780 resultados para County finance and audits


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Nonlocal investors purchase and sell investment property in a distant metropolitan area. In this study, we identify capital value underperformance for nonlocal investors on both sides of the transaction, when they purchase and when they sell. The commercial real estate transactions data include a national sample of office property occurring in more than 100 U.S. markets. Using propensity-score matched sample to control for selection bias, we find that nonlocal investors overpay on the purchase by an estimated 13.8 % and sell at an estimated 7 % discount. These disadvantages relative to local investors expand with the geographic distance separating investor and asset. Nonlocal investors fundamentally overvalue similar assets sold to each other relative to assets transacted between locals, and are less patient as sellers. The positive bias in overpayment is directly tied to office rent differentials between the asset and investor markets.

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In 2007 futures contracts were introduced based upon the listed real estate market in Europe. Following their launch they have received increasing attention from property investors, however, few studies have considered the impact their introduction has had. This study considers two key elements. Firstly, a traditional Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, the approach of Bessembinder & Seguin (1992) and the Gray’s (1996) Markov-switching-GARCH model are used to examine the impact of futures trading on the European real estate securities market. The results show that futures trading did not destabilize the underlying listed market. Importantly, the results also reveal that the introduction of a futures market has improved the speed and quality of information flowing to the spot market. Secondly, we assess the hedging effectiveness of the contracts using two alternative strategies (naïve and Ordinary Least Squares models). The empirical results also show that the contracts are effective hedging instruments, leading to a reduction in risk of 64 %.

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This article proposes an auction model where two firms compete for obtaining the license for a public project and an auctioneer acting as a public official representing the political power, decides the winner of the contest. Players as firms face a social dilemma in the sense that the higher is the bribe offered, the higher would be the willingness of a pure monetary maximizer public official to give her the license. However, it implies inducing a cost of reducing all players’ payoffs as far as our model includes an endogenous externality, which depends on bribe. All players’ payoffs decrease with the bribe (and increase with higher quality). We find that the presence of bribe aversion in either the officials’ or the firms’ utility function shifts equilibrium towards more pro-social behavior. When the quality and bribe-bid strategy space is discrete, multiple equilibria emerge including more pro-social bids than would be predicted under a continuous strategy space.

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Using a variation of the Nelson-Siegel term structure model we examine the sensitivity of real estate securities in six key global markets to unexpected changes in the level, slop and curvature of the yield curve. Our results confirm the time-sensitive nature of the exposure and sensitivity to interest rates and highlight the importance of considering the entire term structure of interest rates. One issue that is of particular of interest is that despite the 2007-9 financial crisis the importance of unanticipated interest rate risk weakens post 2003. Although the analysis does examine a range of markets the empirical analysis is unable to provide definitive evidence as to whether REIT and property-company markets display heightened or reduced exposure.

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Over the past decade, cooperation between China and Kazakhstan in the oil and gas sector has developed significantly. For China, security of its energy supply is a key strategic objective. This paper analyzes the evolution of Sino-Kazakh oil and gas relations, assesses their long-term prospects, and explores how Chinese demand for oil and gas could divert Kazakhstan’s hydrocarbon resources from other energy markets. The netback approach has been used to assess the prices that China will need to offer other producers in Kazakhstan. Sino-Kazakh energy and economic cooperation could create a good basis for free economic zones and development of beneficial ties for both countries.

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We examine the empirical impact of trade openness on the short-run underpricing of initial public offerings (IPOs) using city-level real estate data. This paper represents a first attempt to employ a macroeconomic approach to explain IPO performance. We investigate an openness effect in which urban economic openness (UEO) has a significant impact on the productivity and on the prices of both direct and indirect real estate due to productivity gains of companies in more open areas. This in turn positively affects the firm’s profitability, enhancing the confidence in the local real estate market and the future company performance and decreasing the uncertainty of the IPO valuation. And as a result, we find that issuers have less incentive to underprice the IPO shares. China provides a suitable experimental ground to study the immense underpricing in developing markets, which cannot solely be accounted for by firm specific effects. First, Chinese real estate companies show strong geographic patterns focusing their businesses locally – usually at a city level. Second, we observe a degree of openness which is significantly heterogeneous across Chinese cities. Controlling for company-specific variables, location and state ownership, we find the evidence that companies whose businesses are in economically more open areas experience less IPO underpricing. Our results show high explanatory power and are robust to diverse specifications.

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Stock market wealth effects on the level of consumption in the United States economy have been constantly debated; there is evidence for arguments for and against its prominence and its symmetry. This paper seeks to investigate the strength of its negative effect by creating models to analyze unexpected shocks to the Standard and Poor's 500 index. First, a transmission mechanism between the stock market and GDP is established through the use of second-order vector autoregressive models. Following which, theory from the life cycle model and adaptations of previous researchers' models are used to create a structural model. This paper finds that stock market wealth effects are small, but important to consider, especially if markets are overpriced; this claim is corroborated by evidence from simulation of 'alternative scenarios' and the historical experiences of 1987 and 2001.

