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13 results for “GARCH model”
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Research ArticleOpen accessPhrase in the titleModelling and Forecasting Volatility of Returns on the Ghana Stock Exchange Using Garch ModelsFrimpong Joseph Magnus, Oteng-Abayie Eric FosuThis paper models and forecasts volatility (conditional variance) on the Ghana Stock Exchange using a random walk (RW), GARCH(1,1), EGARCH(1,1), and TGARCH(1,1) models. The unique ‘three days a week’ Databank Stock Index (DSI) was used to study the dynamics of the Ghana stock market volatility over a 10-year period. The competing volatility models were estimated and their specification and forecast … …American Journal of Applied Sciences, Vol. 3, Issue 10 · 31 Oct 2006 · 7,551 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Study on the Behavior of Volatility in Saudi Arabia Stock Market Using Symmetric and Asymmetric GARCH ModelsAjab Al Freedi, Ahmed Shamiri, Zaidi IsaProblem statement: This study examines several stylized facts (heavy-tailedness, leverage effect and persistence) in volatility of stock price returns exploiting symmetric and asymmetric GARCH family models for Saudi Arabia. Approach: This study is carried out using closing stock market prices over 15 years covering the period 1 January 1994 to 31 March 2009. The sample period is divided into three … …Journal of Mathematics and Statistics, Vol. 8, Issue 1 · 7 Feb 2012 · 7,577 accessesView PDFCite
Research ArticleOpen accessPhrase in the titlePrice Volatility of Grains: Relationship with Crude Oil Price Using CCC-Multivariate GARCH ModelAreerat Todsadee, Hiroshi Kameyama, Shoichi ItoAgricultural commodities prices have increased and become significantly more volatile during the past few years periods. The high agricultural commodity prices in recent years have raised the question of whether or not volatility is increasing and leading to more frequent extreme price swings. It is very important to quantify price variability of agricultural products. This paper measures the volatility of … …American Journal of Economics and Business Administration, Vol. 6, Issue 4 · 2 Jan 2015 · 6,640 accessesView PDFCite
Research ArticleOpen accessIn the keywordsForecasting the Stock Exchange of Thailand uses Day of the Week Effect and Markov Regime Switching GARCHP. Sattayatham, N. Sopipan, B. PremanodeProblem statement: We forecast return and volatility of the Stock Exchange of Thailand (SET) Index. Approach: In this study, we modeled the SET Index returns using mean equation with day of the week effect and autoregressive moving-average. Next we forecast the volatility of the SET Index by using the GARCH-type model and the Markov Regime Switching GARCH (MRS-GARCH) model. Results: … …American Journal of Economics and Business Administration, Vol. 4, Issue 1 · 15 Mar 2012 · 7,865 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSuitability of Volatility Models for Forecasting Stock Market Returns: A Study on the Indian National Stock ExchangeTrilochan Tripathy, Luis A. Gil-AlanaProblem statement: Measuring volatility is an important issue for stock market traders. Also, volatility has been used as a proxy for riskiness associated with the asset. This study aims to compare the different volatility models based on how well they model the volatility of the India NSE. Approach: The study has made use of five models which are Historical/Rolling Window … …American Journal of Applied Sciences, Vol. 7, Issue 11 · 8 Nov 2010 · 7,851 accessesView PDFCite
Research ArticleOpen accessIn the keywordsModeling and Forecasting Volatility of the Malaysian Stock MarketsAhmed Shamiri, Zaidi IsaProblem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for … …Journal of Mathematics and Statistics, Vol. 5, Issue 3 · 30 Sep 2009 · 7,900 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Artificial Neural Network Model for Option PricingHanningtone Meshack Simiyu, Anthony Gichuhi Waititu, Jane Aduda AkinyiIn the absence of a well-defined input selection technique associated with the pure ANN models, Option pricing using pure ANN models while relaxing the assumption of constant volatility remains a challenge. The conservative drill espoused has been to make allowances for a large number of input lags with the confidence that the ability of ANN to integrate suppleness and redundancy … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 8 Aug 2019 · 7,410 accessesView PDFCite
Research ArticleOpen accessIn the abstractRandom Forest Based Feature Selection of Macroeconomic Variables for Stock Market PredictionIsaac Kofi Nti, Adebayo Felix Adekoya, Benjamin Asubam WeyoriA firm’s equity price on the stock-market is reported to be closely related to the Macroeconomic Variable (MVs) of the country in which the firm trades. For this reason, researchers, market traders, financial analysts and forecasters to examine the association between MVs and stock-price have carried out numerous studies, using time-series statistical analysis methods like Autoregressive Integrated Moving Average (ARIMA), … …American Journal of Applied Sciences, Vol. 16, Issue 7 · 31 Jul 2019 · 10,234 accessesView PDFCite
Research ArticleOpen accessIn the abstractModeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity ProcessesAyodele Abraham Agboluaje, Suzilah bt Ismail, Chee Yin YipThis study has been able to reveal that the Combine White Noise model outperforms the existing Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Moving Average (MA) models in modeling the errors, that exhibits conditional heteroscedasticity and leverage effect. MA process cannot model the data that reveals conditional heteroscedasticity and GARCH cannot model the leverage effect also. The standardized residuals of GARCH … …American Journal of Applied Sciences, Vol. 12, Issue 11 · 20 Nov 2015 · 6,721 accessesView PDFCite
Research ArticleOpen accessIn the abstractDAILY CRUDE OIL PRICE FORECASTING MODEL USING ARIMA, GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTIC AND SUPPORT VECTOR MACHINESRana Abdullah Ahmed, Ani Bin ShabriCrude oil price forecasting is gaining increased interest globally. This interest is due mainly to the economic value attached to the product. For this reason, new forecasting methods are proposed in the literature. This paper proposes a novel technique for forecasting crude oil price based on Support Vector Machines (SVM). The study adopts the data on crude oil price of … …American Journal of Applied Sciences, Vol. 11, Issue 3 · 11 Jan 2014 · 8,725 accessesView PDFCite