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Practical implications— The vast majority of these firms see adopting environmental sustainability practices as being profitable and think this will be even more important in the future. In Mexico, one might expect that the same would be true, but only anecdotal evidence was heretofore available.
We derive optimal consumption and portfolio policies that are robust to uncertainty about the hard-to-estimate drift rate, jump intensity and jump size parameters. We also provide a semi-closed form formula for the detection-error probability and compare various portfolio holding strategies, including robust and non-robust policies.
Our quantitative analysis shows that ignoring uncertainty leads to significant wealth loss for the investor. We exploit the manifold increase in homicides in —11 in Mexico resulting from its war on organized drug traffickers to estimate the effect of drug-related homicides on housing prices. We use an unusually rich data set that provides national coverage of housing prices and homicides and exploits within-municipality variations.
We find that the impact of violence on housing prices is borne entirely by the poor sectors of the population.
Publicaciones de la facultad
An increase in homicides equivalent to 1 standard deviation leads to a 3 percent decrease in the price of low-income housing. This paper examines foreign direct investment FDI in the Hungarian economy in the period of post-Communist transition since Hungary took a quite aggressive approach in welcoming foreign investment during this period and as a result had the highest per capita FDI in the region as of We discuss the impact of FDI in terms of strategic intent, i.
The effect of these two kinds of FDI is contrasted by examining the impact of resource seeking FDI in manufacturing sectors and market serving FDI in service industries. In the case of transition economies, we argue that due to the strategic intent, resource seeking FDI can imply a short-term impact on economic development whereas market serving FDI strategically implies a long-term presence with increased benefits for the economic development of a transition economy.
Our focus is that of market serving FDI in the Hungarian banking sector, which has brought improved service and products to multinational and Hungarian firms. This has been accompanied by the introduction of innovative financial products to the Hungarian consumer, in particular consumer credit including mortgage financing. However, the latter remains an underserved segment with much growth potential. For public policy in Hungary and other transition economies, we conclude that policymakers should consider the strategic intent of FDI in order to maximize its benefits in their economies.
We propose a general framework for extracting rotation invariant features from images for the tasks of image analysis and classification. Our framework is inspired in the form of the Zernike set of orthogonal functions. It provides a way to use a set of one-dimensional functions to form an orthogonal set over the unit disk by non-linearly scaling its domain, and then associating it an exponential term.
When the images are projected into the subspace created with the proposed framework, the rotations in the image affect only the exponential term while the value of the orthogonal functions serve as rotation invariant features. We exemplify our framework using the Haar wavelet functions to extract features from several thousand images of symbols.
We then use the features in an OCR experiment to demonstrate the robustness of the method. In this paper we explore the use of orthogonal functions as generators of representative, compact descriptors of image content.
In Image Analysis and Pattern Recognition such descriptors are referred to as image features, and there are some useful properties they should possess such as rotation invariance and the capacity to identify different instances of one class of images. We exemplify our algorithmic methodology using the family of Daubechies wavelets, since they form an orthogonal function set. We benchmark the quality of the image features generated by doing a comparative OCR experiment with three different sets of image features.
Our algorithm can use a wide variety of orthogonal functions to generate rotation invariant features, thus providing the flexibility to identify sets of image features that are best suited for the recognition of different classes of images. When analyzing catastrophic risk, traditional measures for evaluating risk, such as the probable maximum loss PML , value at risk VaR , tail-VaR, and others, can become practically impossible to obtain analytically in certain types of insurance, such as earthquake, and certain types of reinsurance arrangements, specially non-proportional with reinstatements.
Given the available information, it can be very difficult for an insurer to measure its risk exposure. This effect can be assessed mathematically. The PML is defined in terms of a very extreme quantile. The resulting reinsurance structures will then be very complicated to analyze and to evaluate their mitigation or transfer effects analytically, so it may be necessary to use alternative approaches, such as Monte Carlo simulation methods. This is what we do in this paper in order to measure the effect of a complex reinsurance treaty on the risk profile of an insurance company.
We compute the pure risk premium, PML as well as a host of results: impact on the insured portfolio, risk transfer effect of reinsurance programs, proportion of times reinsurance is exhausted, percentage of years it was necessary to use the contractual reinstatements, etc.