Perry Sadorsky

The FinTech sector is growing rapidly, prompting a need to explore effective investment diversification strategies for stocks in this sector. The existing literature has identified the benefits of using clean energy stocks to diversify stock portfolios and the purpose of this research is to estimate how useful clean energy stocks are for diversifying an investment in FinTech stocks. This study uses a QVAR model to estimate the dynamic return connectedness between FinTech stocks and clean energy stocks for the period September 2016 to April 2024. Total connectedness is time varying and is higher in the tails than at the median. The onset of the COVID-19 pandemic had a large but short-term impact on connectedness. Under normal market conditions, systemic risk increases by 3.5% per year. FinTech is a net transmitter of shocks to nuclear energy but is mostly unaffected by shocks from wind, solar, and nuclear energy stocks illustrating the diversification benefits of these sub-sectors. Portfolio analysis shows that adding solar, wind, and nuclear energy to a portfolio with FinTech can produce higher risk adjusted returns and lower drawdown than an investment solely in FinTech stocks. These results are robust across various portfolio rebalancing frequencies (daily, weekly, monthly). For example, a minimum connectedness portfolio rebalanced daily has an average annual return of 11% and a Sharpe ratio of 0.37. These values are higher than their respective values for an investment solely in FinTech stocks (5.4%, 0.11). Thus, clean energy stocks do provide diversification benefits for investments in FinTech stocks.

Non-fungible tokens (NFTs) are one-of-a-kind digital assets that are stored on a blockchain. Examples of NFTs include art (e.g., image, video, animation), collectables (e.g., autographs), and objects from games (e.g., weapons and poisons). NFTs provide content creators and artists a way to promote and sell their unique digital material online. NFT coins underpin the ecosystems that support NFTs and are a new and emerging asset class and, as a new and emerging asset class, NFT coins are not immune to economic uncertainty. This research seeks to address the following questions. What is the time and frequency relationship between economic uncertainty and NFT coins? Is the relationship similar across different NFT coins? As an emerging asset, do NFT coins exhibit explosive behavior and if so, what role does economic uncertainty play in their formation? Using a new Twitter-based economic uncertainty index and a related equity market uncertainty index it is found that wavelet coherence between NFT coin prices (ENJ, MANA, THETA, XTZ) and economic uncertainty or market uncertainty is strongest during the periods January 2020 to July 2020 and January 2022 to July 2022. Periods of high significance are centered around the 64-day scale. During periods of high coherence, economic and market uncertainty exhibit an out of phase relationship with NFT coin prices. Network connectedness shows that the highest connectedness occurred during 2020 and 2022 which is consistent with the findings from wavelet analysis. Infectious disease outbreaks (COVID-19), NFT coin price volatility, and Twitter-based economic uncertainty determine bubbles in NFT coin prices.

This paper examines the connectedness and spillovers between decomposed oil shocks and Indian sectoral equities in a time-frequency domain using the most recent Barunik and Krehlik (2018) approach. Our empirical results show that the oil demand shock is the major spillover transmitter across all time horizons followed by the risk shocks; while oil supply shock appears to be a net receiver in all the frequency bands indicating the differential impact of oil shocks. Among the sectors, Basic Materials and Finance receive the highest spillovers from oil shocks in the short-term; while Consumer Discretionary Goods & Services and Industrials join the list in the medium- and long-term. FMCG, Health, Telecom and IT sectors receive the least spillovers in all the bands, making them apt for investments during periods of high volatility. Our empirical results are robust to the application of the alternative TVP-VAR time-frequency parameter framework. The portfolio analysis shows that inclusion of stocks in Oil and sectors like Metal and Telecom significantly reduces the portfolio risk. Dynamic connectedness analysis reveals that the spillovers dramatically increase during times of extreme turmoil, especially during the Global Financial Crisis (2008–2009) and the COVID-19 pandemic. Policy implications of our empirical results are also discussed.

