Credit risk,managerial behaviour and macroeconomic equilibrium within dual banking systems: Interest-free vs. interest-based banking industries

In this paper, an attempt has been made to explore the determinants of credit risk in the banking system with a particular interest toward the Islamic banking industry. We analyze the link between credit risk and a set of bank-specific and macroeconomic along with institutional variables using two complementary approaches. First, we investigate the factors of credit risk using one-step generalized method of moments (GMM) system estimator. Then, we explore the feedback between credit risk and its determinants in a panel vector autoregressive (PVAR) model. We have used a sample of Middle Eastern, North African (MENA) and Asian countries to apply our model. The major purpose of this paper is to find factors that could explain credit risk within the interest-free banking system relative to the interest-based one.     

Can Warren Buffett forecast equity market corrections?

Warren Buffett suggested that the ratio of the market value of all publicly traded stocks to the Gross National Product could identify potential overvaluations and undervaluations in the US equity market when this ratio deviates above 120% or below 80%. We investigate whether this ratio is a statistically significant predictor of equity market corrections and rallies. We find that Buffett's decision rule does not deliver satisfactory forecasts. However, when we adopt a time-varying decision rule, the ratio becomes a statistically significant predictor of equity market corrections. The two time-varying decision rules are: (i) predict an equity market correction when the ratio exceeds a 95% one-tail confidence interval based on a normal distribution, and (ii) predict an equity market correction when the ratio exceeds a threshold computed using Cantelli's inequality. These new decision rules are robust to changes in the two key parameters: the confidence level and the forecasting horizon. This paper also shows that the MV/GNP ratio performs relatively well against the four most popular equity market correction models, but the ratio is not a particularly useful predictor of equity market rallies.     

Kernel-type estimators for the distortion risk premiums of heavy-tailed distributions

A new kernel-type estimator for the distortion risk premiums of heavy-tailed losses is introduced. Using a least-squares approach, a bias-reduced version of this estimator is proposed. Under suitable assumptions, the asymptotic normality of the given estimators is established. A small simulation study, to illustrate the performance of our method, is carried out.     

Estimating the Gerber–Shiu function by Fourier–Sinc series expansion

In this paper, we consider the nonparametric estimation of the Gerber–Shiu function in a compound Poisson risk model perturbed by diffusion. We present a more efficient estimator based on Fourier–Sinc series expansion. Our estimator is easily computed and has a faster convergence rate. Some simulation examples are provided to show that the estimator performs well when the sample size is finite.     

Statistical Methodologies for the Market Risk Measurement

This paper classifies statistical methodologies available for the marketrisk measurement. With the help of the weighted likelihood, a broad class ofnon-normal distributions, which are not generally considered so far, areapplied to possibly hetero-scedastic financial variables. The approach is compared with popular procedures such as GARCH and J. P. Morgan's using daily dataof 12 financial variables.     

购销广告中的数学学问

本文通过对需求市场的分析，得出人们的潜在购买量随广告费的增加而增大。但实际需求量是随机的。为使问题简化，并做出定量分析，本文假定实际需求量服从均匀分布，并用数学方法确定广告费和购进量的最优值。     

基于单元星形光纤网的最大总光程及其求解方法

研究了单元星形光纤网中,最多光路数目与最大允许光链路损耗间的关系,推出了在给定最大允许光链路损耗、不同光路数目时各路总光程的求解方法,进而得到最佳光路数目与最大总光程,最终得出六条结论。     

Volatility Estimation with Price Quanta

Volatility estimators based on high, low, opening and closing prices have been developed, and perform well on simulated data, but on real data they frequently give lower values for volatility than the simple open–close estimator. This may be due to the fact that for real data, the maximum (or minimum) price is often at the beginning or end of the day. While this could not happen if the observed process was log Brownian, it could happen if the observed process were log Brownian, but observed only to the nearest penny. We develop the theory of such approximations to derive the corrected versions of the basic estimators.     

Poisson Regression for Clustered Data

We compare five methods for parameter estimation of a Poisson regression model for clustered data: (1) ordinary (naive) Poisson regression (OP), which ignores intracluster correlation, (2) Poisson regression with fixed cluster‐specific intercepts (FI), (3) a generalized estimating equations (GEE) approach with an equi‐correlation matrix, (4) an exact generalized estimating equations (EGEE) approach with an exact covariance matrix, and (5) maximum likelihood (ML). Special attention is given to the simplest case of the Poisson regression with a cluster‐specific intercept random when the asymptotic covariance matrix is obtained in closed form. We prove that methods 1–5, except GEE, produce the same estimates of slope coefficients for balanced data (an equal number of observations in each cluster and the same vectors of covariates). All five methods lead to consistent estimates of slopes but have different efficiency for unbalanced data design. It is shown that the FI approach can be derived as a limiting case of maximum likelihood when the cluster variance increases to infinity. Exact asymptotic covariance matrices are derived for each method. In terms of asymptotic efficiency, the methods split into two groups: OP & GEE and EGEE & FI & ML. Thus, contrary to the existing practice, there is no advantage in using GEE because it is substantially outperformed by EGEE and FI. In particular, EGEE does not require integration and is easy to compute with the asymptotic variances of the slope estimates close to those of the ML.     

A fad or the future? Examining the effectiveness of virtual reality advertising in the hotel industry

New virtual reality technology presents hotels with wide-ranging marketing, training, and customer service opportunities. The purpose of this study was to explore both the immediate and delayed effects of hotel VR commercials as compared to traditional video commercials. Based on the perceptual load theory and elaboration likelihood model, this study designed and conducted a 2 × 2 laboratory experiment with two stages of data collection. The study demonstrated that VR commercials produced better immediate effects than the traditional commercials, especially for participants with high elaboration likelihood levels. However, the findings also revealed significant decreases in purchase intention among participants watching VR commercials and a significant improvement in attitudes among participants watching traditional commercials. Both theoretical and practical implications were discussed to leverage the power of VR in marketing.