scholarly journals Modelling Market Volatility with Univariate GARCH Models: Evidence from Nasdaq-100

2019 ◽  
Author(s):  
Fuzuli Aliyev ◽  
Richard Ajayi ◽  
Nijat Gasim
Keyword(s):  
Market Volatility ◽  
Stock Index ◽  
Garch Models ◽  
Leverage Effect ◽  
Daily Data ◽  
Asymmetric Garch ◽  
The Impact

This paper models and estimates the volatility of nonfinancial, innovative and hi-tech focused stock index, the Nasdaq-100, using univariate symmetric and asymmetric GARCH models. We employ GARCH, EGARCH and GJR-GARCH using daily data over the period January 4, 2000 through March 19, 2019. We find that the volatility shocks on the index returns are quite persistent. Furthermore, our findings show that the index has leverage effect, and the impact of shocks is asymmetric, whereby the impacts of negative shocks on volatility are higher than those of positive shocks of the same magnitude.

scholarly journals Volatility of Regional Sharia Compliance Returns and US News Impact

2018 ◽  
Vol III (I) ◽  
pp. 294-307
Author(s):  
Kashif Hamid ◽  
Rana Shahid Imdad Akash ◽  
Muhammad Mudasar Ghafoor
Keyword(s):  
Bad News ◽  
Portfolio Diversification ◽  
Good News ◽  
Stock Index ◽  
Leverage Effect ◽  
Pricing Models ◽  
Stock Indices ◽  
Long Run ◽  
Daily Data ◽  
The Impact

Investigation of the impact of US News proxy on the returns of regional sharia compliance indices and volatility is the primary aim of this study. The daily data of Dow Jones Islamic index (DJII), Jakarata Islamic Index (JKII), Karachi Meezan Islamic Index (KMI) and Standard & Poor 500 stock index has been taken for the period of July 01, 2013 to June 30, 2018. GARCH (1,1) is extended with US News proxy for KMI, DJII and JKII. US news proxy identifies that leverage effect reveal the long run persistency in volatility. EGARCH (1,1) model indicates that higher volatility has bee also increased by bad news than good news due to leverage effect in sharia compliance returns. This study leads to extend various assets pricing models by modeling the volatility and will also inform the international and regional investors about the new trends of investment in Islamic stock indices and portfolio diversification.

scholarly journals Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2346
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
José Álvarez-García
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Garch Models ◽  
Algorithmic Trading ◽  
Stock Trading ◽  
Markov Switching Models ◽  
Switching Models ◽  
Asymmetric Garch ◽  
The Impact ◽  
T Distribution

In the present paper, we extend the current literature in algorithmic trading with Markov-switching models with generalized autoregressive conditional heteroskedastic (MS-GARCH) models. We performed this by using asymmetric log-likelihood functions (LLF) and variance models. From 2 January 2004 to 19 March 2021, we simulated 36 institutional investor’s portfolios. These used homogenous (either symmetric or asymmetric) Gaussian, Student’s t-distribution, or generalized error distribution (GED) and (symmetric or asymmetric) GARCH variance models. By including the impact of stock trading fees and taxes, we found that an institutional investor could outperform the S&P 500 stock index (SP500) if they used the suggested trading algorithm with symmetric homogeneous GED LLF and an asymmetric E-GARCH variance model. The trading algorithm had a simple rule, that is, to invest in the SP500 if the forecast probability of being in a calm or normal regime at t + 1 is higher than 50%. With this configuration in the MS-GARCH model, the simulated portfolios achieved a 324.43% accumulated return, of which the algorithm generated 168.48%. Our results contribute to the discussion on using MS-GARCH models in algorithmic trading with a combination of either symmetric or asymmetric pdfs and variance models.

scholarly journals HOW DID COVID-19 PANDEMIC AFFECT THE TOURISM INDEX IN BORSA ISTANBUL?