With the rise in popularity of Non-Fungible Tokens (NFTs), the demand for NFT coins has also surged. NFT coins are cryptocurrencies that facilitate NFT ecosystems by supporting NFT trading and platform governance. Accurate price predictions of NFT coins are crucial for risk managing volatility and constructing optimal portfolios. This study employs machine learning techniques to predict the daily price direction of four key NFT coins, namely ENJ, MANA, THETA, and XTZ. The machine learning methods employed include three decision tree-based methods (random forests, extremely randomized trees, XGBoost), support vector machine, Lasso and Naïve Bayes. The findings show that random forests, extremely randomized trees, XGBoost, and support vector machine models have accuracy ranging between 80% and 90% for predictions in the 14 to 21 day range. This adds to the literature showing that machine learning methods have high prediction accuracy for cryptocurrency prices. Conversely, Lasso or Naïve Bayes models yield considerably lower prediction accuracy. Feature importance is assessed using Shapley values. The Shapley value feature importance calculated from random forests highlights that, for 14 and 21-day forecasts, four variables – five-year expected inflation, ten-year bond yields, the interest rate spread, and on balance volume – are consistently highly ranked across all NFT coins. Additionally, the MA50, MA200, and WAD also emerge as important features. These results highlight the importance of including macroeconomic variables which capture business cycle conditions and technical analysis indicators that capture investor psychology as features. NFT coin portfolios constructed using trading signals generated from Extra Trees outperforms a buy and hold portfolio. Extra Trees are easy and fast to implement and investors not making use of this information are likely making sub-optimal investment decisions.

Rare earth elements (REEs) are indispensable for producing green technologies and electronics. Demand for REEs in clean energy technologies in 2040 are projected to be three to seven times higher than today and will be critical to the clean technology transition needed to stave off catastrophic climate change. Forecasting rare earth stock prices is critical for making well informed investment decisions concerning this important asset class. Despite the latter, the literature on forecasting rare earth stock prices is scarce. We use machine learning techniques to forecast daily rare earth stock price direction. The analysis reveals that random forests, extremely randomized trees, RNN, and support vector machine have higher prediction accuracy than Lasso or Naïve Bayes. We find that the 10- to 20-day forecasts using random forests, extremely randomized trees, and support vector machine achieve prediction accuracies greater than 85% with some prediction accuracy reaching 90%. Lasso prediction accuracy is higher than Naïve Bayes but never greater than 67%. The MA200, MA50, on balance volume, VIX, and WAD are the most important predictive features of rare earth stock price direction. A switching portfolio that uses trading signals from an Extra Trees model impressively outperforms a buy and hold portfolio. Our results reveal the high prediction accuracy of using machine learning methods in forecasting rare earth stock price direction which should be useful to investors, policy makers and venture capitalists.

This study endeavors to identify the extreme quantile dependence between clean energy stocks and climate-friendly (or green) bonds with GCC stock markets for the period encompassing September 1, 2014 to September 17, 2021. Employing the cross-quantilogram technique, we report higher dependencies between clean energy stocks and the stocks of United Arab Emirates, Qatar, and Saudi Arabia, whereas moderate to lower dependencies exist between clean energy stocks and the stocks of Bahrain, Kuwait, and Oman. Climate-friendly bonds reveal an insignificant correlation with all GCC stocks except the UAE, indicating the diversification benefits of these climate-friendly bonds for GCC stock markets. The recursive cross-quantilogram emphasizes time-varying features where two significant crisis events are spotted as the shale oil crisis and COVID-19 pandemic with a sharp increase in the lower, median, and upper quantiles. Comparing clean energy stocks with climate bonds, clean energy stocks have substantial comovement with GCC stocks while climate bonds have little comovement. Climate friendly bonds are useful for diversifying investments in GCC stocks. Our findings are of particular interest to policymakers, regulators, investors, and portfolio managers who need to understand the relationship between clean energy stocks, green bonds, and GCC stocks.

Outward foreign direct investment in the extractive industry increases the availability of metals and minerals that run the economic engine in the home country. It is unclear, however, whether tax provision by subsidiaries of emerging multinationals in extractive sectors respond to environmental pollution and corruption in the host country. In this paper we examine the tax provision in the host countries by subsidiaries of private sector based emerging multinationals in the extractive resources (metals and mining) sector. The analysis is carried out through a two-step system dynamic panel data GMM estimation, using data from 86 international subsidiaries of 15 Indian multinationals in 31 host countries for the period 2010 to 2019. Tax provisioning is found to be lesser in countries with higher environmental pollution. Tax provision is higher in countries with greater prevalence of corruption. However, the interactive effect suggests that in the presence of environmental pollution the subsidiary tax provisioning is higher in host countries if there is better control of corruption. This indicates that low corruption will offset a decline in tax provision from higher pollution. Furthermore, subsidiaries are found to have lesser tax provisioning when the parent firm has a tax dispute in the home country, implying the role of firm behaviour in shaping tax contribution by subsidiaries. The results are robust to the organization of subsidiaries through offshore financial centres.

