Foreign Direct Investment Relations between Myanmar and ASEAN

Myanmar highly appreciates foreign direct investment (FDI) as a key solution reducing the development gap with leading ASEAN countries. Accordingly, it is welcomed by the government. Myanmar's Foreign Investment Law was enacted in 1988 soon after the adoption of a market-oriented economic system to boost the flow of FDI into the country. Foreign investors positively responded to these measures in the early years and FDI inflow into Myanmar gradually increased during the period from 1989 to 1996. However, after 1997, FDI inflow was dramatically reduced and markedly declined until 2004. In 2005, FDI inflow increased at an unprecedented rate and reached the highest level in the country's history. However, this growth was not sustainable in the subsequent years, as it declined again and turned stagnant at the previous level. In terms of source regions, ASEAN is a major investor in Myanmar, which investment is significantly exceeds the combined investment of other regions of the world. Among top ten countries, Thailand's investment alone is significantly more than combined total investments of the other nine countries. Next to Thailand in terms of investments in Myanmar are Singapore and Malaysia among ASEAN, at second and third places, respectively. The combined total FDI inflows into the power and oil and gas sector represent about 65 percent of the total investment. There are many opportunities for foreign investment in other sectors, which are not, yet exploited. ASEAN countries will certainly be source countries of Myanmar FDI in the future, and Myanmar should expand to other Asian countries like Japan, India, China, Korea, and Hong Kong where its FDI portfolio is concerned. To effectively attract FDI into the country, Myanmar needs to minimize the effect of policy while opening and encouraging other potential sectors of FDI to foreign investors in ASEAN and Asian countries.

Corruption in Kazakhstan and the quality of governance

In Kazakhstan, uncover of numerous corruption scandals involving government officials has become almost a normal feature of life. Behind the high-profile acts of waging a battle against corruption, however, is a serious and systemic phenomenon. The most endemic form of corruption is the various transfers of funds in the state structures and national companies which remain opaque and thus unaccounted for. There are questions about the volumes and spending of revenues earned from natural resources, and there is no independent monitoring and control of the flow of funds in national oil and gas companies. The main actors involved in the shadow economy are state officials and informal pressure groups, who distribute resources among themselves, and accumulate wealth by way of legalising informal incomes or obtaining official business using connections. While important decision making is carried out among the close circles of the elite, formal institutions remain weak and ineffective.

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.