Multifractality and long-range dependence of asset returns: the scaling behavior of the Markov-switching multifractal model with lognormal volatility components

In this paper, we consider daily financial data from various sources (stock market indices, foreign exchange rates and bonds) and analyze their multiscaling properties by estimating the parameters of a Markov-switching multifractal (MSM) model with Lognormal volatility components. In order to see how well estimated models capture the temporal dependency of the empirical data, we estimate and compare (generalized) Hurst exponents for both empirical data and simulated MSM models. In general, the Lognormal MSM models generate "apparent" long memory in good agreement with empirical scaling provided that one uses sufficiently many volatility components. In comparison with a Binomial MSM specification [11], results are almost identical. This suggests that a parsimonious discrete specification is flexible enough and the gain from adopting the continuous Lognormal distribution is very limited.

True and apparent scaling : the proximity of the Markov-switching multifractal model to long-range dependence

In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov-switching multifractal (MSM) model. In order to see how well the estimated model captures the temporal dependence of the data, we estimate and compare the scaling exponents H(q) (for q=1,2) for both empirical data and simulated data of the MSM model. In most cases the multifractal model appears to generate ‘apparent’ long memory in agreement with the empirical scaling laws.

A comparative analysis of accounting-based valuation models

We empirically compare the reliability of the dividend (DIV) model, the residual income valuation (CT, GLS) model, and the abnormal earnings growth (OJ) model. We find that valuation estimates from the OJ model are generally more reliable than those from the other three models, because the residual income valuation model anchored by book value gets off to a poor start when compared with the OJ model led by capitalized next-year earnings. We adopt a 34-year sample covering from 1985 to 2013 to compare the reliability of valuation estimates via their means of absolute pricing errors (MAPE) and corresponding t statistics. We further use the switching regression of Barrios and Blanco to show that the average probability of OJ valuation estimates is greater in explaining stock prices than the DIV, CT, and GLS models. In addition, our finding that the OJ model yields more reliable estimates is robust to analysts-based and model-based earnings measures.

Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south-eastern Australia

The pathogen Phytophthora cinnamomi causes extensive 'dieback' of Australian native vegetation. This study investigated the distribution of infection in an area of significant sclerophyll vegetation in Australia. It aimed to determine the relationship of infection to site variables and to develop a predictive model of infection. Site variables recorded at 50 study sites included aspect, slope, altitude, proximity to road and road characteristics, soil profile characteristics and vegetation attributes. Soil and plant tissues were assayed for the presence of the pathogen. A geographical information systyem (GIS) was employed to provide accurate estimations of spatial variables and develop a predictive model for the distribution of P. cinnamomi. The pathogen was isolated from 76% of the study sites. Of the 17 site variables initially investigated during the study a logistic regression model identified only two, elevation and sun-index, as significant in determining the probability of infection. The presence of P. cinnamomi infection was negatively associated with elevation and positively associated with sun-index. The model predicted that up to 74% of the study area (11 875 ha) had a high probability of being affected by P. cinnamomi. However, the present areas of infection were small, providing an opportunity for management to minimize spread into highly susceptible uninvaded areas.

Managerial ownership concentration and agency conflict using logistic regression approach : evidence from Bursa Malaysia

This paper adopted logistic regression model to examine the relationship between level of managerial ownership concentration and agency conflict which are proxied by level of risk, firms leverage and firms dividend policy. The study covers a period of 5 years from 1997 through 2001. The study is based on the 100 blue-chip stocks, majority of which are derived from CI components. The findings suggest a positive and significant association between level of level of risk at lower level and managerial ownership while a negative and significant association is also evidenced between risk at higher level and managerial ownership concentration. While debt policy which serves as positive monitoring substitute for agency conflict is found to be positive and significant explaining the level of ownership concentration. Furthermore, dividend policies, which also serve as monitoring, substitute to reduce agency conflict between manager and external shareholders do not appear to have any significant impact on managerial ownership. On the other hand, the level of institutional ownership, which serves as external monitoring force, is found to have inverse impact on level of managerial ownership concentration. This is marginally significant at 10 level (p=.12). The findings, in part explain the argument that the managerial ownership help reduce agency conflict between outside equity holders and managers.

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths.

