Aplikasi metode markov switching autoregressive dalam memodelkan nilai indeks harga saham gabungan indonesia = Application of the markov switching autoregressive method in modelling the indonesia composite index

Angela, Millania (2021) Aplikasi metode markov switching autoregressive dalam memodelkan nilai indeks harga saham gabungan indonesia = Application of the markov switching autoregressive method in modelling the indonesia composite index. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Model Markov Switching Autoregressive (MSAR) adalah salah satu model yang dapat digunakan untuk menganalisis perubahan kondisi fluktuasi pada suatu data deret waktu. Indeks Harga Saham Gabungan (IHSG) adalah salah satu variabel untuk mengukur pertumbuhan pasar modal yang berfluktuasi sebagai akibat dari kondisi politik dan ekonomi. Tujuan dari penelitian ini adalah memodelkan IHSG dengan menggunakan metode MSAR dan memilih model MS(k)AR(p) terbaik yang akan digunakan untuk memprediksi data. Data deret waktu yang digunakan adalah dari 1 Januari 1998 sampai 1 Januari 2021. Analisis model menggunakan data yang dibagi menjadi dua bagian, yaitu data training dan data testing. Estimasi parameter dilakukan untuk 7 model MSAR. Pada model MSAR terdapat variabel state dan nilai peluang transisi dari satu state ke state lainnya yang dihitung menggunakan metode Maximum Likelihood Estimation. Ketujuh model dibandingkan, model dengan indikator Mean Square Error terkecil terpilih sebagai model terbaik yang digunakan untuk memprediksi data. Model terbaik yang diperoleh adalah MS(2)AR(4) dimana state terbagi menjadi dua kondisi, yaitu tren naik dan tren turun. Analisis hasil prediksi menunjukkan bahwa IHSG mengalami penurunan terbatas untuk dua belas bulan. Prediksi nilai IHSG tertinggi adalah sebesar 6531.757 dan yang paling rendah adalah sebesar 4962.092. / The Markov Switching Autoregressive (MSAR) model is a model that can be used to analyze changes in fluctuating conditions in time series data. The IDX Composite is one of the variables to measure the growth of capital market which fluctuates as a result of political and economic conditions. The purpose of this research is to model IDX composite using the MSAR method and choose the best MS(k)AR(p) model that will be used to forecast the data. In this study, the data used is IDX Composite which is taken on a monthly basis from January 1, 1998 to January 1, 2021. In the model analysis, data is divided into two parts, training data and testing data. Parameter estimation was calculated for 7 MSAR models. MSAR model consist of state variables and transition probability matrix which is calculated using the Maximum Likelihood Estimation method. The seven models were compared, the model with smallest Mean Square Error indicator was selected as the best model used to predict the data. The best model obtained is MS(2)AR(4), where the state is divided into 2 conditions, which is an uptrend and a downtrend. The analysis result shows that the IDX composite will have a downtrend movement for 12 months. The highest close price forecasting for the Jakarta Composite Index is 6531.757 and the lowest rate is 4962.092.

Item Type: Thesis (Bachelor)
Creators:
CreatorsNIMEmail
Angela, MillaniaNIM01112170027millaniangela7@gmail.com
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorSaputra, Kie Van IvankyNIDN0401038203kie.saputra@uph.edu
Thesis advisorSembiring, Ukur AriantoNIDN0311056901ukur.sembiring@uph.edu
Uncontrolled Keywords: Markov Switching Autoregressive; rantai markov; prediksi; matriks transisi; peluang
Subjects: Q Science > QA Mathematics
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Mathematics
Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Mathematics
Depositing User: Users 5999 not found.
Date Deposited: 03 Aug 2021 00:46
Last Modified: 23 Feb 2022 02:31
URI: http://repository.uph.edu/id/eprint/41080

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