Investors generally aim to obtain a high return from their stock portfolio. However, investors must realize that a high value-at-risk (VaR) is essential to calculate for this aim. One of the objects in the VaR calculation is the asymmetric return volatility of stocks, which causes an unbalanced decrease and increase in returns. Therefore, this study proposes a mean-value-at-risk (mean-VaR) stock portfolio optimization model based on stocks’ asymmetric return volatility and investors’ risk aversion preferences. The first stage is the determination of the mean of all stocks in the portfolio conducted using the autoregressive moving average Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity (ARMA-GJR-GARCH) models. Then, the second stage is weighting the capital of each stock based on the mean-VaR model with the investors’ risk aversion preferences. This is conducted using the Lagrange multiplier method. Then, the model is applied to stock data in Indonesia’s capital market. This application also analyzed the sensitivity between the mean, VaR, both ratios, and risk aversion. This research can be used for investors in the design and weighting of capital in a stock portfolio to ensure its asymmetrical effect is as small as possible.

The fundamental stages in designing a stock portfolio are each stock’s selection and capital weighting. Selection and weighting must be conducted through diversification and price movement analysis to maximize profits and minimize losses. The problem is how the technical implementations of both are carried out. Based on this problem, this study aims to design these selection and weighting mechanisms. Stock selection is based on clusters and price movement trends. The optimal stock clusters are formed using the K-Means algorithm, and price movement analyses are carried out using the moving average indicator. The selected stocks are those whose prices have increasing trends with the most significant Sharpe ratio in each cluster. Then, the capital weighting for each preferred stock is carried out using the mean-variance model with transaction cost and income tax. After designing the mechanism, it is applied to Indonesia’s 80 index stock data. In addition, a comparison is conducted between the estimated portfolio return and the actual one day ahead. Finally, the sensitivity of investors’ courage in taking risks to their profits and losses is also analyzed. This research is expected to assist investors in diversification and price movement analysis of the stocks in the portfolios they form.

The national scale of catastrophic losses risk linked to state catastrophe bonds (SCB) is enormous. It can reduce investors’ interest in buying them because the capital required and the loss probability are also significant. To overcome this, the SCB can be made on a smaller regional scale, known as a regional catastrophe bond (RCB). Through RCBs, the catastrophic loss risk investors bear becomes smaller, which can increase investors’ interest in buying them. Unfortunately, RCB issuance faced a fundamental obstacle, where its complex pricing model needed further study. Therefore, this study aims to model it. The model uniquely involves the inflation rate modeled using the Fisher equation and the nonbinary scheme of coupon and redemption value payments modeled by a compound Poisson process. In addition, the model is applied to Indonesia’s catastrophe data, resulting in all provinces’ RCB price estimation and the effects of several variables on RCB price. This research can guide the RCB pricing process of the country’s regions. The estimated RCB prices can be used by Indonesia’s government if RCBs are to be issued one day. Finally, the effects of the inflation rate, catastrophe intensity, and geographical location on RCB prices can guide investors in selecting bond portfolios.

Forming a portfolio is a strategy that is often carried out by investors in risky investment conditions. Five non-risk free stocks were selected, namely PTBA, IPCM, ANTM, BUMI, and ADMF. The purpose of forming this portfolio is to determine the composition of the weight (proportion) of the allocation of funds in each of these shares in forming the optimum portfolio. The method used is the Mean-Variance investment portfolio optimization model without risk-free assets using the Markowitz approach. Based on the results obtained by the optimum portfolio of the Mean-Variance model without risk-free assets, the average return is 0.00105 and the variance is 0.000067 with a portfolio ratio value of 14.65256. The proportion of fund allocation to PTBA shares = 0.28872; IPCM=0.02484; ANTM=0.00016; EARTH=0.13501; and ADMF=0.55126. It is hoped that the formation of this portfolio optimization model will be useful as an alternative for investors in optimizing the investment portfolio to make it more profitable in the future.

Sports education has learning characteristics that are different from other subjects because it is more psychomotor in nature and still involves affective and cognitive abilities that are comparable to learning those who mainly move. Students are expected to be able to improve affective and cognitive skills with learning modifications that have been arranged by the teacher. The same goes for encouraging healthy living habits through sports education. By banning students from the values of healthy living habits, sports education can become the foundation for a complex of knowledge that will help students maintain their quality of life into old age. Health is a fundamental need in life. The majority of individuals frequently disregard the behavior needed to establish a culture of healthy living. To create good quality human beings in terms of physical, mental and spiritual development, it is crucial to promote a culture of healthy living. The government and people who care about health must continue to step up their efforts to better understand each person in order to develop a perspective that can shift behavior in a positive direction. It is believed that through physical, sports and health education in educational institutions, children will develop a healthy lifestyle culture from an early age.

Stocks are one of the most widely used financial market instruments by investors in investing. The most important component of any investment is volatility. Volatility is a conditional measure of variance in stock returns and is important for risk management. In addition to volatility, the important things in investing are return and risk. Risk can be measured using Value-at-Risk (VaR) and can estimate the maximum loss that occurs. The purpose of this study is to determine VaR using the Autoregressive Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GJR-GARCH) model. The stages of data analysis used are estimating the ARMA model and the GARCH model, then estimating the GJR-GARCH model by looking at the heteroscedasticity and asymmetric effects on the GARCH model. Next, determine the VaR value from the estimated mean and variance (volatility) using the ARMA-GJR-GARCH model. The results of the model estimator obtained are based on the return data for the four stocks analyzed, namely the ARMA (5,5)-GJR-GARCH (1,1) model for ICBP stocks and ARMA (1,2)-GJR-GARCH (1,1) for PGAS shares. The Value-at-Risk values of each stock are 0.060427 and 0.024724. This research can be used by investors as a consideration in making investment decisions.