On the Efficiency of Conditional Heteroskedasticity Models

This paper discusses how conditional heteroskedasticity models can be estimated efficiently without imposing strong distributional assumptions such as normality. Using the generalized method of moments (GMM) principle, we show that for a class of models with a symmetric conditional distribution, the GMM estimates obtained from the joint estimating equations corresponding to the conditional mean and variance of the model are efficient when the instruments are chosen optimally. A simple ARCH(1) model is used to illustrate the feasibility of the proposed estimation procedure.     

Beijing, Beijing!

“There is a saying about Beijing, it goes:she is the lover,the mother of everyone.It seems that Beijing pos- sess this magical attraction to everyone coming to her“.Everyone,living in Bei- jing or even spent some moments here, keeps a vivid picture of Beijing in their eyes and hearts.Now,let‘s listen to what they would like to say about this city.     

关于服务过程质量管理的思考

本文剖析了导致服务过程质量问题的因素,提出了目前在服务过程质量管理中存在着两大难点:一个压力点--对“真实瞬间“的管理;一个盲点--对顾客行为的管理,并就实践中如何加强对这“两点“的管理作了深入的探讨.     

Robust estimation of risk-neutral moments

This study provides an in-depth analysis of how to estimate risk-neutral moments robustly. A simulation and an empirical study show that estimating risk-neutral moments presents a trade-off between (a) the bias of estimates caused by a limited strike price domain and (b) the variance of estimates induced by microstructural noise. The best trade-off is offered by option-implied quantile moments estimated from a volatility surface interpolated with a local-linear kernel regression and extrapolated linearly. A similarly good trade-off is achieved by estimating regular central option-implied moments from a volatility surface interpolated with a cubic smoothing spline and flat extrapolation.     

Inequality and growth: What comes from the different inequality measures?

The objective of the paper is to verify if income inequality impedes the growth rates in OECD countries in the period of 1990–2014 and to reveal whether the choice of the income inequality measure determines the sign and the strength of the estimated relationship. We use system GMM to estimate parameters of a dynamic panel growth model. The research indicates that income inequality negatively affects economic growth. We also find evidence that various measures of inequality bring the different scale of consequences for economic growth, with measures that give more weight to the middle part of the distribution being the weakest predictor of GDP growth. Simultaneously, we present the test of weak instruments, which helps to explain these differences.     

On a distribution of Jasso and Kotz

Explicit expressions are derived for the moments and the Gini coefficient of the ring-exponential distribution introduced by Jasso and Kotz ( Statistica Neerlandica , 61 , 2007, 305–328).     

Bayesian inference and prediction in the single server Markovian queue

The use of stochastic models and performance measures for the analysis of real life queuing scenarios are based on the fundamental premise that parameters values are known. This is a rarity since more often than not, parameters are usually unknown and require to be estimated. This paper presents techniques for the same from Bayesian perspective. The queue we intend to deal with is the M/M/1 queuing model. Several closed form expressions on posterior inference and prediction are presented which can be readily implemented using standard spreadsheet tools. Previous work in this direction resulted in non-existence of posterior moments. A way out is suggested. Interval estimates and tests of hypothesis on performance measures are also presented.     

Bayesian Premium Rating with Latent Structure

We propose a fully Bayesian approach to non-life risk premium rating, based on hierarchical models with latent variables for both claim frequency and claim size. Inference is based on the joint posterior distribution and is performed by Markov Chain Monte Carlo. Rather than plug-in point estimates of all unknown parameters, we take into account all sources of uncertainty simultaneously when the model is used to predict claims and estimate risk premiums. Several models are fitted to both a simulated dataset and a small portfolio regarding theft from cars. We show that interaction among latent variables can improve predictions significantly. We also investigate when interaction is not necessary. We compare our results with those obtained under a standard generalized linear model and show through numerical simulation that geographically located and spatially interacting latent variables can successfully compensate for missing covariates. However, when applied to the real portfolio data, the proposed models are not better than standard models due to the lack of spatial structure in the data.     

Asymmetry and downside risk in foreign exchange markets

Abstract

This paper evaluates the double gamma distribution as a means of modelling asymmetry in the conditional distribution of financial data. To do this the model is applied to ten exchange rate series covering mature and emerging market countries. A second contribution of this paper is to highlight the link between the double gamma distribution and the measurement of the second lower partial moment (or semi-variance). The resulting empirical performance of the double gamma model is found to be mixed when compared to a symmetric GARCH-t model. Estimates of conditional downside risk based on the double gamma model are constructed for each series. The results for the Malaysian Riggit, Zimbabwe Dollar and the Korean Won demonstrate the extreme downside volatility experienced by these countries during the emerging markets currency crisis.