Pub Date : 2023-08-23DOI: 10.24843/mtk.2023.v12.i03.p417
N. Kadek, Lani Pitrayani, K. Dharmawan, I. N. Widana
Options are used as a hedge against stock price uncertainty brought on by unstable stock prices fluctuation. The price of an option contract can be determined using a variety of approaches, one of which is the Variance Gamma. The purpose of this study is to compare the Black Scholes method with the Variance Gamma simulation model to determine the European call option contract price. The first thing that needs to be done is to figure out the moment variance gamma method. These parameters were used as initial values to get an idea of what the parameters that will be used in the simulation will be like. The European call option contract's price is calculated using the simulation results, which are then compared to the Variance Gamma simulation model and the Black Scholes model for the European call option contract. This study shows that the European call option contract's price, which was calculated using the Variance Gamma simulation, is less expensive than the Black Scholes contract's price.
{"title":"PENENTUAN KONTRAK OPSI TIPE EROPA MENGGUNAKAN MODEL SIMULASI VARIANCE GAMMA (VG)","authors":"N. Kadek, Lani Pitrayani, K. Dharmawan, I. N. Widana","doi":"10.24843/mtk.2023.v12.i03.p417","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i03.p417","url":null,"abstract":"Options are used as a hedge against stock price uncertainty brought on by unstable stock prices fluctuation. The price of an option contract can be determined using a variety of approaches, one of which is the Variance Gamma. The purpose of this study is to compare the Black Scholes method with the Variance Gamma simulation model to determine the European call option contract price. The first thing that needs to be done is to figure out the moment variance gamma method. These parameters were used as initial values to get an idea of what the parameters that will be used in the simulation will be like. The European call option contract's price is calculated using the simulation results, which are then compared to the Variance Gamma simulation model and the Black Scholes model for the European call option contract. This study shows that the European call option contract's price, which was calculated using the Variance Gamma simulation, is less expensive than the Black Scholes contract's price.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47492848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-23DOI: 10.24843/mtk.2023.v12.i03.p423
Yeky Abil Nizar, M. Susilawati, I. G. A. M. Srinadi
East Java Province is a province that experiences many flood disasters. Floods are natural disaster events that are generally affected by the inability of an area to accommodate high rainfall, where rainfall is different in each region. This study aims to determine models and factors that can significantly cause floods in East Java Province with predictable variables including population density, number of rainy days, rainfall, humidity, population growth rate and development land use. The regression method that is able to model cases with these conditions is Geographically Weighted Regression (GWR). Source of research data were obtained from the Central Statistic Agency, POWER Data Access Viewer and Ministry of Environment and Forestry. The best model can be shown by the coefficient of determination, where the GWR obtains a greater coefficient of determination, namely 65.37% compared to the coefficient of determination in linear regression, which is equal to 31.19%, and the coefficient of determination of SAR is 36.26%.