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The acronym BRICS was a fad among the media and global investors. Now, the acronym sounds passé. However, the group of countries remains important, from both political and economic reasons. They have a large aggregate size, 28% of the global GDP and 42% of the world’s population, high growth potential due to the current significant misallocation of resources and relatively low stock of human capital, structural transformation is in progress and one of them, China, is taking steps to become a global power and a challenger to the US dominance. This paper provides a brief overview of the five economies, Brazil, Russia, India, China and South Africa. We focus on some aspects of their history, the Chinese initiatives in international finance and geopolitical strategic moves, their growth experience and structural transformation over the last 35 years, trade and investment integration into the global economy and among themselves, the growth challenges faced by their economies and the potential gains to the Brazilian economy from a stronger integration with the other BRICS. In association with its efforts to be a global power, China aims to become a major player in global finance and to achieve the status of global currency for the renminbi, which would be the first currency of an emerging economy to attain such position. Despite the similarities, the BRICS encompass very diverse economies. In the recent decades, China and India showed stellar growth rates. On the other hand, Brazil, Russia and South Africa have expanded just in line with global output growth with the Russian economy exhibiting high volatility. China is by far the largest economy, and South Africa the smallest, the only BRICS economy with a GDP lower than US$ 1 trillion. Russia abandoned communism almost 25 years ago, but reversed many of the privatizations of 90’s. China is still ruled by communism, but has a vibrant private sector and recently has officially declared market forces to play a dominant role in its economy. Brazil, Russia and South Africa are global natural resources powerhouses and commodity exporters while China and India are large commodity importers. Brazil is relatively closed to international trade of goods and services, in marked contrast to the other four economies. Brazil, India and South Africa are dependent on external capital flows whereas China and Russia are capital exporters. India and South Africa have younger populations and a large portion living below the poverty line. Despite its extraordinary growth experience that lifted many millions from poverty, China still has 28% of its population classified as poor. Russia and China have much older populations and one of their challenges is to deal with the effects of a declining labor force in the near future. India, China and South Africa face a long way to urbanization, while Brazil and Russia are already urbanized countries. China is an industrial economy but its primary sector still absorbs a large pool of workers. India is not, but the primary sector employs also a large share of the labor force. China’s aggregate demand structure is biased towards investment that has been driving its expansion. Brazil and South Africa have an aggregate demand structure similar to the developed economies, with private consumption accounting for approximately 70%. The same similarity applies to the supply side, as in both economies the share of services nears 70%. The development problem is a productivity problem, so microeconomic reforms are badly needed to foster long-term growth of the BRICS economies since they have lost steam due a variety of factors, but fundamentally due to slower total factor productivity growth. China and India are implementing ambitious reform programs, while Brazil is dealing with macroeconomic disequilibria. Russia and South Africa remain mute about structural reforms. There are some potential benefits to Brazil to be extracted from a greater economic integration with the BRICS, particularly in natural resources intensive industries and services. Necessary conditions to the materialization of those gains are the removal of the several sources of resource misallocation and strong investment in human capital.

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The Rest will be able to catch up and grow faster than the West only if it goes against a “received truth”, namely that capital-rich countries should transfer their capital to capital-poor countries. This intuitive truth is the mantra that the West cites to justify its occupation of the markets of developing countries with its finance and its multinationals. Classical Developmentalism successfully criticized the unequal exchange involved in trade liberalization, but it didn’t succeed in criticizing foreign finance. This task has been recently achieved by New Developmentalism and its developmental macroeconomics, which shows that countries will invest and grow more if they don’t run current account deficits, even when these deficits are financed by foreign direct investment

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In this dissertation, different ways of combining neural predictive models or neural-based forecasts are discussed. The proposed approaches consider mostly Gaussian radial basis function networks, which can be efficiently identified and estimated through recursive/adaptive methods. Two different ways of combining are explored to get a final estimate – model mixing and model synthesis –, with the aim of obtaining improvements both in terms of efficiency and effectiveness. In the context of model mixing, the usual framework for linearly combining estimates from different models is extended, to deal with the case where the forecast errors from those models are correlated. In the context of model synthesis, and to address the problems raised by heavily nonstationary time series, we propose hybrid dynamic models for more advanced time series forecasting, composed of a dynamic trend regressive model (or, even, a dynamic harmonic regressive model), and a Gaussian radial basis function network. Additionally, using the model mixing procedure, two approaches for decision-making from forecasting models are discussed and compared: either inferring decisions from combined predictive estimates, or combining prescriptive solutions derived from different forecasting models. Finally, the application of some of the models and methods proposed previously is illustrated with two case studies, based on time series from finance and from tourism.

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Includes bibliography

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Includes bibliography