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All Journal JBIO: jurnal biosains (the journal of biosciences) CAUCHY: Jurnal Matematika Murni dan Aplikasi Althea Medical Journal Journal of the Indonesian Mathematical Society BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications Journal of Epidemiology and Public Health Jambura Journal of Mathematics M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika GREENOMIKA Jurnal Kesehatan Jurnal Pendidikan Administrasi Perkantoran (JPAP) Milang Journal of Mathematics and Its Applications Jurnal Indonesia Sosial Sains Proceeding Applied Business and Engineering Conference MILANG Journal of Mathematics and Its Applications

Retno Budiarti

Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Bogor Agricultural University, Jl. Meranti, Kampus IPB Darmaga, Bogor

Author-ID : 244060

Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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CONSISTENCY OF A KERNEL-TYPE ESTIMATOR OF THE INTENSITY OF THE CYCLIC POISSON PROCESS WITH THE LINEAR TREND Mangku, I Wayan; -, Siswadi; Budiarti, Retno
Journal of the Indonesian Mathematical Society Volume 15 Number 1 (April 2009)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.15.1.42.37-48

A consistent kernel-type nonparametric estimator of the intensity function of a cyclic Poisson process in the presence of linear trend is constructed and investigated. It is assumed that only a single realization of the Poisson process is observed in a bounded window. We prove that the proposed estimator is consistent when the size of the window in definitely expands.DOI :Â http://dx.doi.org/10.22342/jims.15.1.42.37-48

CONSISTENCY OF A KERNEL-TYPE ESTIMATOR OF THE INTENSITY OF THE CYCLIC POISSON PROCESS WITH THE LINEAR TREND I Wayan Mangku; Siswadi -; Retno Budiarti
Journal of the Indonesian Mathematical Society Volume 15 Number 1 (April 2009)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.15.1.42.37-48

Penentuan Harga Opsi Dengan Volatilitas Stokastik Menggunakan Metode Monte Carlo Chalimatusadiah Chalimatusadiah; Donny Citra Lesmana; Retno Budiarti
Jambura Journal of Mathematics Vol 3, No 1: January 2021
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

ABSTRAKHal yang utama dalam perdagangan opsi adalah penentuan harga jual opsi yang optimal. Namun pada kenyataan sebenarnya fluktuasi harga aset yang terjadi di pasar menandakan bahwa volatilitas dari harga aset tidaklah konstan, hal ini menyebabkan investor mengalami kesulitan dalam menentukan harga opsi yang optimal. Artikel ini membahas tentang penentuan harga opsi tipe Eropa yang optimal dengan volatilitas stokastik menggunakan metode Monte Carlo dan pengaruh harga saham awal, harga strike, dan waktu jatuh tempo terhadap harga opsi Eropa. Adapun model volatilitas stokastik yang digunakan dalam penelitian ini adalah model Heston, yang mengasumsikan bahwa proses harga saham (St) mengikuti distribusi log-normal, dan proses volatilitas saham (Vt) mengikuti Proses Cox-Ingersoll-Ross. Hal pertama yang dilakukan dalam penelitian ini adalah mengestimasi parameter model Heston untuk mendapatkan harga saham dengan menggunakan metode ordinary least square dan metode numerik Euler-Maruyama. Langkah kedua adalah melakukan estimasi harga saham untuk mendapatkan harga opsi tipe Eropa menggunakan metode Monte Carlo. Hasil dari penelitian ini menunjukkan bahwa penggunaan metode Monte Carlo dalam penentuan harga opsi tipe Eropa dengan volatilitas stokastik model Heston menghasilkan solusi yang cukup baik karena memiliki nilai error yang kecil dan akan konvergen ke solusi eksaknya dengan semakin banyak simulasi. Selain itu, simulasi Monte Carlo memberikan kesimpulan bahwa parameter harga strike, harga saham awal dan waktu jatuh tempo memiliki pengaruh terhadap harga opsi yang konsisten dengan teori harga opsi. ABSTRACTWhat is important in options trading is determining the optimal selling price. However, in real market conditions, fluctuations in asset prices that occur in the market indicate that the volatility of asset prices is not constant, this causes investors to experience difficulty in determining the optimal option price. This article discusses the optimal determination of the European type option price with stochastic volatility using the Monte Carlo method and the effect of the initial stock price, strike price, and expiration date on European option prices. The stochastic volatility model used in this study is the Heston model, which assumes that the stock price process (S) follows the normal log distribution, and the stock volatility process (V) follows the Ingersoll-Ross Cox Process. The first thing to do in this study is to estimate the parameters of the Heston model to get stock prices using the ordinary least square method and the Euler-Maruyama numerical method. The second step is to estimate the share price to get the European type option price using a Monte Carlo Simulation. This study indicates that using the Monte Carlo method in determining the price of European type options with the Heston model of stochastic volatility produces a fairly good solution because it has a small error value and will converge to the exact solution with more simulations. Also, the Monte Carlo simulation concludes that the parameters of the strike price, initial stock price, and maturity date influence the option price, which is consistent with the option price theory.