东爪哇省是一个经历过多次洪水灾害的省份。洪水是一种自然灾害事件，通常受一个地区无法容纳高降雨量的影响，每个地区的降雨量不同。本研究旨在通过可预测的变量，包括人口密度、雨天数量、降雨量、湿度、人口增长率和开发用地，确定可能在东爪哇省引发洪水的模型和因素。能够在这些条件下对案例进行建模的回归方法是地理加权回归（GWR）。研究数据来源于中央统计局、POWER data Access Viewer和环境与林业部。最佳模型可以用决定系数来表示，其中GWR获得了更大的决定系数，即65.37%，而线性回归中的决定系数等于31.19%，SAR的决定系数为36.26%。
{"title":"A PEMODELAN JUMLAH KEJADIAN BANJIR DI KABUPATEN DAN KOTA PROVINSI JAWA TIMUR DENGAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)","authors":"Yeky Abil Nizar, M. Susilawati, I. G. A. M. Srinadi","doi":"10.24843/mtk.2023.v12.i03.p423","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i03.p423","url":null,"abstract":"East Java Province is a province that experiences many flood disasters. Floods are natural disaster events that are generally affected by the inability of an area to accommodate high rainfall, where rainfall is different in each region. This study aims to determine models and factors that can significantly cause floods in East Java Province with predictable variables including population density, number of rainy days, rainfall, humidity, population growth rate and development land use. The regression method that is able to model cases with these conditions is Geographically Weighted Regression (GWR). Source of research data were obtained from the Central Statistic Agency, POWER Data Access Viewer and Ministry of Environment and Forestry. The best model can be shown by the coefficient of determination, where the GWR obtains a greater coefficient of determination, namely 65.37% compared to the coefficient of determination in linear regression, which is equal to 31.19%, and the coefficient of determination of SAR is 36.26%.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46993846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-23DOI: 10.24843/mtk.2023.v12.i03.p418
Delvi Amy Deska, Ketut Jayanegara, Desak Putu Eka Nilakusmawati
Transportation is a very important field of activities in the life of Indonesian people. Recognizing the importance role of transportation, traffic and road transportation must be organized in an integrated national transportation system and able to realize the availability of transportation services in accordance to the level of need. One of the most widely used transportation is ojek online. Of course, some factors become the influence of the use of online motorcycle taxis. One quantitative method that can measure customer perception using online motorcycle taxis is the Factor Analysis method, which is a statistical analysis used to find out the factors that underlie and show interrelationships between changemakers. Based on questionnaires distributed to 150 students in the FMIPA environment of Udayana University in 2021 and after an analysis of factors on questionnaire data, it was obtained that factors which influence students' decisions to use online motorcycle taxis are location and destination factors, service factors, application factors, and promotional factors. These factors can explain the diversity of students to use online motorcycle taxis by 34.666%; 28,897%; 22.563% and 10.873%. The dominant factor that mostly influence students' decision to use online motorcycle taxis is the factor of place and destination location that can be explained by 34.666%.
{"title":"FAKTOR-FAKTOR YANG MEMENGARUHI MAHASISWA DALAM MENGGUNAKAN OJEK ONLINE","authors":"Delvi Amy Deska, Ketut Jayanegara, Desak Putu Eka Nilakusmawati","doi":"10.24843/mtk.2023.v12.i03.p418","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i03.p418","url":null,"abstract":"Transportation is a very important field of activities in the life of Indonesian people. Recognizing the importance role of transportation, traffic and road transportation must be organized in an integrated national transportation system and able to realize the availability of transportation services in accordance to the level of need. One of the most widely used transportation is ojek online. Of course, some factors become the influence of the use of online motorcycle taxis. One quantitative method that can measure customer perception using online motorcycle taxis is the Factor Analysis method, which is a statistical analysis used to find out the factors that underlie and show interrelationships between changemakers. Based on questionnaires distributed to 150 students in the FMIPA environment of Udayana University in 2021 and after an analysis of factors on questionnaire data, it was obtained that factors which influence students' decisions to use online motorcycle taxis are location and destination factors, service factors, application factors, and promotional factors. These factors can explain the diversity of students to use online motorcycle taxis by 34.666%; 28,897%; 22.563% and 10.873%. The dominant factor that mostly influence students' decision to use online motorcycle taxis is the factor of place and destination location that can be explained by 34.666%.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48232377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.24843/mtk.2023.v12.i02.p410
Ni Ketut, Zelina Yeriska, Gusti Ayu Made Srinadi, I. Komang, Gde Sukarsa
The parameter estimator on the regression model can be obtained through the ordinary least square (OLS). When there are outliers in the data, OLS cannot be applied because it will produce an unbiased estimator that is not the best linear estimator. Another alternative to addressing the presence of outlier data without deleting the data is robust regression. Robust regression methods include the minimum covariance determinant (MCD) and the TELBS method. This study aims to determine the estimation of regression parameters produced using the MCD and TELBS methods when entering outlier data. The data used are simulation data with various levels of outliers, namely 5%, 10%, and 20%. The outliers inserted are the outliers on variable X, variable Y, and variables X and Y. The result of this study is that the robust regression methods of MCD and TELBS both produce unbiased parameter estimators when there are outlier data.
