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Analysis of the Impact of Billing, Authorizations and Unknown Debts on the Technical Reserve of Health Promotion Companies: Case of HPC in Colombia
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We develop an R package panelView and a Stata package panelview for panel data visualization. They are designed to assist causal analysis with panel data and have three main functionalities: (1) They plot the treatment status and missing values in a panel dataset; (2) they visualize the temporal dynamics of the main variables of interest; and (3) they depict the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. These tools can help researchers better understand their panel datasets before conducting statistical analysis.
Currently, there are 37 insurance companies and branches of foreign insurance companies operating in Slovakia. The impact on results of operations of insurance companies have obligations to clients and investment activities. Insurance works by creating reserves, which are on the one hand intended to eliminate the negative financial consequences and the fulfillment of the obligations and, on the other hand, to realize their own investment policy. Control mechanism based on the establishment of technical provisions must guarantee, if necessary, their availability.Technical provisions in insurance are used as mathematical and statistical methods for calculating basic characteristics. Parametric trend estimation uses the method of least squares known from regression analysis. The starting point of the analysis is a set of data published by the National Bank of Slovakia for the period of 2004-2014. Identification of the trend in the technical insurance in the area of life and non-life insurance in the Slovak Republic for 2004-2014 has been realised taking into consideration three basic types of time series (linear, quadratic and exponential) as well as basic characteristics of the technical insurance were found out in order to create a model for the linear trend up to 2017. Prognoses of the technical insurance in life and non-life insurance for the 2013-2017 have been elaborated. The results show that profitability of the insurance sector has not fundamentally changed lately and remained sufficient. The solvency of the insurance companies increased significantly due to the profits of re-evaluation of the commercial papers in the portfolio for sales. In life insurance the growth of technical premium continues, although its structure has changed. In non-life insurance, negative trends continued in car insurance. It can be concluded that as a result of higher competitiveness, the insurance shows a downward trend and the profitability in the insurance sector is lower, however, insurance premiums remain sufficient.
Presented to the Institute of Actuaries, 28 January 2002] abstract This paper considers a wide range of stochastic reserving models for use in general insurance, beginning with stochastic models which reproduce the traditional chain-ladder reserve estimates. The models are extended to consider parametric curves and smoothing models for the shape of the development run-off, which allow extrapolation for the estimation of tail factors. The Bornhuetter-Ferguson technique is also considered, within a Bayesian framework, which allows expert opinion to be used to provide prior estimates of ultimate claims. The primary advantage of stochastic reserving models is the availability of measures of precision of reserve estimates, and in this respect, attention is focused on the root mean squared error of prediction (prediction error). Of greater interest is a full predictive distribution of possible reserve outcomes, and different methods of obtaining that distribution are described. The techniques are illustrated with examples throughout, and the wider issues discussed, in particular, the concept of àbest estimate'; reporting the variability of claims reserves; and use in dynamic financial analysis models.
The advent of the future European prudential framework (Solvency II) and, to a lesser extent, of the phase II of the IFRS dedicated to the insurance contracts, will systematize the use of the Value-at-Risk (VaR) risk measure in insurance. Especially used for financial purposes, the measure of an insurance risk by a VaR technique requires a specific adaptation. Schematically we may discern two different contexts, which impose distinct approaches: − the measurement of the risk related to the sinistrality by the use of a VaR in the heart of the probability distribution : the technical provision will have to be enough to pay the claims with a 75 % probability ; − the measurement of risk related to the ruin of the company by the mean of a very high order VaR : the solvency capital must to be calibrated to control the ruin if the insurer with a probability higher than 99.5 %. In the first situation, the two standard approaches (historical VaR or modeling the sinistrality) can be based on a statistical material of relative but sufficient size to estimate a VaR in the heart of the probability distribution. In the second case, we are confronted to the absence of observations. Also we have to model the basic variables which influence the solvency of the company in order to be able to simulate the ruin of the company and finally to estimate the high order VaR. This last stage will require the use of Extreme Value Theory. In this paper, we present the contexts of VaR computation in insurance, the related probabilistic results and the limits of these kinds of criterion for insurance purposes. α Pierre Thérond is a Ph.D. Student and is teaching at the actuarial school ISFA (Université Claude Bernard Lyon 1 – France). He works as an actuary for the consulting company WINTER & Associés. Contact : ptherond@winter-associes.fr * Frédéric Planchet is an associate Professor of finance and insurance at the actuarial school ISFA. He is an Associate Actuary of the consulting company WINTER & Associés. Contact : fplanchet@winter-associes.fr β Institut de Science Financière et d'Assurances (ISFA) -50 avenue Tony Garnier -69366 Lyon Cedex 07 – France. γ WINTER & Associés – 43-47 avenue de la Grande Armée -75116 Paris et 18 avenue Félix Faure -69007 Lyon – France.
This paper investigates the relationship between governance structure and bank performance in normal and crisis times. Using statistical learning algorithms on R, we regressed the profitability indicators as dependent variables on board structure, bank-specific characteristics, and macroeconomic variables to examine the impact on profitability for 76 banks over 17 countries for the period from 2007 to 2019, including normal periods and the subprime crisis time. The findings show that the duality between CEO and chair has a negative and significant impact on bank performance during the full study period. BoD size and gender diversity within the BoD have no influence on bank performance but in crisis time, a woman CEO has a positive and significant impact on bank's performance.
This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated,vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.