Renewable energy is one of the fastest growing segments of energy consumption and wind energy is one of the most widely used sources of renewable energy. There is, however, much less known about the main drivers of wind energy consumption at the country level. This paper uses the logarithmic mean Divisia index (LMDI) method to study the driving factors in wind energy consumption for a group of 17 countries (Australia, Canada, China, Denmark, France, Germany, Greece, India, Ireland, Italy, Japan, Netherlands, Portugal, Spain, Sweden, United Kingdom, and the United States) that are major consumers of wind energy. The renewable energy share component has the largest impact on wind energy consumption. Improvements in energy intensity are the largest driver of reductions in wind energy consumption. Wind energy consumption forecasts for each country in the business as usual (BAU) scenario for the years 2018–2025 show that compound annual growth rates (CAGRs) for wind energy consumption are highest for Canada, Sweden, China, and Germany. Countries that have high shares of renewable energy like Spain, Portugal, and Denmark have low forecast values of CAGRs. Wind energy forecasts are also calculated for a high growth rate scenario and a low growth rate scenario. Future increases in wind energy consumption are going to depend upon the continued increase in renewable energy share which in turn is affected by energy policy designed to promote fuel switching from fossil fuels to renewables.

This paper uses a large unique panel data set of 91 OECD and non-OECD countries and recently developed panel regression estimation techniques to answer the question by how much energy demand changes when income and energy prices display asymmetric effects. Both long run and short run impacts are studied. For the full sample, we find the short run impact of a 1% increase in GDP increases energy consumption by 0.35% while a 1% decrease in GDP decreases energy consumption by 0.68%. These values are similar across different country groupings. GDP decreases have a larger impact on energy consumption than increases in GDP by a factor of approximately 2 to 1. We do not, however, find any evidence of asymmetric long run GDP effects. The result that energy demand falls more proportionally when GDP falls then when GDP rises has implications for energy policy and energy demand forecasting. There is evidence of long run price asymmetry for the OECD countries.

The Australian government has recently launched a National Energy Productivity Plan that calls for a 40% increase in energy productivity (economic output divided by energy use) before 2030. Improving energy productivity would help boost economic competitiveness, reduce energy costs, and reduce carbon dioxide emissions in Australia. Understanding energy productivity dynamics at the state level is essential for the success of this program. This research analyses the convergence path of energy productivity in Australian states and territories. Club convergence analysis applied to data over the period 1990–2015 reveals two converging energy productivity clubs. Initial energy productivity, industry structure, and automobile fuel prices are important determinants of higher energy productivity. Based on Australian state energy productivity forecasts to 2030, New South Wales and Victoria will be the forerunners in maintaining higher energy productivity in 2030. Australia will not achieve a 40% increase in energy productivity before 2030 without significant changes to its fuel mix and industry structure.

Understanding the dynamics of carbon emissions across time and space is important when formulating energy policies to minimise climate changes in the future. If per capita carbon emissions (or carbon intensity) converge over time, then any negotiation of multilateral agreements will be easier than if convergence is absent. It is also possible for club convergence to exist where countries within a club converge but countries between clubs do not converge. We examine the convergence of consumption-based and territory-based carbon emissions intensity across 70 countries. We find two convergent clubs for consumption-based emissions and three convergent clubs for territory-based emissions. Increases in each of total factor productivity, renewable energy consumption and urbanisation increase the odds of belonging to a low carbon emissions intensity club. An increase in industry value added reduces the odds of belonging to a low carbon intensity club. Increases in total factor productivity can be obtained through effective macroeconomic policy, while increasing renewable energy consumption may require structural changes in economies focused on transitioning to a low carbon economy. Under a business as usual forecasting scenario the number of consumption-based carbon emissions intensity clubs increases between 2014 and 2030 making it more difficult to negotiate multilateral agreements on climate change in the future.