{"title":"PENDUGAAN PARAMETER REGRESI ROBUST METODE MINIMUM COVARIANCE DETERMINANT DAN METODE TELBS","authors":"Ni Ketut, Zelina Yeriska, Gusti Ayu Made Srinadi, I. Komang, Gde Sukarsa","doi":"10.24843/mtk.2023.v12.i02.p410","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i02.p410","url":null,"abstract":"The parameter estimator on the regression model can be obtained through the ordinary least square (OLS). When there are outliers in the data, OLS cannot be applied because it will produce an unbiased estimator that is not the best linear estimator. Another alternative to addressing the presence of outlier data without deleting the data is robust regression. Robust regression methods include the minimum covariance determinant (MCD) and the TELBS method. This study aims to determine the estimation of regression parameters produced using the MCD and TELBS methods when entering outlier data. The data used are simulation data with various levels of outliers, namely 5%, 10%, and 20%. The outliers inserted are the outliers on variable X, variable Y, and variables X and Y. The result of this study is that the robust regression methods of MCD and TELBS both produce unbiased parameter estimators when there are outlier data.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48901420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.24843/mtk.2023.v12.i02.p412
I. Bayu, Sulaksana Putra, P. Eka, N. Kencana, Luh Putu, Ida Harini
This research aims to develop an artificial neural network-based estimation system to predict the suitable study program at Udayana University for high school students in Denpasar City based on their report cards. The research is divided into four stages: system overview, user interface design, implementation of the artificial neural network in the system, and system testing. System testing results on report card data for science and social science classes demonstrate that the developed model has good accuracy with an error rate below 7%
{"title":"PENERAPAN ARTIFICIAL NEURAL NETWORK UNTUK MENDUGA PROGRAM STUDI DI UNIVERSITAS UDAYANA BERDASARKAN NILAI RAPOR","authors":"I. Bayu, Sulaksana Putra, P. Eka, N. Kencana, Luh Putu, Ida Harini","doi":"10.24843/mtk.2023.v12.i02.p412","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i02.p412","url":null,"abstract":"This research aims to develop an artificial neural network-based estimation system to predict the suitable study program at Udayana University for high school students in Denpasar City based on their report cards. The research is divided into four stages: system overview, user interface design, implementation of the artificial neural network in the system, and system testing. System testing results on report card data for science and social science classes demonstrate that the developed model has good accuracy with an error rate below 7%","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49452309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.24843/mtk.2023.v12.i02.p405
Felina Chantika Putri, NI Luh Putu Suciptawati, M. Susilawati
Spatial regression is an extension of classical regression analysis by considering spatial elements of spatial elements. One of the model of spatial regressions is the Geographically Weighted Regression (GWR). In the analysis, the GWR method considers the differences in characteristics between regions (spatial heterogeneity). Diarrhea cases in toddlers can be modeled using the GWR model. This research aims to model and identify factors that significantly influence diarrhea cases in toddlers in each district in East Java Province in 2020 using GWR. There are two weighting functions used in this research that are fixed bisquare kernel and adaptive bisquare kernel. The results showed that the GWR model with the adaptive kernel bisquare weighting function was more suitable because it produced the highest 𝑅 2 value of 79.29%. The factors that have a significant effect in each district are different and the dominant factor is the provision of vitamin A to toddlers.
