very weak evidence of an association between climate change sentiment and the EUA returns showing that the EU ETS is not very high in the consciousness of people posting tweets about climate change. Finally, there is some evidence that energy commodity prices and stock market returns can explain, but not predict, EUA prices. This suggests that the EU ETS is efficient with regard to this fundamental information but that in general the Efficient Market Hypothesis does not provide a complete description of the market dynamics. This thesis therefore shows not only that the Efficient Market Hypothesis does not provide a complete description of market dynamics but that sentiment does not rely on uninformed traders to have a real and substantial effect in the emissions and oil markets

Modeling extreme risk spillovers between crude oil and Chinese energy futures markets

This paper aims to model the extreme risk spillovers between crude oil and Chinese energy futures markets to assess the effect of excessive oil price volatility on Chinese energy sectors. To this end, we set up a Generalized Autoregressive Conditional Heteroskedasticity - Extreme Value Theory Value-at-Risk specification (or GARCH-EVT-VaR hereafter) to flexibly model extreme risks. Moreover, we focus on two international crude oil futures markets and ten Chinese energy futures markets to measure the extreme risk spillovers. Our findings point to two main results. First, we find significant evidence of extreme risk spillovers from the two international crude oil markets to Chinese energy futures markets, which are asymmetric. More specifically, the spillover effects across extreme risks are more significant than those measured with the return series. Second, some Chinese energy future markets also exhibit internal extreme risk spillovers from the petrochemical sector to the coal sector. These findings reveal the potential vulnerability of Chinese energy sectors and call for active risk management policies to better hedge Chinese energy futures markets against extreme events

A Review of Barriers to Full-Scale Deployment of Emissions-Reduction Technologies

     Innovative clean technologies are part of the solution to reducing greenhouse gas emissions in both Canada and Alberta, particularly in the latter’s petroleum industry. However, while governments and their agencies may provide policies and financial support, proponents of cleantech still face numerous barriers to full deployment and commercialization.  To navigate the innovation and funding process successfully, it’s crucial for proponents to know the factors that impact the effective commercialization of cleantech innovations. They must also understand the role policies play in either supporting or hindering favourable outcomes.  Start-ups require support that focuses on innovation with a strong commercial potential, while scale-ups need to rely on proven strengths if they want to obtain private sector support for growth. Granting agencies and governments have an important role in supporting innovation. More clearly demonstrating and communicating their due diligence around funding decisions justifies expenditure of public money. Moreover, their decisions can and should send a signal to private sector financiers whether a certain innovation represents a good investment. Due diligence equally works to signal financiers when a specific project does not merit investment.       Innovative clean technologies are part of the solution to reducing greenhouse gas emissions in both Canada and Alberta, particularly in the latter’s petroleum industry. However, while governments and their agencies may provide policies and financial support, proponents of cleantech still face numerous barriers to full deployment and commercialization.  To navigate the innovation and funding process successfully, it’s crucial for proponents to know the factors that impact the effective commercialization of cleantech innovations. They must also understand the role policies play in either supporting or hindering favourable outcomes.  Start-ups require support that focuses on innovation with a strong commercial potential, while scale-ups need to rely on proven strengths if they want to obtain private sector support for growth. Granting agencies and governments have an important role in supporting innovation. More clearly demonstrating and communicating their due diligence around funding decisions justifies expenditure of public money. Moreover, their decisions can and should send a signal to private sector financiers whether a certain innovation represents a good investment. Due diligence equally works to signal financiers when a specific project does not merit investment.    The need to find innovative solutions to reducing emissions may seem pressing, but the race should not be to the swiftest. De-risking for commercialization means that a proponent must firmly establish that the technology works, is economically feasible and can attain sufficient market penetration for a return on investment to the prospective financier, as well as provide socio-economic and environmental benefits.  Trying to simplify or speed up the stages of innovation and the funding process means proponents can be exposed to incompletely proven and riskier technologies, which can damage credibility with financiers. A balance must be struck between the financier’s wish to expedite the de-risking process and the need to avoid inadequate de-risking which can jeopardize the project and its funding at a later stage.  Distinctions must also be made between firm-level support, which allows a company more flexibility in pursuing or cancelling projects, and project-level supports, in which the funding is specifically targeted for use in the development of a particular innovation and has a defined end point.  Cleantech innovation in Alberta faces added hurdles associated with a post-2014 economic downturn that has reduced some firms’ cash flows and has made firms, as well as government, less inclined to support cleantech innovations. This situation makes it crucial for innovation proponents seeking funding to distinguish clearly between a proposed project’s economic and environmental benefits. A technology whose primary benefit is reducing emissions is susceptible to changes in emissions pricing or regulations, and thus is not an attractive candidate for investors. An innovation that primarily reduces costs but offers a secondary environmental benefit is a better investment because it is much less sensitive to policy changes.  Alberta innovators must make sure they emphasize the economic benefits, and do their due diligence and careful de-risking if they want to surmount the added obstacles. Cleantech innovation doesn’t have to become a casualty of the provincial economic environment if the proper steps in the innovative and fiscal processes are conscientiously followed

Volatility forecasting in the Chinese commodity futures market with intraday data

Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms

Review on efficiency and anomalies in stock markets

The efficient-market hypothesis (EMH) is one of the most important economic and financial hypotheses that have been tested over the past century. Due to many abnormal phenomena and conflicting evidence, otherwise known as anomalies against EMH, some academics have questioned whether EMH is valid, and pointed out that the financial literature has substantial evidence of anomalies, so that many theories have been developed to explain some anomalies. To address the issue, this paper reviews the theory and literature on market efficiency and market anomalies. We give a brief review on market efficiency and clearly define the concept of market efficiency and the EMH. We discuss some efforts that challenge the EMH. We review different market anomalies and different theories of Behavioral Finance that could be used to explain such market anomalies. This review is useful to academics for developing cutting-edge treatments of financial theory that EMH, anomalies, and Behavioral Finance underlie. The review is also beneficial to investors for making choices of investment products and strategies that suit their risk preferences and behavioral traits predicted