The impact that oil-market shocks have on stock prices in oil exporting countries has implications for both domestic and international investors. We derive the shocks driving oil prices from an oil market model that explicitly identifies speculative trading in the crude oil market. We study the nonlinear relationship of oil price shocks with stock market returns in major oil-exporting countries in a multi-factor Markov-switching framework. Flow oil-demand shocks have a statistically significant impact on stock returns in Canada, Norway, Russia, Kuwait, Saudi Arabia, and the UAE. Idiosyncratic oil-market shocks affect stock returns in Norway, Russia, Kuwait, Saudi Arabia and UAE. Speculative (oil-inventory) shocks impact stock returns in Canada, Russia, Kuwait and the UAE. Flow oil-supply shocks matter for the UK, Kuwait, and UAE. Mexico is the only country where stock returns are unaffected by oil-market shocks. A portfolio that uses the Markov-switching probabilities to switch between equities in the low volatility state and T-bills in the high volatility state outperforms a buy and hold strategy for some countries.

We examine the causal relationship between globalization, economic growth and energy consumption for 25 developed economies using both time series and panel data techniques for the period 1970–2014. Due to the presence of cross-sectional dependence in the panel (countries from Asia, North America, Western Europe and Oceania), we employ the cross-sectional augmented IPS test to ascertain unit root properties. The cointegration test results indicate the presence of a long-run association between globalization, economic growth and energy consumption. Long-run heterogeneous panel elasticities are estimated through the common correlated effects mean group estimator and the augmented mean group estimator. The empirical results reveal that, for most countries, globalization increases energy consumption. In the USA and UK, globalization is negatively correlated with energy consumption. The causality analysis indicates the presence of the globalization-driven energy consumption hypothesis. This empirical analysis suggests insightful policy guidelines for policy makers using globalization as an economic tool to utilize energy efficiently for sustainable economic development in the long run.

The Indian government has a number of ambitious economic and energy related initiatives including increasing access to electricity (“24X7 Power for All”), greater economic activity from manufacturing (“Make in India”), and reducing carbon dioxide emissions. Energy productivity is an important factor in helping to achieve these objectives. In this paper, we test the hypothesis of energy productivity convergence in a panel of contiguous states and territories (S&Ts) in India. In measuring energy productivity at the S&T level, we use a unique firm-level dataset maintained by the Centre for Monitoring Indian Economy (CMIE) for the period 1988 to 2016. We identify convergence clubs across Indian S&Ts; i.e. we identify groups of states that converge to different equilibria. The findings from the club merging analysis show that energy productivity across the S&Ts converges into two clubs with one divergent club. Higher initial energy productivity makes it more likely for states to be in the high energy productivity club. Industry structure is also an important determinant. The club convergence of the S&Ts has implications for Indian energy policy.

Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin (“digital gold”). Our approach is to use several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An analysis of the economic value shows that risk-averse investors will be willing to pay a high performance fee to switch from a portfolio with gold to a portfolio with bitcoin. These results are robust to the inclusion of trading costs.

There is a growing movement for both individual investors and large institutions to divest from oil companies, and from fossil fuel producers in general. This paper investigates the implications of doing so, by comparing three portfolios: (1) a portfolio that includes fossil fuel producing companies and utilities, (2) a portfolio that replaces fossil fuel producing companies and utilities with clean energy companies, and (3) a portfolio without fossil fuel producing companies, utilities, or clean energy companies. Using a range of measures, we find that portfolios that divest from fossil fuels and utilities and invest in clean energy perform better than those with fossil fuels and utilities. We also find that risk-averse investors would be willing to pay a fee to make this switch, even when trading costs are included.

Many international organizations have called for an increased usage of renewable energy as a means to reduce CO2 emissions and address climate change. This paper uses a large panel data set of 117 countries and recently developed panel estimation techniques to answer the question by how much does increasing the share of non-fossil fuels in electricity generation reduce the subsequent carbon dioxide emissions. For the full sample, we find long-run displacement elasticities of approximately -0.75, indicating that a one percent increase in non-fossil fuel electricity generation reduces CO2 emissions per capita from electricity generation by about 0.75%. Long-run displacement elasticities for non-OECD (OECD) countries are approximately – 0.90 (-0.60). These results have a number of policy implications.

Using annual data for the period 1971–2012, this study explores the relationship between globalization and energy consumption for India by endogenizing economic growth, financial development and urbanization. The cointegration test proposed by Bayer–Hanck (2013) is applied to estimate the long-run and short-run relationships among the variables. After confirming the existence of cointegration, the overall results from the estimation of an ARDL energy demand function reveal that in the long run, the acceleration of globalization (measured in three dimensions — economic, social and overall globalization) leads to a decline in energy demand in India. Furthermore, while financial development is negatively related to energy consumption, economic growth and urbanization are the key factors leading to increased energy demand in the long run. These results have policy implications for the sustainable development of India. In particular, globalization and financial development provide a win–win situation for India to increase its economic growth in the long run and become more environmentally sustainable.

