27 research outputs found
Modeling of Mean-VaR portfolio optimization by risk tolerance when the utility function is quadratic
No full textMathematical Modeling for Estimating the Risk of Rice Farmers’ Losses Due to Weather Changes
No full textThis paper discusses the relationship between weather and rice productivity modeled using the Cobb–Douglas production function principle, with the hypothesis that rice production will increase in line with the increase in average rainfall, wind speed, and temperature every month and then decrease if the weather conditions exceed the threshold. As a result, farmers have the risk of losing rice production. To overcome this problem, we try to estimate the value of the risk. The purpose of this study is to estimate the risk of losses that occurred in rice plants due to weather changes. The method used in this study is risk estimation with the Tail Value at Risk (TVaR) approach. In addition to TVaR, it is estimated simultaneously with Value at Risk (VaR) and Conditional Value at Risk (CVaR). This study uses weather data consisting of rainfall data, wind speed, and air temperature collected from geophysical and meteorological data. Meanwhile, yield data were obtained and processed from the Central Statistics Agency and the West Java Agricultural Service. The data used are data from 2008 to 2021. There are two main parts of the results in this study, namely mathematical analysis and data analysis. The mathematical analysis is a risk model derivation process, which includes TVaR risk measurement. The data analysis process is a simulation of the estimated risk of rice production loss. The results obtained from this study are the value of opportunity risk of loss based on the VaR, CVaR, and TVaR approaches. The conclusion of this study is that the rice plants have a risk of loss in the form of reduced yields caused by weather changes. Farmers can plan to overcome this loss problem, by setting up a reserve fund. Risk of loss can be managed through the rice agricultural insurance program. This is in line with the Indonesian government’s program through the ministry of agriculture. Thus, farmers, insurance companies, and the government can manage the risk of losing rice yields
Mean-Variance portfolio optimization by using non constant mean and volatility based on the negative exponential utility function
No full textStock Investment Portfolio Optimization Using Mean-Variance Model Based on Stock Price Prediction with Long-Short Term Memory
No full textStock investment in the technology sector in Indonesia offers high potential returns. However, like any other investment instruments, the associated risks cannot be overlooked. Therefore, an appropriate portfolio optimization strategy is needed to enable investors to achieve optimal returns while managing risk. In this study, the author combines stock price prediction approaches with portfolio optimization methods to construct an efficient portfolio. The Long-Short Term Memory (LSTM) model is used to predict daily closing stock prices, with model performance evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. An optimal LSTM model is obtained with a batch size hyperparameter of 16 for ISAT, MTDL, MLPT, and EDGE stocks, and a batch size of 32 for DCII stock. For all stocks, the average prediction error from the actual values falls within the range of 1.53% ≤ MAPE ≤ 3.52%. The optimal portfolio is constructed using the Mean-Variance risk aversion model to maximize expected returns while considering risk. The resulting optimal portfolio composition consists of a weight allocation of 19.7% for ISAT stock, 36.8% for MTDL stock, 34.8% for MLPT stock, 3.6% for EDGE stock, and 15% for DCII stock. This portfolio yields an expected portfolio return of 0.001249 and a portfolio variance of 0.000311
Developing New Method in Measuring City Economic Resilience by Imposing Disturbances Factors and Unwanted Condition
No full textRecent research uses an index to measure economic resilience, but the index is inadequate because it is impossible to determine which disturbance factors have the greatest impact on the economic resilience of cities. This study aims to develop a new methodology to measure the economic resilience of a city by simultaneously examining unwanted conditions and disturbance factors. The ratio of regional original income to the number of poor people is known as Z and is identified as a measure of economic resilience in Indonesia. Resilience is measured by Z’s position in relation to the unwanted area following a specific level of disturbance. If Z is in the unwanted condition, the city’s per capita income will decrease, and the city will be considered economically not resilient. The results of the analysis show that six levels of economic resilience have been successfully distinguished based on research on 514 cities in Indonesia involving nine indicators of disturbance and one variable of economic resilience during the five-year observation period, 2015–2019. Only 3.11 percent of cities have economic resilience level 1, while 69.18 percent have level 0. Economically resilient cities consist of 4.24 percent of cities at level 2, as much as 3.39 percent at level 3, as much as 3.39 percent at level 4, and as much as 16.69 percent at level 5. The novelty of this research is to provide a new methodology for measuring the economic resilience of cities by integrating unwanted conditions as necessary conditions and disturbance factors as sufficient conditions. The measurement of a city’s economic resilience is critical to help the city government assess the security of the city so the government can take preventive actions to avoid the cities falling into unwanted conditions
Mean-variance portfolio optimization by using time series approaches based on logarithmic utility function
No full textEstimation model of life insurance claims risk for cancer patients by using Bayesian method
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