Estimation tail parameter for Geometric Brownian motion

Right-tailed distributions are very important in many applications. There are many studies estimating the tail index. In this paper, we will estimate the tail parameter using the three (the Direct, Bootstrap and Double Bootstrap) methods. Our aim is to illustrate the best way to estimate the -stable with using simulation and real data for the daily Iraqi financial market dataset.

The Effect of Branch Office Locations on Technical Efficiency: Evidence from the Indian Life Insurance Industry

The purpose of this article is to investigate the efficiency and productivity growth of Indian life insurance industry and to assess the effect of branch office locations on efficiency. This study has analysed the efficiency and productivity performance of all the 24 life insurance companies during the period from 2016 to 2019, using slack-based measures (SBM) of data envelopment analysis. SBM super-efficiency model is used to rank the fully efficient life insurers. Malmquist index is used to assess the productivity of life insurance companies. To assess the effect of branch office geographical locations on efficiency, double bootstrap regression has been used. The findings indicate that Indian life insurance industry experienced significant fluctuations in mean technical efficiency during the study period. Almost 50% of life insurers operated efficiently in one or more years during the study period. Only 3 out of 24 life insurers were found scale efficient. Interestingly, 50% of life insurers experienced growth during our study period. Double bootstrap regression analysis indicates that semi-urban and rural branch offices have positive effect on the efficiency of the life insurers. This study is first of its kind that has assessed the effect of branch office locations on the efficiency of life insurers. The study brings to light the operating characteristics, efficiencies and productivity of the Indian life insurance companies for the period from 2016 to 2019.

Stressed assets, off-balance sheet business activities and performance of Indian banking sector: a DEA double bootstrap approach

Purpose This paper examines the technical efficiency (TE) of Indian commercial banks during 1998–2015. Design/methodology/approach This study uses mathematical programming-based data envelopment analysis (DEA) methodology to measure technical efficiency of Indian banks. Further, Simar and Wilson (2007) double bootstrap procedure is applied to examine the determinants of efficiency of the Indian banks, by examining the effects of various bank specific and other contextual variables. Findings The results indicate substantial upward bias in the conventional efficiency estimates of the Indian commercial banks. Needless to note, such upward bias is consistent with the theoretical postulates. The bootstrapped regression results show that increasing capital adequacy ratio is positively associated with bank efficiency. The popular belief that non-performing assets have a dampening effect on performance of banks is validated. Among others, ownership category is observed to be an important determining factor of bank efficiency. Specifically, state-owned banks (SOBs) are relatively lagging behind the foreign banks. Moreover, larger banks are observed to have a significantly higher level of efficiency, therefore, recent official policy initiatives toward consolidation of SOBs are validated. Originality/value As this study uses Simar and Wilson (2007) bootstrap approach, it enables the authors to have an estimate of the extent of bias in the traditional DEA TE scores. It also helps us drawing consistent inferences by rectifying the problem of serial correlation in the conventional second stage regression in this regard.

Productivity growth, catching-up and technology innovation in microfinance institutions in India: evidence using a bootstrap Malmquist Index approach

PurposeThis paper aims to examine the total factor productivity (TFP) change and its components: efficiency change and technical change in microfinance institutions (MFIs) in India operating from 2005 to 2018. The study also scrutinizes the variations in productivity levels across the distinct organizational form and size groups of MFIs. In addition to this, the authors identify the contextual factors that determine TFP growth, catching-up and technology innovation in MFIs.Design/methodology/approachThe study employs a smooth homogeneous bootstrap estimation procedure of Simar and Wilson (1999) for obtaining reliable estimates of Malmquist indices –productivity and its components – in a data envelopment analysis (DEA) framework for individual MFIs. In order to identify the determinants of productivity change and its components, the study follows Simar and Wilson's (2007) guidelines and applies a bootstrap truncated regression model. The double bootstrap procedure performs well, both in terms of allowing correct estimation of bias and deriving statistically consistent productivity estimates in the first and root mean square errors in the second stage of the analysis.FindingsThe empirical results reveal that the MFIs have shown average productivity growth of 6.70% during the entire study period. The observed productivity gains are primarily contributed by a larger efficiency increase at the rate of 4.80%, while technical progress occurs at 2.3%. Nonbanking financial companies (NBFC)-MFIs outperformed non-NBFC-MFIs. Small MFIs show the highest TFP growth in terms of size groups, followed by the large MFIs and medium MFIs. The bootstrap truncated regression results suggest that the credit portfolio, size and age of MFIs matter in achieving higher productivity levels.Practical implicationsThe practical implication drawn from the study is that the Indian MFI industry might adopt the latest technology and innovations in the products, risk assessment and credit delivery to improve their productivity levels. The industry must focus on enhancing the managerial skill of its employees to achieve a high productivity level.Originality/valueThis study is perhaps the initial attempt to explain the productivity behavior of MFIs in India by deploying a statistically robust double bootstrap procedure in the DEA-based Malmquist Productivity Index (MPI) framework. The authors estimate the bias-adjusted productivity index and its decompositions, which represent more reliable and statistically consistent estimates. For contextual factors responsible for driving productivity change, the study deploys a bootstrap truncated regression approach.

Inefficiency Factors in Basic Health Care Supply in Côte d’Ivoire: A Comparative Econometric Analysis

The achievement of universal health coverage has put Primary Health Care back at the center of policy orientations, particularly by identifying factors likely to improve the organization of peripheral facilities. However, this objective depends on the econometric methods used, especially for cross-sectional data and small sample sizes. This study aims to examine the sensitivity of the most usual estimation methods (Stochastic Frontier Analysis (SFA), Data Envelopment Analysis (DEA), DEA double bootstrap, Tobit, Truncated Standard Regression) for evaluating the scores and determinants of technical inefficiency of Primary Health Care Facilities (PHCF) in Côte d’Ivoire. Estimates show average technical efficiency scores of 94.13% for the DEA versus 89.61% for the SFA and 82.24% for the DEA double bootstrap. The results also indicate a proportion of determinants of technical inefficiency, in decreasing order of importance, with the DEA double bootstrap, the SFA, truncated regression and Tobit. This technical inefficiency can be improved in policies to promote basic health care by: increasing the proportion of nurses in the medical staff, the nurse/inhabitant ratio, the adult literacy rate by region, controlling the average capacity of the PHCFs, improving their geographical accessibility and reducing the rate of extreme poverty by health region.

An application of PCA-DEA with the double-bootstrap approach to estimate the technical efficiency of New Zealand District Health Boards

Using yearly panel data from 2011 to 2017 on New Zealand District Health Boards (DHBs), this study combines principal component analysis and data envelopment intertemporal analysis with the double-bootstrap approach to estimate the technical efficiency of health care providers along with the trend of efficiency performances. The results show that although most large DHBs have improved their level of technical efficiency between 2011 and 2017, the majority of medium- and small-sized DHBs have not seen any noticeable improvement in their level of technical efficiency. The results also show that large and tertiary DHBs operate at a high level of technical efficiency. In contrast, most of the medium- and small-sized DHBs posted some of the lowest technical efficiency scores. Furthermore, the results show that medium- and small-sized DHBs have a higher average length of hospital stays which is found to be associated with decreasing levels of technical efficiency scores.