This paper uses Markov-switching models to investigate the impact of oil shocks on real exchange rates for a sample of oil exporting and oil importing countries. This is an important topic to study because an oil shock can affect a country’s terms of trade which can affect its competitiveness. We detect significant exchange rate appreciation pressures in oil exporting economies after oil demand shocks. We find limited evidence that oil supply shocks affect exchange rates. Global economic demand shocks affect exchange rates in both oil exporting and importing countries, though there is no systematic pattern of appreciating and depreciating real exchange rates. The results lend support to the presence of regime switching for the effects of oil shocks on real exchange rates.

While much research uses multivariate GARCH to model volatility dynamics and risk measures, one particular type of multivariate GARCH model, GO-GARCH, has been underutilized. This paper uses DCC, ADCC and GO-GARCH to model volatilities and conditional correlations between emerging market stock prices, oil prices, VIX, gold prices and bond prices. A rolling window analysis is used to construct out-of-sample one-step-ahead forecasts of dynamic conditional correlations and optimal hedge ratios. In most of the situations we study, oil is the best asset to hedge emerging market stock prices. Hedge ratios from the ADCC model are preferred (most effective) for hedging emerging market stock prices with oil, VIX, or bonds. Hedge ratios estimated from the GO-GARCH are most effective for hedging emerging market stock prices with gold in some instances. These results are reasonably robust to choice of model refits, forecast length and distributional assumptions.

Mortgage rates are one of the important drivers of the housing market. While there is a literature looking at the pass-through effect from Central Bank rates to mortgage rates, there is less known about how useful Central Bank rates are for forecasting mortgage rates. This article uses a selection of models (ARIMA, ARIMAX, BATS, state space error, trend seasonal (ETS), Holt Winter, random walk, simple exponential smoothing (SES), OLS and VAR) to forecast Canadian 5-year conventional mortgage rates. Based on RMSE, regression-based approaches like ARIMAX or OLS that use Central Bank rates to forecast mortgage rates are preferred when it comes to forecasting Canadian mortgage rates 6 or 12 months into the future, respectively.

The theories of ecological modernization and urban environmental transition both recognize that urbanization can have positive and negative impacts on the natural environment with the net effect being hard to determine a priori. This study uses recently developed panel regression techniques that allow for heterogeneous slope coefficients and cross-section dependence to model the impact that urbanization has on CO2 emissions for a panel of emerging economies. The estimated contemporaneous coefficients on the energy intensity and affluence variables are positive, statistically significant and fairly similar across different estimation techniques. By comparison, the estimated contemporaneous coefficient on the urbanization variable is sensitive to the estimation technique. In most specifications, the estimated coefficient on the urbanization variable is positive but statistically insignificant. The implications of these results for sustainable development policy are discussed.

This article investigates the impact of two important socio‐economic variables—urbanization and industrialization—on energy consumption in a panel of emerging economies. The results indicate that income increases energy consumption in both the long run and the short run. In the long run, urbanization decreases energy consumption, while industrialization increases it. Long‐run dynamics are important as evidenced by the estimated coefficient on the error correction term. These results have implications for sustainable development. Economic growth policies designed to increase income and industrialization will increase energy consumption. Since most energy needs in emerging economies are currently met by the burning of fossil fuels, economic growth and industrialization policies will be at odds with sustainable development.

Increased financial integration between countries and the financialization of commodity markets are providing investors with new ways to diversify their investment portfolios. This paper uses VARMA-AGARCH and DCC-AGARCH models to model volatilities and conditional correlations between emerging market stock prices, copper prices, oil prices and wheat prices. The dynamic conditional correlation model is found to fit the data the best and used to generate dynamic conditional correlations, hedge ratios and optimal portfolio weights. Emerging market stock prices and oil prices display leverage effects where negative residuals tend to increase the variance (conditional volatility) more than positive ones. Correlations between these assets increased considerably after 2008, and have yet to return to their pre 2008 values. On average, oil provides the cheapest hedge for emerging market stock prices while copper is the most expensive but given the variability in the hedge ratios, one should probably not put too much emphasis on average hedge ratios.

