Evaluation of Deposit Insurance Fund Adequacy Using Credit Risk Model--An Indian Experience

There are two methods widely used for evaluating the adequacy of Deposit Insurance Fund： （i） Target Reserve Ratio and （ii） Credit Risk Model. Target Reserve Ratio is one of the macro level indicators more often set by Regulatory act on the basis of minimum Deposit Insurance Fund margin safety, Target Reserve Ratio is calculated as the ratio of Deposit Insurance Fund to the value of insured deposits. However, TRR does not take into consideration the level of Deposit Insurance potential liability, the Loss at Given Default （LGD） and the historical trend of default rate prevailing among the insured banks. It does not also consider the present condition of the economy and current scenario of the banking sector. This paper discusses primarily about development of Credit Risk Model for evaluating the Deposit Insurance Fund Adequacy. For this purpose, Econometric Credit Risk Model was developed based on the historical data of bank failures and the associated losses of the last 25 years from 1990-91 to 2014-15. The model assesses various possible factors impacting the Deposit Insurance Fund： Default rate, Deposit growth, Exposures, impact of macro-economic factors like GDP, GDS, Inflation and Interest rate changes, etc. on the Deposit Insurance Fund through econometric modeling. The model evaluates the adequacy of Deposit Insurance Fund under both （i） Normal scenarios where there is no （economic） systemic risk assumed and （ii） Worst case scenario at 1% level of significance using Monte Carlo Simulation. Since the model empirically validates all the critical factors impacting the assets and liabilities associated with Loss at Given Default, the model output can also be used to determine a suitable Target Reserve Ratio and such models are being used in countries like USA, Canada, Hong Kong, and Singapore, etc. （IADI, 2009）. More importantly, the model outputs are quite useful in determining the adequacy of deposit insurance fund which is an effective risk control measure that organization like Deposit Insurance Credit Guarantee Corporation （DICGC） can adopt both under normal economic scenario as well as worst case scenario, ensuring a strong financial safety net for the banking sector in India. The model also assesses the default probability and the Loss at Given Default of different types of banks： commercial banks, rural banks, cooperative banks, foreign banks, etc. A risk based on premium can possibly be determined for each type of banks in India.

Noninherited Factors in Fetal Congenital Heart Diseases Based on Bayesian Network:A Large Multicenter Study

Background:Current studies have confirmed that fetal congenital heart diseases(CHDs)are caused by various factors.However,the quantitative risk of CHD is not clear given the combined effects of multiple factors.Objective:This cross-sectional study aimed to detect associated factors of fetal CHD using a Bayesian network in a large sample and quantitatively analyze relative risk ratios(RRs).Methods:Pregnant women who underwent fetal echocardiography(N=16,086 including 3,312 with CHD fetuses)were analyzed.Twenty-six maternal and fetal factors were obtained.A Bayesian network is constructed based on all variables through structural learning and parameter learning methods to find the environmental factors that directly and indirectly associated with outcome,and the probability of fetal CHD in the two groups is predicted through a junction tree reasoning algorithm,so as to obtain RR for fetal CHD under different exposure factor combinations.Taking into account the effect of gestational week on the accuracy of model prediction,we conducted sensitivity analysis on gestational week groups.Results:The single-factor analysis showed that the RRs for the numbers of births,spontaneous abortions,and parental smoking were 1.50,1.38,and 1.11(P<0.001),respectively.The risk gradually increased with the synergistic effect of ranging from one to more environmental factors above.The risk was higher among subjects with five synergistic factors,including the number of births,upper respiratory tract infection during early pregnancy,anemia,and mental stress as well as a history of spontaneous abortions or parental smoking,than in those with less than 5 factors(RR=2.62 or 2.28,P<0.001).This result was consistent across the participants grouped by GWs.Conclusion:We identified six factors that were directly associated with fetal CHD.A higher number of these factors led to a higher risk of CHD.These findings suggest that it is important to strengthen healthcare and prenatal counseling for women with these factors.