Perbandingan Analisis Regresi Linear dengan Analisis Regresi Copula pada Data Keuangan Alfi Khairiati; Retno Budiarti; I Gusti Putu Purnaba
Jambura Journal of Mathematics Vol 4, No 2: July 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Regression analysis is a statistical analysis to predict or explain the relationship between the response variable and one or more explanatory variables. The simplest and most commonly used regression analysis is linear regression. Copula regression is often used as an alternative method to overcome the problem of violating the assumptions of the linear regression model. The advantage of using copula regression is that the response variable does not have to follow a certain distribution and copula regression can explain nonlinear relationships. In this study, the copula used is the Gaussian copula and the student’s t copula. The main objective of this study is to compare the results of linear regression analysis with copula regression on financial data. In the linear regression method, the objective is to determine the estimated value of the response variable and to analyze the effect of macroeconomic factors on BMRI’s stock price. Meanwhile, copula regression was used to estimate the copula parameters using the Maximum Likelihood Estimation method and to determine the estimated value of the response variable using the Monte Carlo method. The measure of the accuracy of the model used is MAPE (Mean Absolute Percentage Error). This study uses financial data consisting of BMRI stock price data as response variables, as well as IHSG and the rupiah exchange rate as explanatory variables. The results showed that the MAPE values for linear regression and copula regression were small and not significantly different, meaning that both regressions were quite good in modeling financial data.

Pendugaan Imbal Hasil Saham dengan Model Autoregressive Moving Average Grifin Ryandi Egeten; Berlian Setiawaty; Retno Budiarti
Jambura Journal of Mathematics Vol 3, No 2: July 2021
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

ABSTRAKSeorang investor pada umumnya berharap untuk membeli suatu saham dengan harga yang rendah dan menjual saham tersebut dengan harga yang lebih tinggi untuk memperoleh imbal hasil yang tinggi. Namun, kapan waktu yang tepat melakukannya menjadi tantangan tersendiri bagi para investor. Oleh sebab itu, dibutuhkan suatu model yang mampu menduga imbal hasil saham dengan baik, salah satunya adalah model autoregressive moving average (ARMA). Tujuan dari penelitian ini adalah untuk menerapkan model autoregressive (AR), model moving average (MA), atau model autoregressive moving average (ARMA) pada data observasi untuk menduga imbal hasil saham bank central asia (BCA). Terdapat empat prosedur dalam membangun sebuah model AR, MA atau ARMA. Pertama, data yang digunakan harus weakly stationary. Kedua, orde dari model harus diidentifikasi untuk memperoleh model yang terbaik. Ketiga, parameter setiap model harus ditentukan. Keempat, kelayakan model harus diperiksa dengan melakukan analisis residual untuk memperoleh model yang terbaik. Pada akhirnya, model ARMA (1,1) adalah model terbaik dan akurat dalam menduga imbal hasil saham BCA. ABSTRACTGenerally, investor always wish to be able to buy a stock at a low price and sell it at a higher price to obtain high returns. However, when is the best time to buy or sell it is a challenge for investor. Therefore, proper models are needed to predict a stock return, one of them is autoregressive moving average (ARMA) model. The first purpose of this paper is to apply the autoregressive (AR), moving average (MA) or ARMA models to the observations to predict stock returns. There are four procedures which is used to build an AR, MA, or ARMA model. First, the observations must be weakly stationary. Second, the order of the models must be identified to obtain the best model. Third, the unknown parameters of the models are estimated by maximum likelihood. Fourth, through residual analysis, diagnostic checks are performed to determine the adequacy of the model. In this paper, stock returns of BCA are used as data observation. Finally, the ARMA (1,1) model is the best model and appropriate to predict the stock returns BCA in the future.