The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Fusion of GRNN and FA for online noisy data regression

A new online neural-network-based regression model for noisy data is proposed in this paper. It is a hybrid system combining the Fuzzy ART (FA) and General Regression Neural Network (GRNN) models. Both the FA and GRNN models are fast incremental learning systems. The proposed hybrid model, denoted as GRNNFA-online, retains the online learning properties of both models. The kernel centers of the GRNN are obtained by compressing the training samples using the FA model. The width of each kernel is then estimated by the K-nearest-neighbors (kNN) method. A heuristic is proposed to tune the value of Kof the kNN dynamically based on the concept of gradient-descent. The performance of the GRNNFA-online model was evaluated using two benchmark datasets, i.e., OZONE and Friedman#1. The experimental results demonstrated the convergence of the prediction errors. Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.

Non-homogeneous volatility correlations in the bivariate multifractal model

In this paper, we consider an extension of the recently proposed bivariate Markov-switching multifractal model of Calvet, Fisher, and Thompson [2006. "Volatility Comovement: A Multifrequency Approach." Journal of Econometrics {131}: 179-215]. In particular, we allow correlations between volatility components to be non-homogeneous with two different parameters governing the volatility correlations at high and low frequencies. Specification tests confirm the added explanatory value of this specification. In order to explore its practical performance, we apply the model for computing value-at-risk statistics for different classes of financial assets and compare the results with the baseline, homogeneous bivariate multifractal model and the bivariate DCC-GARCH of Engle [2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models." Journal of Business & Economic Statistics 20 (3): 339-350]. As it turns out, the multifractal model with heterogeneous volatility correlations provides more reliable results than both the homogeneous benchmark and the DCC-GARCH model. © 2014 Taylor & Francis.

The value of retail rents with regression models: a case study of Shanghai

Abstract
Purpose– The purpose of this paper is to estimate the determinants of the retail space rent in Shanghai.
Design/methodology/approach – Hedonic model and spatial regression models are used in the paper. The problem of spatial autocorrelation is tested by Moran’s I statistics, and the root mean square error (RMSE) test is performed to find out the best model.
Findings – The significant explaining variables are the age, the area of retail space, the distance to the Jing An CBD centre, the type of the retail and the district of the property. A new classification of district in retail research context is suggested in this paper, and it is proved to be better than the districts set up by government to explain the retail rent variation.
Originality/value – This paper presents the first empirical study about the retail rental market in Shanghai. The research helps retail property investors and retail tenants deepen their understanding of the retail market in Shanghai. Spatial econometrics techniques are first introduced into the empirical retail rent research to produce a more precise estimation.

The impact of computer-assisted learning on academic grades: an assessment of students' perceptions

This study examines student perceptions of the usefulness of Computer-Assisted Learning (CAL) packages in learning accounting concepts in terms of the influence on academic performance. Various additional factors  affecting academic performance [such as gender, prior studies of  accounting, and computer systems, together with entry background] are incorporated in the development of a multiple regression model, together with perceptions of CAL. The study uses a sample of 280 second-year undergraduate accounting students from an Australian university to test the model. In contrast to prior studies (e.g. Lane and Porch, 2002, Accounting Education: an international journal, 11(3), pp. 217-233), this study showed that positive perceptions of the usefulness of CAL significantly influenced performance. Additionally, it was found that international students, many of whom enter university at the second year level having obtained advanced standing credits, had significantly poorer performance than local students. The findings show that gender, prior studies of accounting and computing systems were not significant influences on academic performance. Overall, the results have implications for accounting educators utilising CAL in courses as a means of improving students' understanding of accounting concepts and academic performance.

Influences on undergraduate students` intentions to become qualified accountants : evidence from Australia

In this study, first year commerce students in Australia were surveyed about their perceptions of their accounting studies and their perceptions of the attributes required of professional accountants. The paper specifically addresses the factors important in determining whether first year students intend to become accountants. The study uses a logistic regression model incorporating demographic and academic factors, as well as students' perceptions of the work of accountants, to predict intention to become an accountant. The results show that the perception of importance of generic skills, intrinsic interest in the discipline area, and course satisfaction were significant in determining intention to pursue a career as an accountant. As many students formed their judgments about the work of accountants from their accounting studies, the findings have implications for accounting educators in terms of the enthusiasm and motivation required in teaching accounting, as well as curriculum development that reflects the skill set required for an increasingly sophisticated business environment.

