A study on volatility and return spillover of exchange-traded funds and their benchmark indices in India

2019 ◽  
Vol 46 (1) ◽  
pp. 19-39 ◽  
Author(s):  
Buvanesh Chandrasekaran ◽  
Rajesh H. Acharya
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Exchange Traded Funds ◽  
Volatility Spillover ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Practical Implications

Purpose The purpose of this paper is to empirically examine the volatility and return spillover between exchange-traded funds (ETFs) and their respective benchmark indices in India. The paper uses time series data which consist of equity ETF and respective index returns. Design/methodology/approach The study uses autoregressive moving average–generalized autoregressive conditional heteroscedasticity and autoregressive moving average–exponential generalized autoregressive conditional heteroscedasticity models. The study uses data from the inception date of each ETF to December 2016. Findings The findings of the paper confirm that there is unidirectional return spillover from the benchmark index to ETF returns in most of the ETFs. Furthermore, ETF and benchmark index return have volatility persistence and show the presence of asymmetric volatility wherein a negative news has more influence on volatility compared to a positive news. Finally, unlike unidirectional return spillover, there is a bidirectional volatility spillover between ETF and benchmark index return. Practical implications The study has several practical implications for investors and regulators. A positive daily mean return over a fairly long period of time indicates that the passive equity ETFs can be a viable long-term investment option for ordinary investors. A bidirectional volatility spillover between the ETFs and benchmark index returns calls for the attention of the market regulators to examine the reasons for the same. Originality/value ETFs have seen fast growth in the Indian market in recent years. The present study considers the longest period data possible.

Modeling Research on 1982-2000 NDVI Time Series Data of Chinese Different Vegetation Types Based on Autoregressive Moving Average Model

2014 ◽  
Vol 955-959 ◽  
pp. 863-868
Author(s):  
Rong Yu ◽  
Bo Feng Cai ◽  
Xiang Qin Su ◽  
Ya Zi He ◽  
Jing Yang
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Moving Average ◽  
Original Data ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Research Areas ◽  
Noaa Avhrr ◽  
Index Time Series

Vegetation index time series data modeling is widely used in many research areas, such as analysis of environmental change, estimation of crop yield, and the precision of the traditional vegetation index time series data fitting model is lower. This paper conducts the modeling with introducing the autoregressive moving average time series model, and using NOAA/AVHRR normalized differential vegetation index time series data, to estimate the errors of original data which are between under the situation that the parameters to be estimated are lesser, and on the basis gives the fitted equation to the six kinds of main land covers’ vegetation index time series data of Northeast China region.

scholarly journals Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Irwan Kasse ◽  
Andi Mariani ◽  
Serly Utari ◽  
Didiharyono D.
Keyword(s):  
Risk Analysis ◽  
Value At Risk ◽  
Time Series Data ◽  
Information Criterion ◽  
Series Data ◽  
Investment Risk ◽  
Conditional Heteroscedasticity ◽  
Additional Information ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity

Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.

Introduction to Time Series Analysis for Organizational Research

2016 ◽  
Vol 20 (1) ◽  
pp. 61-94 ◽  
Author(s):  
Andrew T. Jebb ◽  
Louis Tay
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Moving Average ◽  
Dynamic Change ◽  
Decomposition Methods ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Organizational Research ◽  
Series Analysis

Organizational science has increasingly recognized the need for integrating time into its theories. In parallel, innovations in longitudinal designs and analyses have allowed these theories to be tested. To promote these important advances, the current article introduces time series analysis for organizational research, a set of techniques that has proved essential in many disciplines for understanding dynamic change over time. We begin by describing the various characteristics and components of time series data. Second, we explicate how time series decomposition methods can be used to identify and partition these time series components. Third, we discuss periodogram and spectral analysis for analyzing cycles. Fourth, we discuss the issue of autocorrelation and how different structures of dependency can be identified using graphics and then modeled as autoregressive moving-average (ARMA) processes. Finally, we conclude by describing more time series patterns, the issue of data aggregation, and more sophisticated techniques that were not able to be given proper coverage. Illustrative examples based on topics relevant to organizational research are provided throughout, and a software tutorial in R for these analyses accompanies each section.

