Losses experienced by the Indonesian government due to floods are predicted. It is because of the significance of population growth, closure of water catchment areas, and climate change in many regions in Indonesia. The government has tried to reduce the risk but faces insufficient funds. Therefore, new innovative funding sources are essential to overcome these limitations. One way to obtain it is through issuing Flood Catastrophe Bonds (FCB). Unfortunately, Indonesia has had no FCB price estimate until now. On the basis of this problem, this study aims to estimate the FCB price in Indonesia. The primary method used is the approximation method of the aggregate loss distribution. This method can compute the aggregate flood loss cumulative distribution function value faster. The FCB fair price estimation results are cheap because the risk of the instrument is significant. This significant risk is also proportional to the large return. Finally, further analysis shows that in Indonesia, the attachment point of the FCB has a relationship that is in line with the price, while the term of FCB does not. This research is expected to assist the Indonesian government in determining the fair price of FCB in Indonesia. This research can assist the investors in choosing FCB based on expected return, attachment point, and the term they want.

Stock is one of the investment assets that has its charm for investors. It is very liquid and has a high rate of return, but it has a high risk. The strategy commonly used to minimize investment risk is to diversify through portfolio formation. A good allocation of funds must be determined in forming an optimal portfolio. In addition, the method of stock selection needs to be considered so the stocks are well diversified and the portfolio developed has good performance. This study aims to compare stock selection between K-Means and Average Linkage clustering approaches in forming an investment portfolio. Clustering analysis is used to group IDX80 stocks based on their attributes. In forming a portfolio with the Mean-VaR model, the stock selection decision criteria used are by selecting stocks with the highest positive returns from each cluster. As a result, the two clustering techniques show the superiority of the Silhouette score for a certain number of clusters, but there are still more advantages in Average Linkage. The portfolio approached by Average Linkage resulted in a better performance than the portfolio approached by K-Means. Therefore, Average Linkage clustering can be used as a better recommendation in decision-making to select stocks so as to produce optimal portfolio performance.

In deciding to invest in stocks traded in the capital market, investors need to predict which stocks provide the prospect of return and the risks to be faced. This paper aims to predict the return and risk of stock asymmetry using a time series model approach. Predicting stock returns and risk is based on the Autoregressive Integrated Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GJR-GARCH) model. In contrast, the largest risk potential measurement is performed using the Value-at-Risk (VaR) model. The data analyzed are the best ten stocks according to the criteria that apply on the IDX, the period between 17 December 2018 to 14 December 2021, which includes the names of stock BBCA, BBNI, BBRI, BMRI, ASII, ICBP, PGAS, PTBA, TLKM, and UNVR. The analysis results show that of the best ten stocks, based on the ratio between the predicted values of the average return and Value-at-Risk, those with relatively better performance are PTBA, TLKM, UNVR and BBCA stocks. Based on the results of this analysis, it can be used as a reference in making investment decisions for investors, specifically investing in the ten stocks analyzed.

Demand elasticity is the sensitivity of changes in the number of goods demanded by consumers due to changes in the price of goods. This paper compares the price elasticity of demand with and without memory effect using fractional-order derivatives. This study is designed using the development theory of fractional derivatives for the economic field in determining the price elasticity of demand. The result of numerical simulation using the value of α and p indicated that the price elasticity of demand with memory effect is more accurate than without the memory effect. Furthermore, this study concluded that the price elasticity of demand does not only depend on the latest price (current price) but changes in all prices from a specific time interval. The findings of this study suggest future studies can examine the phenomenon of market equilibrium using fractional-order derivatives.