PENGARUH BETA TERHADAP RETURN SAHAM DEFENSIF DAN AGRESIF GUNA MEMBANTU INVESTOR DALAM KEPUTUSAN INVESTASI Helynda Mulya Arga Retha; Retno Budiarti
GREENOMIKA Vol. 3 No. 2 (2021): GREENOMIKA
Publisher : Universitas Nahdlatul Ulama Sidoarjo

The capital market is a place for someone to invest in financial assets, one of which is equity instruments (stocks). Investment activities are very useful to get the maximum return, but with appropriate and acceptable risk. Stocks in this study are divided into defensive and aggressive stocks which are classified according to the level of risk. This risk must be considered by investors before investing. The parameter associated with systematic risk is called beta. The objects of research in this paper are 20 stocks consisting of ANTM, BBCA, BKSL, CPIN, GGRM, ICBP, KLBF, TLKM, PTBA, UNVR, ADHI, ADRO, AKRA, ASII, BBKP, BRPT, BMRI, CTRA, EXCL, and WIKA. The research method used is regression analysis to determine the beta value of stocks to be classified as defensive or aggressive stocks. At the end of the study, the effect of stock beta on return has an effect of 61.9%. While the other 38.1% are influenced by factors outside the independent variables.

Pengaruh Inflasi terhadap Strategi Optimal Investasi dan Konsumsi dengan Model Stokastik Dara Irsalina; Retno Budiarti; I Gusti Putu Purnaba
Limits: Journal of Mathematics and Its Applications Vol 19, No 1 (2022)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v19i1.9987

The aim of this study is to investigate an optimal investment-consumption strategy under inflation rate with interest rate is described by Cox-Ingersol-Ross (CIR) model and volatility of the stock price is defined by Heston’s volatility model. A dynamic programming principle is used to obtain a Hamilton Jacobi Bellman (HJB) equation for the value function and choose a power utility function as utility function. The explicit solution of optimal investment and consumption are acquired with using separate variable and approach variable technique. The parameter’s values are approached by Euler-Maruyama method and Ordinary Least Square (OLS) method. Assumed that the portfolio of the investor contains a risk-free asset and a risk asset. Monthly historical data of TLK stock is used as risk asset and monthly historical data of BI 7-Day (Reverse) Repo Rate (BI7DRR) is used as risk-free asset, we obtain that the proportion of investment in stock is directly proportional to return of stock and the inflation rate does not have an impact on proportion investment in the stock. Meanwhile the optimal consumption of wealth is directly proportional to investor’s wealth and inversely proportional with inflation rate, which is the investor should consume less money of his wealth when the inflation rate increases.

Perbandingan Estimasi Parameter Metode Bayesian Self dengan Prior Vague dan Uniform Pada Model Survival Berdistribusi Rayleigh Asri Rahmawati; Retno Budiarti
Jurnal Indonesia Sosial Sains Vol. 2 No. 03 (2021): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Analysis survival is an analysis that is used to analyze the survival time (survivaltime). The survival (survivaltime)is one of the studies used to calculate the time from symptom onset and with the advent of the incident. Inanalysis, the survival termdata is known survival , which is data that shows the time an individual can survive until an event occurs. This study aims to determine the parameter estimation of themodels survival Rayleigh distribution ofusing the Bayesian SELF method. In addition, this study will also discuss the comparison of parameter estimates for themodels survival Rayleigh distribution ofwith the Bayesian SELF method using prior Vague and Uniform. The result of parameter estimation requires information from thefunction likelihood and prior distribution which will then form the posterior distribution. The posterior distribution is the basis for obtaining a Bayesian estimate. After the estimator on the Bayesian SELF method is obtained, then the estimator will be applied to the data from the Stanford heart transplant program from October 1967 to February 1980. Based on the MSE value obtained from this study, the Bayesian SELF method with prior vague is better than the Bayesian method. SELF with Prior uniform.

