The South African insurance sector is experiencing a positive growth as the nation is on high quality economic growth and development. However, there is little attention with regards to research on the growth analysis, hence the researchers aim to bridge the gap by analyzing the growth using a mathematically based approach. To verify the wide spread phenomenon behind insurance growth an extended Gompertz model (EGM) which is a member of the unified Richards family was used. The quantitative approach by means of functional limits, the cumulative distribution approach, initial value problem (IVP) and the qualitative derivative approach were used to fully analyze the model. We managed to derive a cumulative function was derived which can be used to estimate the number of insurance growth indicators. The maximum carrying capacity of an insurance industry was estimated using the IVP which in our case is time dependent hence does not concur with other Gompertz related works. Using both the qualitative and derivative approach, a growth function which produced the same pattern with the original Gompertz curve with K(t) as the asymptotically stable and non-constant growth limit were deduced. Hence we can conclude that the growth of insurance sectors does follow a sigmoid shape with non-constant maturity levels. Lastly, we performed a statistical analysis of the nexus between insurance sector growth and economic development using GDP and insurance indicators (net premiums) data. From the statistical analysis done the results showed a positive relationship between the two. This showed that, insurance sector indeed plays a significant role towards economic development and as such their growth patterns should be well attended.
We investigated if general insurance claims are normal or rare events through systematic, discontinuous or sporadic jumps of the Brownian motion approach and Poisson processes. Using firm quarterly data from March 2010 to December 2018, we hypothesized that claims with high positive (negative) slopes are more likely to have large positive (negative) jumps in the future. As such, we expected salient properties of volatile jumps on the written products/contracts. We found that insurance claims for general insurance quoted products cease to be normal. There exist at times some jumps, especially during holidays and weekends. Such jumps are not healthy to the capital structures of firms, as such they need attention. However, it should be noted that gaps or jumps (unless of specific forms) cannot be hedged by employing internal dynamic adjustments. This means that, jump risk is non-diversifiable and such jumps should be given more attention.
Fundamental theorem behind financial markets is that stock prices are intrinsically complex and stochastic in nature. One of the complexities is the volatilities associated with stock prices. Price volatility is often detrimental to the return economics and thus investors should factor it in when making investment decisions, choices, and temporal or permanent moves. It is therefore crucial to make necessary and regular stock price volatility forecasts for the safety and economics of investors’ returns. These forecasts should be accurate and not misleading. Different traditional models and methods such as ARCH, GARCH have been intuitively implemented to make such forecasts, however they fail to effectively capture the short-term volatility forecasts. In this paper we investigate and implement a combination of numeric and probabilistic models towards short-term volatility and return forecasting for high frequency trades. The essence is that: one-day-ahead volatility forecasts were made with Gaussian Processes (GPs) applied to the outputs of a numerical market prediction (NMP) model. Firstly, the stock price data from NMP was corrected by a GP. Since it not easy to set price limits in a market due to its free nature, and randomness of the prices, a censored GP was used to model the relationship between the corrected stock prices and returns. To validate the proposed approach, forecasting errors were evaluated using the implied and estimated data.
There is a continuous increase in health costs, thereby increasing pressure on individuals and consequently making the amounts claimed by the insured to be on the increase. In this study, data was collected from a large local insurance company in Zimbabwe for the period from January 2012 to December 2016. The aim of this study was to analyse the distribution and future pattern of insurance health claim system using time series approach. Akaike information criterion and Schwarz Bayesian criterion were used to select the adequate model through maximum likelihood estimation methods. ARIMA (0, 0, 0) (1, 0, 1) [12] is the model that was chosen to forecast claim amounts. The use of ARIMA models proves to be an excellent instrument for predicting and capturing the cost trend of health claims which can help in decision making to insurance companies. Keywords: ARIMA; Box-Jenkins; health insurance; time series.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.