{"title":"IMPLEMENTASI METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) PADA KASUS DIARE BALITA DI PROVINSI JAWA TIMUR","authors":"Felina Chantika Putri, NI Luh Putu Suciptawati, M. Susilawati","doi":"10.24843/mtk.2023.v12.i02.p405","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i02.p405","url":null,"abstract":"Spatial regression is an extension of classical regression analysis by considering spatial elements of spatial elements. One of the model of spatial regressions is the Geographically Weighted Regression (GWR). In the analysis, the GWR method considers the differences in characteristics between regions (spatial heterogeneity). Diarrhea cases in toddlers can be modeled using the GWR model. This research aims to model and identify factors that significantly influence diarrhea cases in toddlers in each district in East Java Province in 2020 using GWR. There are two weighting functions used in this research that are fixed bisquare kernel and adaptive bisquare kernel. The results showed that the GWR model with the adaptive kernel bisquare weighting function was more suitable because it produced the highest 𝑅 2 value of 79.29%. The factors that have a significant effect in each district are different and the dominant factor is the provision of vitamin A to toddlers.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42626031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.24843/mtk.2023.v12.i02.p406
Hamita Hakmi, Komang Dharmawan, R. Widiastuti
The formation of an optimal portfolio can be done with the Safety First Criterion method which is based on down side risk, namely the risk of causing a loss. The purpose of this study is to determine the optimal portfolio using Safety First Criterion method. Safety first criteria for portfolio selection are concerned only with the risk of failing to achieve a criteria minimum target return or secure prespecified safety margins. There are three criteria for the Safety First, namely Roy, Kataoka and Telser criteria. The results of this study formed an optimal portfolio with different risk values the Roy criteria is 0.0486, Kataoka is 0.0487 and Telser is 0.0527. So that the best portfolio of the three criteria is Roy's criterion because it has the lowest risk value with expected return the same
{"title":"PENERAPAN METODE SAFETY FIRST CRITERION PADA SELEKSI SAHAM UNTUK PEMBENTUKAN PORTOFOLIO OPTIMAL","authors":"Hamita Hakmi, Komang Dharmawan, R. Widiastuti","doi":"10.24843/mtk.2023.v12.i02.p406","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i02.p406","url":null,"abstract":"The formation of an optimal portfolio can be done with the Safety First Criterion method which is based on down side risk, namely the risk of causing a loss. The purpose of this study is to determine the optimal portfolio using Safety First Criterion method. Safety first criteria for portfolio selection are concerned only with the risk of failing to achieve a criteria minimum target return or secure prespecified safety margins. There are three criteria for the Safety First, namely Roy, Kataoka and Telser criteria. The results of this study formed an optimal portfolio with different risk values the Roy criteria is 0.0486, Kataoka is 0.0487 and Telser is 0.0527. So that the best portfolio of the three criteria is Roy's criterion because it has the lowest risk value with expected return the same","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42539813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.24843/mtk.2023.v12.i02.p404
YR VaniaRiskasari, P. Eka, Nila Kencana, I. Komang, Gde Sukarsa
Cirrhosis is one type of liver disease and is caused by forming fibrosis so that changes the liver structure become abnormal. Based on the presence of ascites, varicose veins, and bleeding, cirrhosis is divided into four clinical stages. This study aims to find the best classification model of cirrhosis using the support vector machine (SVM). SVM is a supervised learning method that aims to find the hyperplane with the maximum margin. In this study, the resulted model useful for determining the cirrhosis’ stage from patients. The variables to classify are age, gender, ascites status, hepatomegaly status, spiders status, edema status, total bilirubin, total cholesterol, amount of albumin, amount of copper, alkaline phosphatase level test results, SGOT test results, amount of tryglycerides, amount of platelets, and prothrombin time. By applying radial basis function kernel, combination of parameter C and 𝛾 that gives the best accuracy is determined. The final model using SVM with parameters C = 1 and 𝛾 = 0,6 is the best model with the accuracy value of 67,86 percent.
