Abstract In this paper we make an empirical analysis of a wide range of claims development trapezoids following Benford’s law. In particular we determine Benfors’s law for different characteristic factors depending on claims development triangles/trapezoids. These characteristic factors are the cumulative claims payments, the incremental claims payments and the individual development factors. For each characteristic factor hypothesis testing is done for verifying/rejecting Benford’s law.

Abstract Recently, the use of dynamic conditional score (DCS) time series models are suggested in the body of literature on time series econometrics. DCS models are robust to extreme observations because those observations are discounted by the score function that updates each dynamic equation. Examples of the DCS models are the quasi-autoregressive (QAR) model and the Beta- t -EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, which measure the dynamics of location and scale, respectively, of the dependent variable. Both QAR and Beta- t -EGARCH discount extreme observations according to a smooth form of trimming. Classical dynamic location and scale models (for example, the AR and the GARCH models) are sensitive to extreme observations. Thus, the AR and the GARCH modelsmay provide imprecise estimates of location and scale dynamics. In the application presented in this paper, we use data from the Shanghai Stock Exchange A-Share Index and the Shenzhen Stock Exchange A-Share Index for the period of 5th January 1998 to 29th December 2017. For the corresponding stock index return time series, a relatively high number of extreme values are observed during the sample period. We find that the statistical performance of QAR plus Beta- t -EGARCH is superior to that of AR plus t -GARCH, due to the robustness of QAR plus Beta- t -EGARCH to extreme unexpected returns.

Abstract This paper studies the association between price of crude oil and the Pakistani Rupee-US Dollar exchange. Asymmetric power autoregressive conditional heteroscedastic (APARCH) model is used to measure the influence of oil price on the nominal exchange rate using daily data of extreme oil price volatility (2006 – 2013). This model is found to fit the data well and the results reveal a high degree of volatility persistence and leverage effect in returns. This study also establishes a positive association between currency exchange rate and oil price. These findings provide insight into the transmission link between the global oil market and exchange rate.

Abstract 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.

Abstract 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.