Wulan Anggraeni

Sunflower seed oil is sourced from Helianthus annuus L., which is a plant that is extremely rich in essential fatty acids and antioxidants, so it can be used as a natural moisturizer that helps prevent, reduce, and protect skin from dryness. The purpose of this study was to evaluate the effect of variations in Carbopol 940 concentration on the physical properties and moisture effectiveness of sunflower seed oil emulgel dosage forms. The formulas were made into 3 formulas, with each concentration of Carbopol 940 for each formula, namely F1 (1%), F2 (1.5%), and F3 (2%). Furthermore, organoleptic, homogeneity, pH, viscosity and spreadability tests were carried out, as well as irritation and moisture effectiveness tests. The results of statistical analysis using one-way ANOVA showed a significant difference (P 0.05) which clarified that F1 provided an optimal increase in skin moisture (50.00%) compared to F2 (42.75%) and F3 (45.55%) and had good and stable physical properties evaluation results. In addition, increasing the concentration of Carbopol 940 in F2 and F3 can increase viscosity, but this actually affects the stability of the physical properties and moisture effectiveness of the preparation. This research concluded that F1 is the best formula and this study provides important information for the development of effective and stable skin moisturizing products, with recommendations for the use of effective concentrations of Carbopol 940 in the formulation of emulgel dosage forms.

Investor interest in single-regional earthquake catastrophe bonds has the potential to decline in the future. To pique investor interest, disaster bond prices can be determined by decomposed disaster zones using seismic parameters and Space Time Depth Magnitude (STDM) distance. Therefore, this study aims to develop a Decomposition of Disaster Region Using Earthquake Parameters and STDM Distance on the Earthquake Catastrophe Bond Pricing (DECBP) model for a single period. The basic idea of developing the model is to observe earthquake characteristics in an area by clustering the area based on the Earthquake Disaster Risk Index (EDRI), earthquake parameters (earthquake magnitude and depth), and STDM distance. The research and development (R&D) methodology used in this work is pursued through the creation of a mathematical model for calculating the price of earthquake catastrophe bonds over a single period. The development stages carried out are regional decomposition modelling, payment functions modelling, distribution of extreme earthquake magnitude values modelling, prediction of interest rates and coupons, numerical simulations, and analysis of the effect of interest rates, coupons, and the amount of regional decomposition on earthquake bond prices. Interest rates, coupons, and the number of regional decompositions that affect bond prices for earthquake events are the results of the analysis of the model that's been developed. The resulting model in this study is expected to assist the Super Purpose Vehicle (SPV) in determining the price of earthquake bonds and serve as a reference for future researchers developing models for the price of earthquake catastrophe bonds.

The variety of catastrophe bond issuances can be used for portfolio diversification. However, the structure of catastrophe bonds differs from traditional bonds in that the face value and coupons depend on triggering events. This study aims to build a diversification strategy model framework using probabilistic–possibilistic bijective transformation (PPBT) and credibility measures in fuzzy environments based on the payoff function. The stages of modeling include identifying the trigger distribution; determining the membership degrees for the face value and coupons using PPBT; calculating the average face value and coupons using the fuzzy quantification theory; formulating the fuzzy variables for the yield; defining the function of triangular fuzzy membership for the yield; defining the credibility distribution for the triangular fuzzy variables for the yield; determining the expectation and total variance for the yield; developing a model of the catastrophe bond diversification strategy; the numerical simulation of the catastrophe bond strategy model; and formulating a solution to the simulation model of the diversification strategy using the sequential method, quadratic programming, transformation, and linearization techniques. The simulation results show that the proposed model can overcome the self-duality characteristic not possessed by the possibilistic measures in the fuzzy variables. The results obtained are expected to contribute to describing the yield uncertainty of investing in catastrophe bond assets so that investors can make wise decisions.

The investor interest in multi-regional earthquake bonds may drop because high-risk locations are less appealing to investors than low-risk ones. Furthermore, a single parameter (earthquake magnitude) cannot accurately express the severity due to an earthquake. Therefore, the aim of this research is to propose valuing a framework for single earthquake bonds (SEB) using a double parameter trigger type, namely magnitude and depth of earthquakes, based on zone division according to seismic information. The zone division stage is divided into two stages. The first stage is to divide the covered area based on regional administrative boundaries and clustering based on the earthquake disaster risk index (EDRI), and the second stage involves clustering based on magnitude and depth of earthquakes and distance between earthquake events using the K-Means and K-Medoids algorithms. The distribution of double parameter triggers is modeled using the Archimedean copula. The result obtained is that the price of SEB based on the clustering result of EDRI categories and K-Means is higher than the price obtained by clustering EDRI categories and K-Medoids with maturities of less than 5 years. The result of this research is expected to assist the Special Purpose Vehicle in determining the price of SEB.

Earthquake catastrophe bond pricing models (ECBPMs) employ extreme value theory (EVT) to predict severe losses, although studies on EVT’s use in ECBPMs are still rare. Therefore, this study aimed to use a mini-review approach (MRA) to examine the use of EVT and identify the gaps and weaknesses in the methods or models developed. The MRA stages include planning, search and selection, analysis, and interpretation of the results. The selection results showed five articles regarding the application of EVT in ECBPMs. Furthermore, the analysis found the following: First, the generalized extreme value (GEV) could eliminate extreme data in a period. Second, the trigger model using two parameters is better than one, but the study did not discuss the joint distribution of the two parameters. Third, the autoregressive integrated moving average (ARIMA) allows negative values. Fourth, Cox–Ingersoll–Ross (CIR) in-coupon modeling is less effective in depicting the real picture. This is because it has a constant volatility assumption and cannot describe jumps due to monetary policy. Based on these limitations, it is hoped that future studies can develop an ECBPM that reduces the moral hazard.