{"title":"KLASIFIKASI PENYAKIT SIROSIS MENGGUNAKAN SUPPORT VECTOR MACHINE","authors":"YR VaniaRiskasari, P. Eka, Nila Kencana, I. Komang, Gde Sukarsa","doi":"10.24843/mtk.2023.v12.i02.p404","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i02.p404","url":null,"abstract":"Cirrhosis is one type of liver disease and is caused by forming fibrosis so that changes the liver structure become abnormal. Based on the presence of ascites, varicose veins, and bleeding, cirrhosis is divided into four clinical stages. This study aims to find the best classification model of cirrhosis using the support vector machine (SVM). SVM is a supervised learning method that aims to find the hyperplane with the maximum margin. In this study, the resulted model useful for determining the cirrhosis’ stage from patients. The variables to classify are age, gender, ascites status, hepatomegaly status, spiders status, edema status, total bilirubin, total cholesterol, amount of albumin, amount of copper, alkaline phosphatase level test results, SGOT test results, amount of tryglycerides, amount of platelets, and prothrombin time. By applying radial basis function kernel, combination of parameter C and 𝛾 that gives the best accuracy is determined. The final model using SVM with parameters C = 1 and 𝛾 = 0,6 is the best model with the accuracy value of 67,86 percent.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45709359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.24843/mtk.2023.v12.i02.p413
Emerald Diori Silaban, K. Dharmawan, Desak Putu Eka Nilakusmawati
Penelitian ini bertujuan untuk menghitung nilai beta dan ekspektasi return pada CAPM dengan menggunakan data historis dan menggunakan data dari simulasi Monte Carlo. Data yang digunakan dalam penelitian ini adalah data saham dari indeks infobank15. Model yang digunakan dalam penelitian ini adalah model ekuilibrium CAPM dan untuk mengestimasi harga saham penelitian ini menggunakan simulasi Monte Carlo. Hasil penelitian menunjukkan perhitungan beta menggunakan data historis dan data simulasi saham BBCA (0,91578 dan 0,89393), BBNI (2,10434 dan 2,28636), BBRI (1,42862 dan 1,43427), BMRI (1 ,28249 dan 1,37485), dan BBTN (2,49935 dan 2,75265). Dengan hasil tersebut saham BBCA defensif karena beta kurang dari satu dan empat saham lainnya agresif karena beta lebih dari satu. Hasil perhitungan expected return dengan menggunakan data historis dan data simulasi adalah BBCA (5,42% dan 5,28%), BBNI (6,46% dan 8,05%), BBRI (5,87% dan 6,36%) , BMRI (5,74% dan 6,24%), dan BBTN (6,81% dan 8,98%).
{"title":"ANALISIS KEPUTUSAN INVESTASI PADA SAHAM PERBANKAN MENGGUNAKAN CAPM DAN CAPM-MONTE CARLO","authors":"Emerald Diori Silaban, K. Dharmawan, Desak Putu Eka Nilakusmawati","doi":"10.24843/mtk.2023.v12.i02.p413","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i02.p413","url":null,"abstract":"Penelitian ini bertujuan untuk menghitung nilai beta dan ekspektasi return pada CAPM dengan menggunakan data historis dan menggunakan data dari simulasi Monte Carlo. Data yang digunakan dalam penelitian ini adalah data saham dari indeks infobank15. Model yang digunakan dalam penelitian ini adalah model ekuilibrium CAPM dan untuk mengestimasi harga saham penelitian ini menggunakan simulasi Monte Carlo. Hasil penelitian menunjukkan perhitungan beta menggunakan data historis dan data simulasi saham BBCA (0,91578 dan 0,89393), BBNI (2,10434 dan 2,28636), BBRI (1,42862 dan 1,43427), BMRI (1 ,28249 dan 1,37485), dan BBTN (2,49935 dan 2,75265). Dengan hasil tersebut saham BBCA defensif karena beta kurang dari satu dan empat saham lainnya agresif karena beta lebih dari satu. Hasil perhitungan expected return dengan menggunakan data historis dan data simulasi adalah BBCA (5,42% dan 5,28%), BBNI (6,46% dan 8,05%), BBRI (5,87% dan 6,36%) , BMRI (5,74% dan 6,24%), dan BBTN (6,81% dan 8,98%).","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45993846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.24843/mtk.2023.v12.i02.p414
I. W. R. A. Prayana, I. N. Widana, Desak Putu Eka Nilakusmawati
{"title":"ANALISIS PREMI BULANAN ASURANSI JIWA DWIGUNA POLIS PARTISIPASI MENGGUNAKAN SUKU BUNGA MODEL VASICEK","authors":"I. W. R. A. Prayana, I. N. Widana, Desak Putu Eka Nilakusmawati","doi":"10.24843/mtk.2023.v12.i02.p414","DOIUrl":"https://doi.org/10.24843/mtk.2023.v12.i02.p414","url":null,"abstract":"","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48827311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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