Agent-based modeling for benchmarking banking regulation regimes: Application for the CBDC

COVID-19 pandemic challenges the sustainability of the modern financial system. International central bankers claim that banks are solid. They have accumulated significant capital buffers. Those buffers should be further more augmented by 2027 in line with Basel III reforms. However, disregarding such a consecutive rise in the banking capital adequacy requirements, the US financial authorities undertook an unprecedented step. First time in the country history they lowered the reserve requirement to zero at the end of March 2020. Friedrich von Hayek demonstrated the fragility of the modern fractional reserve banking systems. Together with Ludwig von Mises (von Mises, 1978) he was thus able to predict the Great Depression of 1929 and explain its mechanics much in advance. Thus, we wish to utilize the agent-based modeling technique to extend von Hayek’s rationale to the previously unstudied interaction of capital adequacy and reserve requirement regulation. We find that the full reserve requirement regime even without capital adequacy regulation provides more stable financial environment than the existing one. Rise in capital adequacy adds to modern banking sustainability, but it still preserves the system remarkably fragile compared to the full reserve requirement. We also prove that capital adequacy regulation is redundant when the latter environment is in place. We discuss our findings application to the potential Central Bank Digital Currencies regulation.

Note on “Parameters estimators of irregular right-angled triangular distribution”

Simple estimators were given in (Kachiashvili & Topchishvili, 2016) for the lower and upper limits of an irregular right-angled triangular distribution together with convenient formulas for removing their bias. We argue here that the smallest observation is not a maximum likelihood estimator (MLE) of the lower limit and we present a procedure for computing an MLE of this parameter. We show that the MLE is strictly smaller than the smallest observation and we give some bounds that are useful in a numerical solution procedure. We also present simulation results to assess the bias and variance of the MLE.

Some evidences and applications of the randomized response crossed model in a survey of sensitive population behaviors

Surveying human behaviors, especially in demographic, social, medical and public health research, often involves sensitive issues. Posing direct inquiries about stigmatizing or threatening topics may lead survey participants to refuse to answer or to give untruthful responses. Nonresponse and misreporting denote measurement errors that are difficult to treat and are likely to yield unreliable analyses of the surveyed topics. This problem can be mitigated by adopting survey methods that enhance anonymity and respondent cooperation. One possibility is to create a trustful and confidential relationship between the interviewer and the survey participants. Alternatively, it is possible to fully protect privacy by adopting indirect questioning procedures that elicit information without posing sensitive questions directly. We consider both above-mentioned possibilities showing the results of a real study which explores the effectiveness of the randomized response crossed model proposed by Lee et al. (2013) to produce prevalence estimates for two sensitive traits, cannabis use and its legalization.

Assessment of health and social security agency participants proportion using hierarchical bayesian small area estimation

Data on the number of health insurance participants at the subdistrict level is crucial since it is strongly correlated with the availability of health service centers in the areas. This study’s primary purpose is to predict the proportion of health and social security participants of a state-owned company named Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS) in eleven subdistricts in Padang, Indonesia. The direct, ordinary least square, and hierarchical Bayesian for small area estimation (HB-SAE) methods were employed in obtaining the best estimator for the BPJS participants in these small areas. This study found that the HB-SAE method resulted in better estimation than two other methods since it has the smallest standard deviation value. The auxiliary variable age (percentage of individuals more than 50 years old) and the percentage of health complaints have a significant effect on the proportion of the number of BPJS participants based on the HB-SAE method.

Different inference approaches for the estimators of the sushila distribution

In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.

Logistic and multinomial-logit models: A brief review on their modifications and extensions

The work presents various techniques of the logistic and multinomial-logit modeling with their modifications. These methods are useful for regression modeling with a binary or categorical outcome, structuring in regression and clustering, singular value decomposition and principal component analysis with positive loadings, and numerous other applications. Particularly, these models are employed in the discrete choice modeling and the best-worst scaling known in applied psychology and socio-economics studies.