Safety and Efficacy of Digoxin TherapymWhere Are We Now？

The aim of our study was to determine the criteria and key factors for the effectiveness of digoxin therapy. A prospective opened-type study was carried out in conditions of everyday clinical practice. The concentrations of digoxin were quantified from blood samples taken following the achievement of drug steady-state （using AxSYM microparticle enzyme immunoassay-MEIA）. The risk/benefit ratio was evaluated based upon the correlation between measured blood concentrations of the drug and clinical response. Study results （100 decompensated patients） revealed that therapy indication field was correctly covered, showing a higher prevalence in elderly. On average, each examinee had 2 or 3 comorbidities. Applied daily dose of digoxin ranged from 0.053 mg to 0.25 mg. Renal function was assessed by creatinine clearance which is one of the key factors for the accomplishment of optimal digoxin serum concentrations （p 〈 0.05）. Co-administration of seven drugs was complicating factor for the management of rational therapy. 76/100 patients were within referent range （0.8-2.0 ng/mL）, while 13/100 were above the upper limit. Four side effects in total were recorded （nausea, vomiting, confusion）, whereas in only two patients digoxin was excluded from the therapy. Digoxin confirmed the justifiability of its use in contemporary clinical practice.

Much Ado about Almost Nothing:Methods for Dealing with Limited Data

Applied statisticians are often confronted with statistical inference problems dealing with situations in which there appear to be no data,or data of only limited usefulness.For example,when attempting to find a confidence interval for a binomial proportion,the sample may contain no successes.Such a scenario could be encountered when attempting to estimate the incidence of an extremely rare side effect associated with the administration of a newly developed drug.In this article,we use examples for our experiences working with scientific investigators and describe several scenarios in which there appeared to be no useful data,or data of only limited usefulness.We describe the methods we prefer for analyzing the data in these situations and illustrate their application using the actual data from the investigations we participated in.

Risks of temperature extremes over China under 1.5℃ and 2℃ global warming

The Paris Agreement aims to keep global warming to well below 2℃ above pre-industrial levels and to pursue efforts to limit it to 1.5℃,recognizing this will reduce the risks of natural disasters significantly.As changes in the risks of temperature extremes are often associated with changes in the temperature probability distribution,further analysis is still needed to improve understanding of the warm extremes over China.In this study,changes in the occurrence probability of temperature extremes and statistic characteristics of the temperature distribution are investigated using the fifth phase of the Coupled Model Intercomparison Project(CMIP5)multimodel simulations from 1861 to 2100.The risks of the once-in-100-year TXx and TNx events are projected to increase by 14.4 and 31.4 times at 1.5℃ warming.Even,the corresponding risks under 2℃ global warming are 23.3 and 50.6,implying that the once-in-100-year TXx and TNx events are expected to occur about every 5 and 2 years over China,respectively.The Tibetan Plateau,Northwest China and south of the Yangtze River are in greater risks suffering hot extremes(both day and night extremes).Changes in the occurrence probability of warm extremes are generally well explained by the combination of the shifts in location and scale parameters in areas with grown variability,i.e.,the Tibetan Plateau for TXx,south of the Yangtze River for both TXx and TNx.The location(scale)parameter leading the risks of once-in-20-year TXx to increase by more than 5(0.25)and 3(0.75)times under 2℃ warming in the Tibetan Plateau and south of the Yangtze River,respectively.The location parameter is more important for regions with decreased variability e.g.,the Tibetan Plateau for TNx,Northwest China for both TXx and TNx,with risks increase by more than 3,6 and 4 times due to changes in location.

