scholarly journals Month of the Year Effect pada Pasar Obligasi di Indonesia

2017 ◽  
Vol 20 (2) ◽  
pp. 291
Author(s):  
Robiyanto Robiyanto
Keyword(s):  
Random Walk ◽  
Price Index ◽  
Weak Form ◽  
Bond Market ◽  
Conditional Heteroscedasticity ◽  
Year Effect ◽  
Analysis Technique ◽  
Bond Returns ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Positive Effect

<p><em>This study examines the month-of-the-year effect on the bond returns in Indonesia. I use the monthly closing price index (Indonesia Bond Indexes / INDOBeX) data for the periods of July 2003-July 2017 from Bloomberg. I then run the Generalize Autoregressive Conditional Heteroscedasticity (GARCH) analysis technique to analyze the data because the residuals exhibit a significant pattern of Autoregressive Conditional Heteroscedasticity (ARCH). The results show that only the month of July has a significantly positive effect on the bond returns; indicating that there is the month-of-the-year effect in the Indonesian bond market. Further, these also imply that the Indonesian bond market does not exhibit a random walk pattern and consequently they are inefficient in the weak form.</em></p><p><em><br /></em>Abstrak</p><p>Penelitian ini menguji pengaruh bulan-bulan perdagangan (month of the year) terhadap return obligasi di Indonesia. Data yang dipergunakan dalam penelitian ini adalah data indeks harga obligasi (Indonesia Bond Indexes / INDOBeX) penutupan bulanan selama periode Juli 2003 hingga Juli 2017 yang diperoleh dari Bloomberg. Analisis data dilakukan dengan menggunakan teknik analisis Generalize Autoregressive Conditional Heteroscedasticity (<em>GARCH</em>) karena pola residual yang dihasilkan menunjukkan adanya pola Autoregressive Conditional Heteroscedasticity (<em>ARCH</em>) yang signifikan. Hasil penelitian ini menunjukan bahwa bulan Juli memiliki pengaruh positif yang signifikan terhadap return obligasi di Indonesia. Sementara bulan-bulan lainnya tidak memiliki pengaruh terhadap return obligasi di Indonesia. Hasil ini menunjukkan bahwa terjadi month of the year effect di pasar obligasi di Indonesia. Temuan ini memiliki implikasi bahwa pasar obligasi di Indonesia tidak berjalan acak (random walk) sehingga tidak efisien dalam bentuk lemah.</p>

scholarly journals ARCH GARCH METHOD OF FORECASTING CONSUMER PRICE INDEX (CPI) IN SEMARANG

2020 ◽  
Vol 1 (1) ◽  
pp. 14-22
Author(s):  
Sri Kustiara ◽  
Indah Manfaati Nur ◽  
Tiani Wahyu Utami
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Garch Model ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

Indeks Harga Konsumen (IHK) merupakan salah satu indikator ekonomi penting yang dapat memberikan informasi mengenai perkembangan harga barang/jasa yang dibayar oleh konsumen di suatu wilayah. Penghitungan IHK ditujukan untuk mengetahui perubahan harga dari sekelompok tetap barang atau jasa yang umumnya dikonsumsi oleh masyarakat setempat. Dalam metode yang digunakan dalam pemodelan data runtun waktu memiliki syarat khusus yaitu yang  teridentifikasi efek heteroskedastisitas. Tujuan dari penelitian ini adalah untuk mengetahui model terbaik peramalan periode berikutnya serta hasil prediksi periode mendatang. Variabel yang digunakan adalah data Indeks Harga Konsumen dalam bulan. Sehingga untuk mengatasi permasalahan pada data penelitian ini digunakan metode Autoregressive Conditional Heteroscedasticity Generalized Autoregressive Conditional Heteroscedasticity (ARCH GARCH). Hasil dari penelitian ini didapatkan metode ARCH GARCH model terbaik yang digunakan adalah ARIMA (1,1,1)~GARCH (1,0). Dengan prediksi dari volatilitas dengan nilai standar deviasi 0.98283514 diperoleh prediksi volatilitas terendah sebesar 0.9632546 dan prediksi volatilitas tertinggi sebesar 0.9980155.

scholarly journals KORELASI DINAMIS ANTARA PERGERAKAN HARGA MINYAK DUNIA DAN INDEKS HARGA SAHAM SEKTORAL DI BURSA EFEK INDONESIA

2020 ◽  
Vol 9 (3) ◽  
pp. 188
Author(s):  
Yunita Dewi Safitri ◽  
Robiyanto Robiyanto
Keyword(s):  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
Secondary Data ◽  
Oil Prices ◽  
Dynamic Correlation ◽  
Analysis Technique ◽  
Static Approach ◽  
Stock Price Index ◽  
Autoregressive Conditional Heteroscedasticity

