scholarly journals Modelling volatility of Kuala Lumpur composite index (KLCI) using SV and garch models

2019 ◽  
Vol 13 (3) ◽  
pp. 1087
Author(s):  
Ezatul Akma Abdullah ◽  
Siti Meriam Zahari ◽  
S.Sarifah Radiah Shariff ◽  
Muhammad Asmu’i Abdul Rahim
Keyword(s):  
Stock Returns ◽  
Price Index ◽  
Financial Time Series ◽  
Composite Index ◽  
Garch Models ◽  
Financial Assets ◽  
Kuala Lumpur ◽  
Financial Decisions ◽  
Auto Regressive ◽  
Public Holidays

It is well-known that financial time series exhibits changing variance and this can have important consequences in formulating economic or financial decisions. In much recent evidence shows that volatility of financial assets is not constant, but rather that relatively volatile periods alternate with more tranquil ones. Thus, there are many opportunities to obtain forecasts of this time-varying risk. The paper presents the modelling volatility of the Kuala Lumpur Composite Index (KLCI) using SV and GARCH models.  Thus, the aim of this study is to model the KLCI stock market using two models; Stochastic Volatility (SV) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH). This study employs an SV model with Bayesian approach and Markov Chain Monte Carlo (MCMC) sampler; and GARCH model with MLE estimator. The best model will be used to forecast the future volatility of stock returns. The study involves 971 daily observations of KLCI Closing price index, from 2 January 2008 to 10 November 2016, excluding public holidays. SV model is found to be the best based on the lowest RMSE and MAE values.

scholarly journals The Phenomenon of the Month of Sela in the Indonesian Capital Market

2020 ◽  
Vol 12 (1) ◽  
pp. 65
Author(s):  
Arini Putri Helanda ◽  
Ani Wilujeng Suryani
Keyword(s):  
Stock Returns ◽  
Capital Market ◽  
Price Index ◽  
Composite Index ◽  
Abnormal Returns ◽  
Investment Decisions ◽  
Market Inefficiency ◽  
New Knowledge ◽  
The Mean ◽  
The Garch Model

Seasonal anomalies cause market inefficiency by affecting the mean and volatility of stock returns, and allow investors to obtain abnormal returns. In Indonesia, there is the month of Sela which is believed as an unlucky month so that many people avoid this month to hold ceremonial activities. As a result, the economy declines in the month of Sela and possibly, the return will also drop in this month. Therefore, this research aims to reveal whether the month of Sela is a seasonal anomaly. This research tested two hypotheses; the effect of the mean and volatility of price index return by using the GARCH model. To examine the effect of the month of Sela on the mean and volatility of return of price index, we collected the data on Indonesian Composite Index and 10 sectoral indices from 2009 to 2019 on three Javanese months, Sawal, Selo and Besar. In total, we collected 7.095 returns data. The month of Sela was a seasonal anomaly that the average and volatility of returns during the month of Sela were lower than those during the months of Sawal and Besar. These results also indicated that during the months of Sawal and Besar, the price index was more volatile than it was during the month of Sela. This research is useful for investors in considering their investment decisions to obtain an abnormal return. This research also contributes to the literature by adding new knowledge about seasonal anomalies that exist in Indonesia.

scholarly journals Application of ARIMA-GARCH Model for Prediction of Indonesian Crude Oil Prices

2020 ◽  
Vol 1 (1) ◽  
pp. 25-33
Author(s):  
Sukono Sukono ◽  
Emah Suryamah ◽  
Fujika Novinta S
Keyword(s):  
Crude Oil ◽  
Stock Price ◽  
Price Index ◽  
Moving Average ◽  
Oil Prices ◽  
Garch Models ◽  
Crude Oil Price ◽  
Price Forecast ◽  
Crude Oil Prices ◽  
Auto Regressive

Crude oil is one of the most important energy commodities for various sectors. Changes in crude oil prices will have an impact on oil-related sectors, and even on the stock price index. Therefore, the prediction of crude oil prices needs to be done to avoid the future prices of these non-renewable natural resources to increase dramatically. In this paper, the prediction of crude oil prices is carried out using the Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) models. The data used for forecasting are Indonesian Crude Price (ICP) crude oil data for the period January 2005 to November 2012. The results show that the data analyzed follows the ARIMA(1,2,1)-GARCH(0,3) model, and the crude oil price forecast for December 2012 is 105.5528 USD per barrel. The prediction results of crude oil prices are expected to be important information for all sectors related to crude oil.

scholarly journals Pengaruh Berbagai Indeks Saham Asia terhadap Indeks Harga Saham Gabungan Tahun 2015-2019

2020 ◽  
Vol 2 (4) ◽  
pp. 1096
Author(s):  
Vincent Hartantio ◽  
Yusbardini Yusbardini
Keyword(s):  
Regression Analysis ◽  
Unit Root ◽  
Stock Price ◽  
Price Index ◽  
Composite Index ◽  
Unit Root Test ◽  
Kuala Lumpur ◽  
Monthly Data ◽  
Integration Test ◽  
Stock Price Index