Socially responsible investing (SRI) is one of the fastest growing areas of investing. While there is a considerable literature comparing SRI to various benchmarks, very little is known about the volatility dynamics of socially responsible investing. In this paper, multivariate GARCH models are used to model volatilities and conditional correlations between a stock price index comprised of socially responsible companies, oil prices, and gold prices. The dynamic conditional correlation model is found to fit the data the best and used to generate dynamic conditional correlations, hedge ratios and optimal portfolio weights. From a risk management perspective, SRI offers very similar results in terms of dynamic conditional correlations, hedge ratios, and optimal portfolio weights as investing in the S&P 500. For example, SRI investors can expect to pay a similar amount to hedge their investment with oil or gold as investors in the S&P 500 would pay. These results can help investors and portfolio managers make more informed investment decisions.

Carbon dioxide emissions represent a new traded asset that, in addition to reducing carbon dioxide emissions through cap-and-trade initiatives, can offer financial risk diversification benefits. In this paper, multivariate generalized auto-regressive conditional heteroscedasticity (GARCH) models are used to model conditional correlations between carbon prices, oil prices, natural gas prices and stock prices. Compared with the diagonal or dynamic conditional correlation model, the constant conditional correlation model is found to fit the data the best and is used to generate hedge ratios and optimal portfolios. Carbon does not appear to be useful for hedging oil or the S&P 500 index but does seem to be useful for hedging natural gas. The average weight for the carbon/natural gas portfolio indicates that for a US$1 portfolio, 29 cents should be invested in carbon and 71 cents invested in natural gas. Hedge ratios and optimal portfolio weights vary considerably over the sample period, indicating that financial positions should be monitored frequently.

Against a backdrop of concerns about climate change, peak oil, and energy security issues, reducing energy intensity is often advocated as a way to at least partially mitigate these impacts. This study uses recently developed heterogeneous panel regression techniques like mean group estimators and common correlated effects estimators to model the impact that income, urbanization and industrialization has on energy intensity for a panel of 76 developing countries. In the long-run, a 1% increase in income reduces energy intensity by − 0.45% to − 0.35%. Long-run industrialization elasticities are in the range 0.07 to 0.12. The impact of urbanization on energy intensity is mixed. In specifications where the estimated coefficient on urbanization is statistically significant, it is slightly larger than unity. The implications of these results for energy policy are discussed.

In this paper, a model of the determinants of environmental management practices and the impact of these practices on environmental performance is described and tested using Canadian manufacturing facility-level data. Our results show that Canadian manufacturing facilities have indeed undertaken environmental initiatives as a result of pressures arising from the buyers of their products and corporate headquarters. The relationship between environmental management practices and environmental performance is curvilinear. Increases in environmental performance are observed as the number of environmental practices increases up to an inflection point. Past this inflection point, environmental performance diminishes with further increases in environmental practices. We also find that, across time, facilities with more comprehensive practices continue to see improvements in environmental performance.

Cleantech venture capital investment differs from the typical venture capital investment in that it tends to be very capital intensive and faces greater technology risks associated with the functioning of the technology, scalability and exit requirements than the typical venture capital investment. Moreover, unlike the typical venture capital investment, the benefits arising from cleantech cannot be totally captured by the venture capitalist as many of its benefits accrue to society via reduced environmental degradation and better health and quality of life outcomes. The public goods literature posits that such externalities reduce investment in cleantech below the socially optimal level. We seek to determine whether there are countervailing factors which may incite greater cleantech investment. We argue that oil prices, increased stakeholder attention, as well as the impact of various formal and informal institutions are such factors. This paper provides a cross-country analysis of the determinants of cleantech venture capital investment with a unique worldwide dataset of 31 countries spanning 1996–2010. The data show consistent evidence of a pronounced role for oil prices in driving cleantech venture capital deals, which is more important than other economic, legal or institutional variables. Cleantech media coverage is likewise a statistically significant determinant of cleantech venture capital investment and as economically significant as other country level legal, governance, and cultural variables. Uncertainty avoidance has a negative impact on cleantech venture capital investment, as well as a moderating effect on other variables.

The renewable energy sector is one of the fastest growing components of the energy industry and along with this increased demand for renewable energy there has been an increase in investing and financing activities. The tradeoff between risk and return in the renewable energy sector is, however, precarious. Renewable energy companies are often among the riskiest types of companies to invest in and for this reason it is necessary to have a good understanding of the risk factors. This paper uses a variable beta model to investigate the determinants of renewable energy company risk. The empirical results show that company sales growth has a negative impact on company risk while oil price increases have a positive impact on company risk. When oil price returns are positive and moderate, increases in sales growth can offset the impact of oil price returns and this leads to lower systematic risk.