A THREE-PARAMETER FAULT-DETECTION SOFTWARE RELIABILITY MODEL WITH THE UNCERTAINTY OF OPERATING ENVIRONMENTS

As requirements for system quality have increased, the need for high system reliability is also increasing. Soflnvare systems are extremely important, in terms of enhanced reliability and stability, for providing high quality services to customers. However, because of the complexity of software systems, soft-ware development can be time-consuming and expensive. Many statistical models have been developed in the past years to estimate soflnvare reliability. In this paper, we propose a new three-parameter fault-detection software reliability model with the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness-of-fit of the proposed model and several existing non-homogeneous Poisson process （NHPP） models based on three sets of failure data collected from software applications. The results show that the proposed model fits significantly better than other existing NHPP models based on three criteria such as mean squared error （MSE）, predictive ratio risk （PRR）, and predictive power （PP）.

Emission factors of polycyclic aromatic hydrocarbons from domestic coal combustion in China

Domestic coal stove is widely used in China, especially for countryside during heating period of winter, and polycyclic aromatic hydrocarbons （PAHs） are important in flue gas of the stove. By using dilution tunnel system, samples of both gaseous and particulate phases from domestic coal combustion were collected and 18 PAH species were analyzed by GC-MS. The average emission factors of total 18 PAH species was 171.73 mg/kg, ranging from 140.75 to 229.11 mg/kg for bituminous coals, while was 93.98 mg/kg, ranging from 58.48 to 129.47 mg/kg for anthracite coals. PAHs in gaseous phases occupied 95% of the total of PAHs emission of coal combustion. In particulate phase, 3-ring and 4- ring PAHs were the main components, accounting for 80% of the total particulate PAHs. The total toxicity potency evaluated by benzo[a]pyrene-equivalent carcinogenic power, sum of 7 carcinogenic PAH components and 2,3,7,8-tetrachlorodibenzodioxin had a similar tendency. And as a result, the toxic potential of bituminous coal was higher than that of anthracite coal. Efficient emission control should be conducted to reduce PAH emissions in order to protect ecosystem and human health.

Characteristics of particulate-bound polycyclic aromatic hydrocarbons emitted from industrial grade biomass boilers

Polycyclic aromatic hydrocarbons（PAHs） are carcinogenic or mutagenic and are important toxic pollutants in the flue gas of boilers. Two industrial grade biomass boilers were selected to investigate the characteristics of particulate-bound PAHs： one biomass boiler retro-fitted from an oil boiler（BB1） and one specially designed（BB2） biomass boiler. One coal-fired boiler was also selected for comparison. By using a dilution tunnel system, particulate samples from boilers were collected and 10 PAH species were analyzed by gas chromatography–mass spectrometry（GC–MS）. The total emission factors（EFs） of PAHs ranged from 0.0064 to0.0380 mg/kg, with an average of 0.0225 mg/kg, for the biomass boiler emission samples. The total PAH EFs for the tested coal-fired boiler were 1.8 times lower than the average value of the biomass boilers. The PAH diagnostic ratios for wood pellets and straw pellets were similar.The ratio of indeno（1,2,3-cd）pyrene/[indeno（1,2,3-cd）pyrene ＋ benzo（g,h,i）perylene] for the two biomass boilers was lower than those of the reference data for other burning devices, which can probably be used as an indicator to distinguish the emission of biomass boilers from that of industrial coal-fired boilers and residential stoves. The toxic potential of the emission from wood pellet burning was higher than that from straw pellet burning, however both of them were much lower than residential stove exhausts.

Tail asymptotic expansions for L-statistics

We derive higher-order expansions of L-statistics of independent risks X1,..., Xn under conditions on the underlying distribution function F. The new results are applied to derive the asymptotic expansions of ratios of two kinds of risk measures, stop-loss premium and excess return on capital, respectively. Several examples and a Monte Carlo simulation study show the efficiency of our novel asymptotic expansions. Keywords smoothly varying condition, second-order regular variation, tail asymptotics, value-at-risk, con- ditional tail expectation, largest claims reinsurance, ratio of risk measure, excess return on capital