The purpose of this study is to analyze the effect of Nikkei 225 Index, Strait Times Index, Kuala Lumpur Composite Index, Hang Seng Index and Korean Composite Stock Price Index against Jakarta Composite Index (JCI) during the observed period from 2015-2019. The analytical method used in this study are unit root test, classic assumption test ,co-integration test and multiple regression analysis performed with E-views 9.0. This research used monthly data from 2015 – 2019 for each variable. This research analyzing the influence of Nikkei 225 Index, Strait Times Index, Kuala Lumpur Composite Index, Hang Seng Index and Korean Composite Stock Price Index toward Jakarta Composite Index simultaneously and partially. The result of the study shows that simultaneously Nikkei 225 Index, Strait Times Index, Kuala Lumpur Composite Index, Hang Seng Index and Korean Composite Stock Price Index has significant effect on Jakarta Composite Index. Tujuan penelitian ini adalah untuk mengetahui pengaruh dari Indeks Nikkei 225, Strait Times Index, Kuala Lumpur Composite Index (KLCI), Indeks Hang Seng, Korean Composite Stock Price Index (KOSPI) terhadap Indeks Harga Saham Gabungan pada periode 2015-2019. Metode analisis yang digunakan dalam penelitian ini adalah uji akar unit, uji asumsi klasik, uji kointegrasi, dan uji analisis regresi berganda yang menggunakan program E-views 9.0. Penelitian ini menggunakan data bulanan pada periode tahun 2015-2019 untuk setiap variabelnya. Penelitian ini menganalisi pengaruh Indeks Nikkei 225, Strait Times Index, Kuala Lumpur Composite Index (KLCI), Indeks Hang Seng, Korean Composite Stock Price Index (KOSPI) terhadap Indeks Harga Saham Gabungan secara bersama-sama dan sebagian. Hasil dari penelitian ini menunjukan bahwa pengaruh Indeks Nikkei 225, Strait Times Index, Kuala Lumpur Composite Index (KLCI), Indeks Hang Seng, Korean Composite Stock Price Index (KOSPI) saling mempengaruhi secara signifikan.

The money market sensitivity on the stock market

2006 ◽  
Vol 3 (2) ◽  
pp. 85
Author(s):  
Wan Mansor Wan Mahmood ◽  
Zetty Zahureen Mohd Yusoff
Keyword(s):  
Stock Returns ◽  
Composite Index ◽  
Asian Financial Crisis ◽  
Money Market ◽  
Kuala Lumpur ◽  
Correction Model ◽  
Monthly Data ◽  
Cointegration Tests ◽  
The Impact

This paper employs the cointegration tests and error correction model to investigate the impact ofeasing money market on stock returns in Malaysia following the Asian financial crisis during 1997 to 2000. The monthly data on Kuala Lumpur Interbank Offer Rates (KLIBOR), the monthly closing of Kuala Lumpur Composite Index (KLCI) andthe sector indexes - construction, consumer product, finance, industrial product, plantation, properties, mining, andtrading andservices, from January I, 1997 to December 31,2000 are used. The results suggest that there is long-term relationship between KLlBOR andsub sample 2, KLlBOR and constructions, KLlBOR and properties, and KLlBOR and mining.

scholarly journals Modelling and Estimation of Volatility Using ARCH/GARCH Models in Jordan’s Stock Market

2016 ◽  
Vol 8 (1) ◽  
pp. 152 ◽  
Author(s):  
Dana Mohammad AL-Najjar
Keyword(s):  
Stock Returns ◽  
Portfolio Management ◽  
Stock Exchange ◽  
Garch Models ◽  
Leverage Effect ◽  
Direct Role ◽  
Volatility Clustering ◽  
Financial Decisions ◽  
Stock Return Volatility ◽  
Amman Stock Exchange

<p>Financials have been concerned constantly with factors that have impact on both taking and assessing various financial decisions in firms. Hence modelling volatility in financial markets is one of the factors that have direct role and effect on pricing, risk and portfolio management. Therefore, this study aims to examine the volatility characteristics on Jordan’s capital market that include; clustering volatility, leptokurtosis, and leverage effect. This objective can be accomplished by selecting symmetric and asymmetric models from GARCH family models. This study applies; ARCH, GARCH, and EGARCH to investigate the behavior of stock return volatility for Amman Stock Exchange (ASE) covering the period from Jan. 1 2005 through Dec.31 2014. The main findings suggest that the symmetric ARCH /GARCH models can capture characteristics of ASE, and provide more evidence for both volatility clustering and leptokurtic, whereas EGARCH output reveals no support for the existence of leverage effect in the stock returns at Amman Stock Exchange.</p>