Estimating the effects of energy imbalance on changes in body weight in children

BACKGROUND: Estimating changes in weight from changes in energy balance is important for predicting the effect of obesity prevention interventions. OBJECTIVE: The objective was to develop and validate an equation for predicting the mean weight of a population of children in response to a change in total energy intake (TEI) or total energy expenditure (TEE). DESIGN: In 963 children with a mean (+/-SD) age of 8.1 +/- 2.8 y (range: 4-18 y) and weight of 31.5 +/- 17.6 kg, TEE was measured by using doubly labeled water. Log weight (dependent variable) and log TEE (independent variable) were analyzed in a linear regression model with height, age, and sex as covariates. It was assumed that points of dynamic balance, called "settling points," occur for populations wherein energy is in balance (TEE = TEI), weight is stable (ignoring growth), and energy flux (EnFlux) equals TEE. RESULTS: TEE (or EnFlux) explained 74% of the variance in weight. The unstandardized regression coefficient was 0.45 (95% CI: 0.38, 0.51; R(2) = 0.86) after including covariates. Conversion into proportional changes (time(1) to time(2)) gave the equation (weight(2)/weight(1)) = (EnFlux(2)/EnFlux(1))(0.45). In 3 longitudinal studies (n = 212; mean follow-up of 3.4 y), the equation predicted the mean follow-up measured weight to within 0.5%. CONCLUSIONS: The relation of EnFlux with weight was positive, which implied that a high TEI (rather than low physical activity and low TEE) was the main determinant of high body weight. Two populations of children with a 10% difference in mean EnFlux would have a 4.5% difference in mean weight.

Moving a unit online: a quantitative evaluation of student repsonses

In 2005, a unit was converted to ‘wholly online’ delivery mode, where all teaching occurred online. Student evaluation survey data for 2005 suggested that students rated many aspects of the wholly online unit delivery significantly lower than previously. For 2006, ten percent of the unit marks were dedicated to an assessed assignment activity based around an online discussion area. Based on student evaluation items common to the preand post-2006 period, overall student satisfaction with the unit returned to the same levels as prior to the introduction of wholly online delivery. These findings suggest that careful thought, but not necessarily major changes, may be required to avoid student disillusionment and maximise student learning outcomes when moving an existing unit to wholly online delivery. During 2005 and 2006, the same unit was included in a large survey to gauge students’ perceptions of studying wholly online units. The sub-set of respondents relating to this unit was found to have a good demographic match to the total unit enrolment. The survey included the following question item, ‘39: How satisfied have you been with this unit being offered wholly online?’, as an overall measure of student satisfaction with studying the unit in wholly online mode. Multivariate linear regression analysis was conducted with survey item 39 as the dependent variable. While the resultant regression model should not be interpreted literally as a formula for student satisfaction, it does suggest some areas for action to improve student satisfaction with studying this unit in wholly online mode.

VNTR polymorphism of the insulin gene and childhood overweight in a general population

Objective: The VNTR polymorphism 5' of the insulin gene has been related to obesity in a previous study on children with early onset of severe obesity. Our purpose was to analyze the association between this polymorphism and adiposity variability in an unselected population of children and adolescents in northern France.

Research Methods and Procedures: In 293 nuclear families from the Fleurbaix Laventie Ville Santé study, we genotyped the INS VNTR polymorphism in 431 children and adolescents (8 to 18 years of age) and their parents. Overweight was defined according to the international definition in both children and adults. A transmission disequilibrium test in families with an overweight offspring was performed. The prevalence of overweight was compared according to genotype. The effect of the genotype on BMI and waist circumference was tested with a linear regression model, adjusting for age, gender, and Tanner stage.

Results: There was an undertransmission of class III alleles from heterozygous parents to their overweight offspring (p < 0.002). Overweight was associated with class I alleles in children and adolescents (12% I/I, I/III vs. 3% III/III; p < 0.08). Those with a class III/III genotype had a 1 kg/m2 lower mean BMI (p = 0.04) and 3 cm lower waist circumference (p = 0.02) than those bearing one or two class I alleles. No association of adiposity or obesity with class I alleles was found in parents.

Discussion: INS VNTR polymorphism seems to contribute to differences in adiposity level in the general population of children and adolescents.