An empirical analysis of structural models of corporate debt pricing

This paper tests empirically the performance of three structural models of corporate bond pricing, namely Merton (1974), Leland (1994) and Fan and Sundaresan (2000). While the first two models overestimate bond prices, the Fan and Sundaresan model reveals an extremely good performance. When considering the prediction of credit spreads, the three models under-estimate market spreads but, again, Fan and Sundaresan has a better performance. We find rating, maturity and asset volatility effects in the prediction power, as the models under-estimate less the spreads of riskier firms and of bonds with better rating quality and longer maturity. Moreover, our results reveal the existence of a new industry effect. Spread errors are systematically related to some bond- and firm-specific variables, as well as term structure variables.structural models, corporate debt valuation, empirical credit spreads

Do banks price their informational monopoly?

Modern corporate finance theory argues that although bank monitoring is beneficial to borrowers, it also allows banks to use the private information they gain through monitoring to "hold-up" borrowers for higher interest rates. In this paper, we seek empirical evidence for this information hold-up cost. Since new information about a firm's credit-worthiness is revealed at the time of its first issue in the public bond market, it follows that after firms undertake their bond IPO, banks with an exploitable information advantage will be forced to adjust their loan interest rates downwards, particularly for firms that are revealed to be safe. Our findings show that firms are able to borrow from banks at lower interest rates after they issue for the first time in the public bond market and that the magnitude of these savings is larger for safer firms. We further find that among safe firms, those that get their first credit rating at the time of their bond IPO benefit from larger interest rate savings than those that already had a credit rating when they entered the bond market. Since more information is revealed at the time of the bond IPO on the former firms and since this information will increase competition from uninformed banks, these findings provide support for the hypothesis that banks price their informational monopoly. Finally, we find that while entering the public bond market may reduce these informational rents, it is costly to firms because they have to pay higher underwriting costs on their IPO bonds. Moreover, IPO bonds are subject to more underpricing than subsequent bonds when they first trade in the secondary bond market.Corporate bonds ; Credit ratings

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (MAFOI) generated by the association between body fat and body mass index.

Background: A fuzzy obesity index (MAFOI) for use as an alternative to bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI. Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (MAFOI) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the MAFOI, an obesity classification with entirely new classes of obesity in the fuzzy context as well as is used for BSI. Results: There is gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity.
Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the MAFOI

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (OBESINDEX) generated by the association between body fat and body mass index

Background: A Fuzzy Obesity Index (OBESINDEX) for use as an alternative in bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI.

Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (OBESINDEX) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the OBESINDEX, an obesity classification with entirely new classes of obesity in the fuzzy context as well is used for BSI.

Results: There is a gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity.

Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the OBESINDEX

Quantifying Equivocation for Finite Blocklength Wiretap Codes

This paper presents a new technique for providing the analysis and comparison of wiretap codes in the small blocklength regime over the binary erasure wiretap channel. A major result is the development of Monte Carlo strategies for quantifying a code's equivocation, which mirrors techniques used to analyze normal error correcting codes. For this paper, we limit our analysis to coset-based wiretap codes, and make several comparisons of different code families at small and medium blocklengths. Our results indicate that there are security advantages to using specific codes when using small to medium blocklengths.Comment: Submitted to ICC 201

Stochastic urban pluvial flood hazard maps based upon a spatial-temporal rainfall generator

It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system and the catchment. To address this, the paper presents an innovative contribution to produce stochastic urban pluvial flood hazard maps. A stochastic rainfall generator for urban-scale applications was employed to generate an ensemble of spatially—and temporally—variable design storms with similar return period. These were used as input to the urban drainage model of a pilot urban catchment (~9 km2) located in London, UK. Stochastic flood hazard maps were generated through a frequency analysis of the flooding generated by the various storm events. The stochastic flood hazard maps obtained show that rainfall spatial-temporal variability is an important factor in the estimation of flood likelihood in urban areas. Moreover, as compared to the flood hazard maps obtained by using a single spatially-uniform storm event, the stochastic maps generated in this study provide a more comprehensive assessment of flood hazard which enables better informed flood risk management decisions

The negative influences of the new brazilian forest code on the conservation of riparian forests

More than one million hectares of riparian forests were degraded or altered in Mato Grosso State (Brazil) up to 2009. The aim of the research is to set a comparative scenario to show differences in the quantification of environmental liabilities in riparian forest areas resulting from the change in native vegetation protection rules due to the transition between Laws 4771/65 and 12651/2012. Data collection took place in a marginal stretch of Vermelho River in Rondonópolis County, Mato Grosso State. The following data set was taken into consideration: aerial images derived from unmanned aerial vehicle, Rapid Eye satellite images and orbital images hosted at Google Earth. The spatial resolution of those images was compared. The aerial photos composed a mosaic that was photo-interpreted to generate land use and occupation classes. The riparian forest areas of a rural property were used as parameter, and their environmental situation was compared in 05 meter and 100 meter strips. Thus, by taking into consideration the current rules, 23,501 m2 of area ceased to be an environmental liability within the riparian forest and became a consolidated rural area. According to the previous Forest Code, in a different scenario, that is, in a set of rural properties, the public authority would receive USD 68,600.00 in fines. The new Brazilian Forestry Code of 2012, which replaces the previous one made in 1965, exempts those responsible for rural property from regenerating previously deforested native vegetation — an obligation established by older Forest Code. We have shown that the new Forest Code has diminished the legal responsibility of the rural owners in relation to the maintenance of forest fragments in their properties

On the Higgs spectra of the 3-3-1 model

The minimal scalar sector of the 3-3-1 model is composed by the SU(3)$_L$ triplet scalars $\eta$, $\rho$, $\chi$ and its potential allows the trilinear term $\frac{f}{\sqrt{2}}\chi \eta \rho$. Since $f$ is an energy scale associated to the explicit violation of Peccei-Quinn global symmetry, it is natural to consider in what energy scale such symmetry is broken and its consequences in the spectrum of scalars of the model. here, We show that $f$ determines the spectrum of scalars of the model. Hence, we develop the scalar sector considering $f$ belonging to four energy regimes, namely $f \ll \langle \eta \rangle_0$, $\langle \rho \rangle_0$; $f= \langle \eta \rangle_0$, $\langle \rho \rangle_0$; $f=\langle \chi \rangle_0$ and $f \gg \langle \chi \rangle_0$ and obtain the spectrum of scalars for each case. In the first and second cases the spectrum of scalars presents a set of new scalars belonging to the electroweak scale, while in the third case all new scalars belong to the 3-3-1 scale and the fourth case all the new scalars have masses lying at $f$ scale. All cases have a neutral CP-even scalar mimicking the standard Higgs

Quantiles for Fractions and Other Mixed Data

This paper studies the estimation of quantile regression for fractional data, focusing on the case where there are mass-points at zero or/and one. More generally, we propose a simple strategy for the estimation of the conditional quantiles of data from mixed distributions, which combines standard results on the estimation of censored and Box-Cox quantile regressions. The implementation of the proposed method is illustrated using a well-known dataset