WHAT DRIVES HOUSE PRICE IN MALAYSIA? IN SEARCH OF AN ALTERNATIVE PRICING BENCHMARK FOR ISLAMIC HOME FINANCING

2017 ◽  
Vol 15 (4) ◽  
Author(s):  
Salina Hj Kassim ◽  
Nur Harena Redzuan ◽  
Nor Zalina Harun
Keyword(s):  
Interest Rate ◽  
Price Index ◽  
House Price ◽  
Holistic Approach ◽  
Housing Demand ◽  
House Price Index ◽  
Distributed Lag ◽  
Market Interest Rate ◽  
Auto Regressive ◽  
Conventional Banks

The current practise of the Islamic banks to rely on market interest rate as pricing benchmark for their home financing products has been a subject of intense debate among many parties. Muslim scholars have warned that it is highly discouraged as it could lead to a possible convergence between the practices of the Islamic and conventional banks. This paper intends to address the financing issues in the discussion of human settlement or housing policy by presenting the determinants for house price index as well as looking into the possibility of adopting the House Price Index (HPI) to replace the market interest rate as a pricing benchmark for the Islamic home financing. The study applies Auto-Regressive Distributed Lag (ARDL) method on a model comprising HPI as the dependent variable and a set of independent variables consisting of economic, housing demand and housing supply factors. The findings lead to the formulation of recommendations as a way forward for the Islamic banking industry in particular, and the economy in general. This will require a paradigm shift from basic financing products to a more holistic approach which integrates supply of housing factors, as well as urban planning and urban finance, with human rights and recognizes the need to place and shelter people.

Learning for infinitely divisible GARCH models in option pricing

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Fumin Zhu ◽  
Michele Leonardo Bianchi ◽  
Young Shin Kim ◽  
Frank J. Fabozzi ◽  
Hengyu Wu
Keyword(s):  
Option Pricing ◽  
Stock Returns ◽  
Bayesian Learning ◽  
Space Structure ◽  
Estimation Methods ◽  
Garch Models ◽  
Learning Approaches ◽  
Infinitely Divisible ◽  
Asymmetric Garch ◽  
Non Gaussian

AbstractThis paper studies the option valuation problem of non-Gaussian and asymmetric GARCH models from a state-space structure perspective. Assuming innovations following an infinitely divisible distribution, we apply different estimation methods including filtering and learning approaches. We then investigate the performance in pricing S&P 500 index short-term options after obtaining a proper change of measure. We find that the sequential Bayesian learning approach (SBLA) significantly and robustly decreases the option pricing errors. Our theoretical and empirical findings also suggest that, when stock returns are non-Gaussian distributed, their innovations under the risk-neutral measure may present more non-normality, exhibit higher volatility, and have a stronger leverage effect than under the physical measure.

scholarly journals Options trading strategy based on ARIMA forecasting

2020 ◽  
Vol 4 (2) ◽  
pp. 111-127
Author(s):  
Pierre Rostan ◽  
Alexandra Rostan ◽  
Mohammad Nurunnabi
Keyword(s):  
Composite Index ◽  
Garch Model ◽  
Trading Strategy ◽  
Credit Crisis ◽  
Strike Price ◽  
Index Options ◽  
Content Type ◽  
Option Trading ◽  
Auto Regressive ◽  
Options Trading

Purpose The purpose of this paper is to illustrate a profitable and original index options trading strategy. Design/methodology/approach The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented. Findings The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading. Originality/value The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.

MODELING THE INTERACTIONS OF STOCK PRICE AND EXCHANGE RATE IN MALAYSIA

2009 ◽  
Vol 54 (04) ◽  
pp. 605-619 ◽  
Author(s):  
MOHD TAHIR ISMAIL ◽  
ZAIDI BIN ISA
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Stock Prices ◽  
Stock Price ◽  
Price Index ◽  
Composite Index ◽  
Regime Shifts ◽  
Var Model ◽  
Vector Autoregressive ◽  
Stock Price Index

After the East Asian crisis in 1997, the issue of whether stock prices and exchange rates are related or not have received much attention. This is due to realization that during the crisis the countries affected saw turmoil in both their currencies and stock markets. This paper studies the non-linear interactions between stock price and exchange rate in Malaysia using a two regimes multivariate Markov switching vector autoregression (MS-VAR) model with regime shifts in both the mean and the variance. In the study, the Kuala Lumpur Composite Index (KLCI) and the exchange rates of Malaysia ringgit against four other countries namely the Singapore dollar, the Japanese yen, the British pound sterling and the Australian dollar between 1990 and 2005 are used. The empirical results show that all the series are not cointegrated but the MS-VAR model with two regimes manage to detect common regime shifts behavior in all the series. The estimated MS-VAR model reveals that as the stock price index falls the exchange rates depreciate and when the stock price index gains the exchange rates appreciate. In addition, the MS-VAR model fitted the data better than the linear vector autoregressive model (VAR).

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