Analisis Return Metode Dogs Of The Dow dengan Uji-T Pada IDXHIDIV20 Helynda Mulya Arga Retha; Retno Budiarti
ABEC Indonesia Vol. 10 (2022): 10th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

All investors should strive to beat the market, that is, get a higher return than the market return, which is usually represented by an index. Composite Stock Price Index is the name of the stock market index in Indonesia (IHSG). The "Dogs of the Dow" investment strategy steers the portfolio toward high-yield investments in an attempt to outperform the Dow Jones Industrial Average (DJIA) every year. The overall idea is to invest money in the top 10 stocks among the 30 DJIA components with the largest dividend yields. A company's ability to pay dividends provides insight into its value. Testing Dogs of The Dow begins with the selection of stocks with the highest dividend distribution. The author uses stock data recorded on (IDX High Dividend 20) which is accessed from idx.co.id. The author takes stock samples from this index because all of its stocks have distributed cash dividends every year for the last 3 years and have daily trading values in the regular market at least IDR 1 billion for the last 3 months, 6 months and 12 months. The Dogs of the dows strategy had pretty good results. This can be seen from the results of the average return calculation, where 8 of the 11 Dogs of the dows strategy data were able to beat the market returns, namely the JCI, namely in 2011, 2013, 2014, 2016, 2017, 2018, 2020, and 2021. in 2011 the average market return was at 1.16% while the Dogs of the dow strategy had a return of 1.34%. The biggest return was in 2016, where the Dogs of the dow strategy was able to reach 4.95%, while the average return from the JCI was only 1.30%.

PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLAR SINGAPURA, BAHT, DAN PESO MENGGUNAKAN METODE GSTAR Retno Budiarti; D. S. Rahmawati; Fendy Septyanto; I Gusti Putu Purnaba
MILANG Journal of Mathematics and Its Applications Vol. 20 No. 1 (2024): MILANG Journal of Mathematics and Its Applications
Publisher : Dept. of Mathematics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.20.1.1-13

The Generalized Space-Time Autoregressive (GSTAR) model is an extension of the Space-Time Autoregressive (STAR) model. The difference between the two models lies in the parameter assumptions. In the STAR model, the parameters are assumed to be independent of location, so this model is only suitable for data with homogeneous locations. Meanwhile in the GSTAR model, the parameters are assumed to change for each different location. This research aims to develop the best model for forecasting the Rupiah exchange rate against the Singapore Dollar, Thai Baht, and Philippine Peso. The appropriate model used for the Rupiah exchange rate data is the GSTAR(51)I(1) model. The weights used in this study are uniform location weights and inverse distance. The modeling results show that the best model is the model with inverse distance weighting, which has an MSE value of 371.8907 with MAPE values for each of the Rupiah exchange rate data against the Singapore Dollar, Thai Baht, and Philippine Peso of 0.3154214%, 0.8369436%, and 0.6237245%, respectively.

Co-Authors Ade Irawan Adriani, Ika Reskiana Aldinasyah, Monicha Zalzabilla Anang Kistyanto Andre Dwijanto Witjaksono Asri Rahmawati Berlian Setiawaty Bib Paruhum Silalahi Chalimatusadiah Chalimatusadiah D. S. Rahmawati Daniel Happy Putra Dara Irsalina Dewi, Komang Nonik Afsari Diana, Adawiyah Putri Donny Citra Lesmana Dwi Fidiana Endar Hasafah Nugrahani Erliana, Windiani Fendy Septyanto Garianto, Efyluk Grifin Ryandi Egeten Hadi Sumarno Helynda Mulya Arga Retha Helynda Mulya Arga Retha Hemarani, Febri Herdiyantini, Mita I Gusti Putu Purnaba I Wayan Mangku Intansari, Kumala Khairiati, Alfi Khan, Joan Manjaruni, Vivin Aprilia Mohammad Reza Muhammad Yusuf Sulaiman Mulyasari, Rindi Melati Najib, Mohamad Khoirun Nashiruddin, Muhammad Abdurrasyid Nefertiti, Eva Nur Agustiani Parwati, Lusiana Sani Prasetya, Hanung Priambodo, Bima Putra, Hilman Yusupi Dwi Putra, Oki Nugraha Rafhida, Syukri Arif Rahadianto, Rahadianto Rahmah, Salsabilla Rahmawati, D. S. Rizki, Kurniadi Ronald Pratama Adiwinoto Ruhiyat Ruhiyat S. Soedarsono Setyawan, Binar Aulia Silalahi, Romaito Br siswadi - Sitepu, Sanfriska Br Sri Nurdiati Sudiarta, Ketut Edy Sulaiman, Muhammad Yusuf Titut Harnanik Utami, Nadila Rizqi Windiani Erliana Yulianty, Sherly

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