Financial Statistical Journal
EISSN : 2578-1960
Published by: EnPress Publisher (10.24294)
Total articles ≅ 20
Latest articles in this journal
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i4.1074
In the process of economic development, influenced by many factors, this paper establishes a regression model between capital stock and financial development under the influence of endogenous growth theory to analyze the change of capital stock in the process of economic growth. It is found that financial development plays a greater role in the capital stock, and the role played by financial markets is weaker than that of financial intermediaries
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i4.1057
This study aims to investigate the effect of continent and initial GDP per capita level of a country on the relationship between insurance activities and economic growth. This study considers panel data consists of 123 countries from 1967 to 2014. Both static panel model and dynamic panel model are used to evaluate the effect of both continent and initial GDP per capita level of a country to the economic growth. The findings show significant causal relationship between insurance development and economic growth. However, the relationship is varied in different countries due to different initial income levels and locations. The effect of insurance development on economic growth of a country is indirect because it depends on the performance of the investment of insurers. Therefore, policymakers should consider their own country’s special characteristics when formulating a policy. Policymakers should clearly understand the nature of their insurance sector such as interconnectedness between financial sector and insurance sector, whether to promote insurance sector to grow their economy. By understanding the effect of continent and the initial GDP per capita level, policymakers could formulate and implement more effective policies on their country’s insurance sector to ensure the prosperity of the country’s economic growth.
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i3.687
This paper presents a bootstrap resampling scheme to build pre-diction intervals for future values in fractionally autoregressive movingaverage (ARFIMA) models. Standard techniques to calculate forecastintervals rely on the assumption of normality of the data and do nottake into account the uncertainty associated with parameter estima-tion. Bootstrap procedures, as nonparametric methods, can overcomethese diculties. In this paper, we test two bootstrap prediction in-tervals based on the nonparametric bootstrap in the residuals of theARFIMA model. In this paper, two bootstrap prediction intervals areproposed based on the nonparametric bootstrap in the residuals ofthe ARFIMA model. The rst one is the well known percentile boot-strap, (Thombs and Schucany, 1990; Pascual et al., 2004), never usedfor ARFIMA models to the knowlegde of the authors. For the secondapproach, the intervals are calculated using the quantiles of the empir-ical distribution of the bootstrap prediction errors (Masarotto, 1990;Bisaglia e Grigoletto, 2001). The intervals are compared, througha Monte Carlo experiment, to the asymptotic interval, under Gaus-sian and non-Gaussian error distributions. The results show that thebootstrap intervals present coverage rates closer to the nominal levelassumed, when compared to the asymptotic standard method. An ap-plication to real data of temperature in New York city is also presentedto illustrate the procedures.
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i3.574
This publication presents the results of the comparative analysis of economic growth in the United States and Poland using Harver Analytics. It takes into account factors such as GDP, industrial output, consumption expenditure, investment, exports and consumption expenditure of the government. The aim of the publication is presentation of differences between economic growth in Poland and USA.
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i4.829
In the fields of Management and Economics, there are many studies that have made use of the degree of concentration of a market or industry, especially when dealing with subjects such as industrial concentration. However, these indexes do not adequately present the level of significance. This problem is overcome by the proposed KSG indicator based on the Kolmogorov-Smirnov test and whose interpretation of significance is given by Goodman. Hence the name: KSG. The proposed model uses non-parametric techniques to establish the dimension of concentration and defines the level of significance of the value found. This is a quantitative study using parametric statistics (polynomial regression) on data generated through simulation. In each data simulation, for the given value of n companies, the share of Company 1 is made to vary, with the other shares being maintained unchanged. For each simulation, data related to values of "Share of Company 1" were extracted and corresponding indexes: KSG, CR4, CR8 and HHI. The results show that the indicator proposed in this study is fully justified.
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i2.814
Expert systems, a type of artificial intelligence that replicate how experts think, can aide unskilled users in making decisions or apply an expert’s thought process to a sample much larger than could be examined by a human expert. In this paper, an expert system that ranks financial securities using fuzzy membership functions is developed and applied to form portfolios. Our results indicate that this approach to form stock portfolios can result in superior returns than the market as measured by the return on the S&P 500. These portfolios may also provide superior risk-adjusted returns when compared to the market.
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i4.942
The study attempts to explore the relationship between riskgovernance structureand firm performance. In perhaps the first of its kind attempt, a normative framework for risk governance structures is being put forward. Based on the framework, an index indicating strength/quality of risk governance structures is proposed. Then, the impact of risk governance structure on firm performance is gauged. To this end, the study makes use of constituents of S&P CNX500 index and covers a ten year period from April 1, 2005 to March 31, 2015.To control for potential endogeneity among variables of interest, the study makes use of a robust and reliable methodology,‘difference-GMM’. In addition, to ensure completeness of results, the study employs control variables such as recession dummy, firm’s age, size, and growth rate and leverage ratio. The results suggest that robust risk governance structures do not necessarily lead to better firm performance. In fact, risk governance index is negatively related to both ROA and ROE. The relationship is not statistically significant but has wide economic implications. A prominent implication being, mere constitution of risk management committee and appointment of CRO will not improve firm performance; regulators and companies need to ensure that governance structures are not too rigid, excessively risk averse and ineffective and inefficient in decision making. Given the simplicity and reliability of the proposed risk governance index, and the recommendations put forth in the paper, the study is expected to be of immense utility in an important yet neglected area of risk governance.
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i2.420
In this paper we make an empirical analysis of a wide range of claims developmenttrapezoids following Benford’s law. In particular we determine Benfors’s law fordifferent characteristic factors depending on claims development triangles/trapezoids.These characteristic factors are the cumulative claims payments, the incrementalclaims payments and the individual development factors. For each characteristic factor hypothesis testing is done for verifying/rejecting Benford’s law.
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i4.803
Essential healthcare is a civil right. Payments toward healthcare is a moral compulsion, and no less strong than legal compulsion like income tax. Healthcare payments can redistribute disposable income. Redistribution may be vertical (from rich to poor or opposite) and horizontal (from men to women or from households without children to households with children). Health planners are interested in degrees to which redistribution occurs. In this paper, we aim to analyze how well different forms of healthcare payments in Bangladesh redistribute disposable income. Our data comes from Bangladesh Household Income and Expenditure Survey, 2010. Using the methods developed by Aronson et al. (1994), we assessed average rate effect, progressivity, horizontal equity and re-ranking. The results suggest that Bangladesh health systems finance has a pro-rich redistribution of disposable income. Post-payment disposable income decreases for the poor and increases for the rich. As a result, the poor are in a shortfall in disposable income, which ultimately get them to impoverishment, and or push them to deeper poverty. On the contrary, the rich become richer due to increase in post-payment disposable income. This leads to an increase in inequality.
Financial Statistical Journal, Volume 1; https://doi.org/10.24294/fsj.v1i2.913
Stochastic agro-economic model GLOBIOM is used to demonstrate how best to design and evaluate the CAP’s financial and structural measures, both individually and jointly, in the face of inherent uncertainty and risk. The model accounts for plausible shocks simultaneously and derives measures that are robust against all shock scenarios; it can thus help avoid the irreversibility and sunk costs that occur in unexpected scenarios.To allow adequate agricultural production, we show that the distribution of CAP funds needs to account for exposure to risks, security targets, and the synergies between policy measures, including production, trade, storage, and irrigation technologies.