In this paper, multivariate GARCH models are used to model conditional correlations and to analyze the volatility spillovers between oil prices and the stock prices of clean energy companies and technology companies. Four different multivariate GARCH models (BEKK, diagonal, constant conditional correlation, and dynamic conditional correlation) are compared and contrasted. The dynamic conditional correlation model is found to fit the data the best and generates results showing that the stock prices of clean energy companies correlate more highly with technology stock prices than with oil prices. On average, a $1 long position in clean energy companies can be hedged for 20 cents with a short position in the crude oil futures market.

While two different streams of literature exist investigating 1) the relationship between oil prices and emerging market stock prices and 2) the relationship between oil prices and exchange rates, relatively little is known about the dynamic relationship between oil prices, exchange rates and emerging market stock prices. This paper proposes and estimates a structural vector autoregression model to investigate the dynamic relationship between these variables. Impulse responses are calculated in two ways (standard and the recently developed projection based methods). The model supports stylized facts. In particular, positive shocks to oil prices tend to depress emerging market stock prices and US dollar exchange rates in the short run. The model also captures stylized facts regarding movements in oil prices. A positive oil production shock lowers oil prices while a positive shock to real economic activity increases oil prices. There is also evidence that increases in emerging market stock prices increase oil prices.

This study uses panel cointegration regression techniques to examine the relationship between energy consumption, output and trade in a sample of 7 South American countries covering the period 1980 to 2007. Panel cointegration tests show a long-run relationship between 1) output, capital, labor, energy, and exports and 2) output, capital, labor, energy, and imports. Short-run dynamics show a bi-directional feedback relationship between energy consumption and exports, output and exports and output and imports. There is evidence of a one way short-run relationship from energy consumption to imports. In the long-run there is evidence of a causal relationship between trade (exports or imports) and energy consumption. These results have implications for energy policy and environmental policy. One important implication of these results is that environmental policies designed to reduce energy use will reduce trade. This puts environmental policy aimed at reducing energy consumption at odds with trade policy.

This study examines the impact of information communication technology (ICT) on electricity consumption in emerging economies. The empirical results, obtained from dynamic panel demand models, show a positive and statistically significant relationship between ICT and electricity consumption when ICT is measured using internet connections, mobile phones or the number of PCs. Long-run ICT elasticities are smaller than income elasticities but because ICT growth rates are so much higher than income growth rates, the impact of ICT on electricity demand is greater than the impact of income on electricity demand. One implication of these results is that policies designed to close the “digital divide” between developed and developing economics by increasing the adoption of ICT in developing countries are put at odds with energy policies to reduce GHG emissions.

The 2008–2009 financial crisis, often referred to as the Great Recession, presented one of the greatest challenges to economies since the Great Depression of the 1930s. Before the financial crisis, and in response to the Kyoto Protocol, many countries were making great strides in increasing energy efficiency, reducing carbon dioxide (CO2) emission intensity and reducing their emissions of CO2. During the financial crisis, CO2 emissions declined in response to a decrease in economic activity. The focus of this research is to study how energy related CO2 emissions and their driving factors after the financial crisis compare to the period before the financial crisis. The logarithmic mean Divisia index (LMDI) method is used to decompose changes in country level CO2 emissions into contributing factors representing carbon intensity, energy intensity, economic activity, and population. The analysis is conducted for a group of 19 major countries (G19) which form the core of the G20. For the G19, as a group, the increase in CO2 emissions post-financial crisis was less than the increase in CO2 emissions pre-financial crisis. China is the only BRICS (Brazil, Russia, India, China, South Africa) country to record changes in CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period that were lower than their respective values in the pre-financial crisis period. Compared to the pre-financial crisis period, Germany, France, and Italy also recorded lower CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period. Germany and Great Britain are the only two countries to record negative changes in CO2 emissions over both periods. Continued improvements in reducing CO2 emissions, carbon intensity and energy intensity are hard to come by, as only four out of nineteen countries were able to achieve this. Most countries are experiencing weak decoupling between CO2 emissions and GDP. Germany and France are the two countries that stand out as leaders among the G19.CO2 emissions