Changes in the situation that move very quickly on the commodity market have an impact on financial markets, one of which is the stock market in Indonesia. Therefore this study aims to examine the dynamic correlation between the movement of world oil prices and the Sectoral Stock Price Index listed on the Indonesia Stock Exchange (IDX). The data used is obtained from secondary data in the form of daily closing price data for world oil prices and Sectoral Stock Price Index from January 2017 to June 2020. The analysis technique used is Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH), due to previous studies mostly using a static approach. The results of this study show that the DCC-GARCH value between world oil prices (Brent and WTI) and Sectoral Stock Price Index tends to be very weak. A negative dynamic correlation was also found in the Consumer Goods Sector. This research can be a reference for investors who want to invest stocks in Indonesia by looking at the correlation between world oil prices and the Sectoral Stock Price Index.

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

Pengaruh Tingkat Inflasi, Tingkat Suku Bunga Bi, Dan Nilai Tukar Usd-Idr Terhadap Perubahan Harga Saham Sektor Perusahaan Manufaktur Di Indonesia

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Adi Cahya Stefanus ◽  
Robiyanto Robiyanto
Keyword(s):  
Exchange Rate ◽  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Manufacturing Sector ◽  
Analysis Method ◽  
Conditional Heteroscedasticity ◽  
Macroeconomic Factors ◽  
Stock Price Index ◽  
Autoregressive Conditional Heteroscedasticity

The objective of this study is to find out how macroeconomic factors such as exchange rate, BI rate and inflation rate can affect the manufacturing  sector stock price index in IDX from 2011 until 2018. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) is used as the analysis method in this research to find the fittest model. The result, only exchange rate that no significant effect to manufacturing sector stock, price index, Inflation and BI rate have significant effect to manufacturing sector stock price index.

Employee Engagement Dan Stress Kerja Pengaruhnya Terhadap Kepuasan Kerja Dan Turnover Intention di Aman Villas Nusa Dua – Bali

2020 ◽  
Vol 2 (1) ◽  
pp. 60-79
Author(s):  
Wayan Arya Paramarta ◽  
Ni Putu Kurnia Darmayanti
Keyword(s):  
Job Satisfaction ◽  
Employee Engagement ◽  
Work Stress ◽  
Turnover Intention ◽  
Secondary Data ◽  
Qualitative And Quantitative ◽  
Library Research ◽  
Analysis Technique ◽  
Negative Effect ◽  
Positive Effect

The aims of this study was to explain the effect of employee engagement and work stress on job satisfaction and turnover intention at Aman Villas Nusa Dua-Bali. The type of data used in this study is qualitative and quantitative data, with data sources namely primary and secondary data. Data collection method is interview, distributing questionnaires to respondents and library research, while the data analysis technique used Smart PLS 3.2.8. The results of this study showed that employee engagement had a positive effect and significant on job satisfaction, work stress had a negative effect but not significant on job satisfaction, employee engagement had a negative effect and significant on turnover intention, work stress had a positive effect and significant on turnover intention, job satisfaction had a negative effect but not significant on turnover intention, employee engagement had a positive effect but not significant on turnover intention trough job satisfaction, work stress had a positive effect but not significant on turnover intention trough job satisfaction at Aman Villas Nusa Dua-Bali.

Peranan Ketua Pengurus dan Manajer Dalam Penyusunan dan Penerapan Laporan Keuangan Berbasis SAK ETAP Pada Koperasi di Kota Palembang

2019 ◽  
Vol 14 (2) ◽  
pp. 119
Author(s):  
Riza Syahputera ◽  
Martha Rianty
Keyword(s):  
Data Analysis ◽  
Sampling Technique ◽  
Financial Statements ◽  
Significant Positive Effect ◽  
Analysis Technique ◽  
Regression Test ◽  
Using Data ◽  
The City ◽  
Positive Effect

AbstractThis study aims to determine the effect of the role of the Chairperson and Cooperative Manager in the preparation and application of Financial Statements based on SAK ETAP in cooperatives in the city of Palembang. This research is a quantitative study using data obtained from questionnaires and measured using a Likert scale. The sampling technique used is purposive sampling. The sample used in this study was the Chairperson of the cooperative and the manager of the cooperative in the city of Palembang. The cooperatives studied were 203 cooperatives. The data analysis technique used is multiple linear regression test. The results showed that the role of cooperative leaders and managers had a significant positive effect on the preparation and application of SAK ETAP-based financial statements.Keywords : chairman, manager, SAK ETAP, cooperative

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

2021 ◽  
pp. 002072092098849
Author(s):  
Xinzhe Yin ◽  
Jinghua Li
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Financial Market ◽  
Prediction Method ◽  
Good Effect ◽  
Trend Prediction ◽  
Bp Network ◽  
Conditional Heteroscedasticity ◽  
Bp Model ◽  
Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

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