Peramalan Indeks Harga Saham dengan Autoregressive Moving Average Generelized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH)

2021 ◽  
Vol 1 (1) ◽  
pp. 7-12
Author(s):  
Nur Najmi Layla ◽  
Eti Kurniati ◽  
Didi Suhaedi
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Garch Model ◽  
Data Partitioning ◽  
Forecasting Model ◽  
Autoregressive Moving Average ◽  
Conditional Heteroscedasticity ◽  
Eligibility Requirements ◽  
The Garch Model ◽  
Autoregressive Conditional Heteroscedasticity

Abstract. The stock price index is the information the public needs to know the development of stock price movements. Stock price forecasting will provide a better basis for planning and decision making. The forecasting model that is often used to model financial and economic data is the Autoregressive Moving Average (ARMA). However, this model can only be used for data with the assumption of stationarity to variance (homoscedasticity), therefore an additional model is needed that can model data with heteroscedasticity conditions, namely the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. This study uses data partitioning in pre-pandemic conditions and during the pandemic, Insample data with pre-pandemic conditions and insample data during pandemic conditions. Based on the research results, the GARCH model (1,1) was obtained with the conditions before the pandemic and GARCH (1,2) during the pandemic condition. The forecasting model obtained has met the eligibility requirements of the GARCH model. If the forecasting model fulfills the eligibility requirements, then MAPE calculations are performed to see the accuracy of the forecasting model. And obtained MAPE in the conditions before the pandemic and during the pandemic in the very good category. Abstrak. Indeks harga saham merupakan informasi yang diperlukan masyarakat untuk mengetahui perkembangan pergerakan harga saham. Peramalan harga saham akan memberikan dasar yang lebih baik bagi perencanaan dan pengambilan keputusan. Model peramalan yang sering digunakan untuk memodelkan data keuangan dan ekonomi adalah Autoregrresive Moving Average (ARMA). Namun model tersebut hanya dapat digunakan untuk data dengan asumsi stasioneritas terhadap varian (homoskedastisitas), oleh karena itu diperlukan suatu model tambahan yang bisa memodelkan data dengan kondisi heteroskedastisitas, yaitu model Generalized Autoregressive Conditional Heteroscedastisity (GARCH). Penelitian ini menggunakan partisi data pada kondisi sebelum pandemi dan saat pandemi berlangsung data Insample dengan kondisi sebelum pandemi dan insample pada kondisi pandemi. Berdasarkan hasil penelitian, maka didapat model GARCH (1,1) dengan kondisi sebelum pandemi dan GARCH (1,2) saat kondisi pandemi. Model peramalan yang didapat sudah memenuhi syarat kelayakan model GARCH. Apabila model peramalan terpenuhi syarat kelayakannya maka dilakukan perhitungan MAPE untuk melihat keakuratan model peramalannya. Dan diperoleh MAPE pada kondisi sebelum pandemi dan saat pandemi dengan kategori sangat baik. 

Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model

2016 ◽  
Vol 23 (3) ◽  
pp. 302-322 ◽  
Author(s):  
Ka Chi Lam ◽  
Olalekan Shamsideen Oshodi
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Effective Response ◽  
Content Type ◽  
The Impact

Purpose – Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR). Design/methodology/approach – Four quarterly time-series data on the construction output of Hong Kong were collected (1983Q1-2014Q4). The collected data were divided into two parts. The first part was fitted to the model, while the other was used to evaluate the predictive accuracy of the developed models. Findings – The NNAR model can provide reliable and accurate forecast of total, private and “others” construction output for the medium term. In addition, the NNAR model outperforms the ARIMA model, in terms of accuracy. Research limitations/implications – The applicability of the NNAR model to the construction industry of other countries could be further explored. The main limitation of artificial intelligence models is the lack of explanatory capability. Practical implications – The NNAR model could be used as a tool for accurately predicting future patterns in construction output. This is vital for the sustained growth of the construction industry and the economy. Originality/value – This is the first study to apply the NNAR model to construction output forecasting research.

Long memory volatility in Asian stock markets

2017 ◽  
Vol 29 (3) ◽  
pp. 423-442 ◽  
Author(s):  
Geeta Duppati ◽  
Anoop S. Kumar ◽  
Frank Scrimgeour ◽  
Leon Li
Keyword(s):  
Long Memory ◽  
Moving Average ◽  
Realized Volatility ◽  
Theory And Practice ◽  
Long Term Memory ◽  
Conditional Heteroscedasticity ◽  
Term Memory ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity

Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory. Findings Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts. Practical implications The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management. Social implications It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks. Originality/value This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

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