Using association rules in classification is a great success which produces high accuracy classifiers. Even so, the principal advantage of the associative classifiers lies in interpretation. However, pruning the useless rules among the huge set of the mined rules as well as combining them to build a classifier remains a subject for improvement and further research. In this paper, we introduce a new algorithm to build a classifier based on Regularized Class Association Rules in a categorical data space called RCAR. The characteristic of this algorithm is, therefore, threefold: First, mining an exhaustive set of Class Association Rules (CARs) according to a predefined values of support and confidence thresholds. Second, applying a regularized logistic regression algorithm with Lasso penalty on the rules space to build a model that predicts the conditional probability of the existence of the outcome. Useless rules are pruned thanks to the selective nature of Lasso regularization. Third, organizing and visualizing the CARs which survive the first step of pruning by Lasso regularization using metarules. An optional step of pruning could be undertaken on the basis of the metarules and subject knowledge. Likewise, the empirical results indicate that RCAR gives comparable accuracy against Random Forest and GBM.
In this paper, the Barro hypothesis by means of a VAR model and multicointegration for the Mexican economy is proved. The Barro hypothesis is based on the rational expectations postulates, which indicates that a monetary expansion does not have real effects unless it is surprise. Nevertheless, based on the results, the anticipated and the non-anticipated money supply have real effects in the Mexican economy. In this case, the money is non-neutral. The reasons for this non-neutrality include rigidities in the system, the particular characteristics of monetary policy, and the structure of the Mexican economy.
This paper investigates the role of technical reserves in the income smoothing behavior of insurance companies. This is one of the first attempts in the literature to trace such relationship in the insurance industry, especially at a multi-country setting. The experience of 770 insurance firms operating in 87 countries over the period 2000–2009 reveals that there is a significant evidence of income smoothing. The paper also finds that institutional characteristics, e.g., the rule of law, common law legal origin, economic freedom, and regulations relating to technical provisions and supervisory power constrain income smoothing but other factors such as capital requirements, tax deductibility of provisions, auditing, and corporate governance do not have a significant effect.
This routine provides procedures for pooling proportions in a meta-analysis of multiple studies study and/or displays the results in a forest plot. The confidence intervals are based on score(Wilson) (Newcombe, R. G. 1998) or exact binomial(Clopper-Pearson) (Newcombe, R. G. 1998) procedures. A test of whether the summary effect measure is equal to the zero is given, as well as a test for heterogeneity, i.e., whether the true effect in all studies is the same. Heterogeneity is also quantified using the I-squared measure (Higgins et al. 2003).
This paper investigates the role of technical reserves in the income smoothing behavior of insurance companies. This is one of the first attempts in the literature to trace such relationship in the insurance industry, especially at a multi-country setting. The experience of 770 insurance firms operating in 87 countries over the period 2000-2009 reveals that there is a significant evidence of income smoothing. The paper also finds that institutional characteristics, e.g., the rule of law, common law legal origin, economic freedom, and regulations relating to technical provisions and supervisory power constrain income smoothing but other factors such as capital requirements, tax deductibility of provisions, auditing, and corporate governance do not have a significant effect.
A distribution-free formula for the standard error of chain ladder reserve estimates is derived and compared to the results of some parametric methods using a numerical example.
In 1993, the author derived a formula for the standard error or chain ladder reserve estimates. In the present communication, a very intuitive and easily programmable recursive way of calculating the formula is given. Moreover, this recursive way shows how a tail factor can be implemented in the calculation of the standard error.
This article investigates incentives of insurance firm managers to manipulate loss reserves in order to maximize their compensation. We find that managers who receive bonuses that are likely capped or no bonuses tend to over-reserve for current-year incurred losses. However, managers who receive bonuses that are likely not capped tend to under-reserve for current-year incurred losses. We also find that managers who exercise stock options tend to under-reserve in the current period. Copyright (c) The Journal of Risk and Insurance, 2009.
We consider a risk process modelled as a compound Poisson process. The ruin probability of this risk process is minimized by the choice of a suitable investment strategy for a capital market index. The optimal strategy is computed using the Bellman equation. We prove the existence of a smooth solution and a verification theorem, and give explicit solutions in some cases with exponential claim size distribution, as well as numerical results in a case with Pareto claim size. For this last case, the optimal amount invested will not be bounded.
Introduction -- Scope and focus -- The modelling object -- Databases -- The modelling process -- Calculation techniques -- Model examples -- Policy decisions -- Concepts of health economics -- Financial management and accounting for health schemes -- A primer on the mathematics of private health insurance -- Regression analysis -- International reference statistics
Several general approaches to calculating interval forecasts are described and compared. They include the use of theoretical formulae based on a fitted probability model, various "approximate" formulae (which should be avoided), and empirically-based, simulation, and resampling procedures. Some gener al comments are made as to why prediction intervals tend to be too narr ow in practice to encompass the required proportion of future observations. An example demonstrates the overriding importance of careful model specification.
We report that insurance firms manage loss reserves to avoid violating certain test ratio bounds (known as IRIS ratios) that are used by regulators for solvency assessment. In our sample, almost two-thirds of the firms that would violate four or more IRIS ratios successfully adjust reserves to reduce the reported number of violations to less than four. This finding is significant because four violations usually trigger regulatory intervention. Our results indicate that non-earnings goals are an important influence on discretionary accounting choice. They also suggest that reserve manipulation can postpone needed regulatory intervention, sometimes for an extended period.
Business intelligence in the integration and structuring of information to prepare the calculation of the technical reserve in HPCs
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