2021 ◽  
Author(s):  
Ercan Özen ◽  
Letife Özdemir
Keyword(s):  
Regression Model ◽  
Regression Method ◽  
Stock Index ◽  
Daily Data ◽  
Tourism Sector ◽  
The Impact ◽  
Bound Test ◽  
Cointegration Relationship ◽  
Ardl Bound Test

This study aims to investigate the impact of the Covid-19 pandemic on Turkey's tourism sector. In the study, for the period 12 March 2020 - 31 August 2020 the daily data of the BIST tourism stock index and Covid-19 case and death counts in Turkey were used. The cointegration relationship between the Covid-19 pandemic and the BIST tourism index was investigated with the ARDL bound test. In addition, the effect of the Covid-19 pandemic on the BIST tourism index was tested with the FMOLS regression method. As a result of the ARDL bound test, it was determined that there is a long-term cointegration relationship between the Covid-19 case and death numbers and the BIST tourism index. According to the FMOLS regression model results, it is seen that the deaths of Covid 19 significantly affect the tourism index. A 1% increase in the number of deaths causes the BIST tourism index to decrease by 0.08%. The coefficient of the number of Covid-19 cases is not significant, showing that the number of cases does not have a sufficient effect on the tourism index.

Re-Examining the Nifty Returns after the First Decade of Derivative Trading in Indian Capital Market Using Non-Linear Asymmetric GARCH Models

2012 ◽  
Vol 3 (4) ◽  
pp. 29-52 ◽  
Author(s):  
Sunita Narang
Keyword(s):  
Capital Market ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Indian Market ◽  
Total Period ◽  
Dummy Variable ◽  
Asymmetric Garch ◽  
Indian Stock Market ◽  
Surrogate Index ◽  
The Impact

This article examines the Indian stock market for conditional volatility using symmetric and asymmetric GARCH (Generalized Autoregressive Conditional Heteroskedasticity) variants with reference to a comprehensive period of 20 years from July 3, 1990 to November 30, 2010 using S&P CNX Nifty. The impact of future trading on Nifty return and volatility is assessed using dummy variable in total period and using Log (Open Interest of Nifty futures) in post-derivative period. Along with the period of two decades the analysis has also been done on a sub-period of a decade from 1995 to 2005 with NiftyJunior as surrogate index as it had no derivatives during this period. The results show that the PGARCH model is best suited to Indian market conditions.

scholarly journals Long-Term Effects of Expiration of Derivatives on Indian Spot Volatility

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Sunita Narang ◽  
Madhu Vij
Keyword(s):  
Stock Exchange ◽  
Volume Effect ◽  
Indian Market ◽  
Long Term Effects ◽  
Daily Data ◽  
Asymmetric Garch ◽  
Regression Techniques ◽  
The Impact ◽  
Spot Volatility

This paper examines the impact of expiration of derivatives on spot volatility of Indian capital market. The review of the literature shows that the previous Indian studies have covered a period of only 4–6 years after the introduction of derivative trading in India in 2000. They are unanimous about volume effect but not about return and volatility effect. This paper uses regression techniques and one symmetric and three asymmetric GARCH models, namely, TGARCH, EGARCH, and PGARCH, to evaluate the impact. It uses daily data on popular index S&P CNX Nifty of National Stock Exchange of India, during a period of more than a decade from June 12, 2000 to January 10, 2012. Findings of the study show that spot returns, volume, and volatility are high on expiration day and they build up further on the day after expiry which shows that the Indian market is weakly efficient. The expiration effect is mainly due to concentration of volumes in near-month contracts and absence of physical settlement.

scholarly journals Estimating stock market volatility using asymmetric GARCH models

2008 ◽  
Vol 18 (15) ◽  
pp. 1201-1208 ◽  
Author(s):  
Dima Alberg ◽  
Haim Shalit ◽  
Rami Yosef
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Garch Models ◽  
Stock Market Volatility ◽  
Asymmetric Garch

scholarly journals PERAMALAN VOLATILITAS SAHAM MENGGUNAKAN MODEL EXPONENTIAL GARCH DAN THRESHOLD GARCH

2019 ◽  
Vol 8 (4) ◽  
pp. 309
Author(s):  
SITI RAHAYU NINGSIH ◽  
I WAYAN SUMARJAYA ◽  
KARTIKA SARI
Keyword(s):  
Asset Returns ◽  
Financial Data ◽  
Stock Index ◽  
Garch Models ◽  
Leverage Effect ◽  
Financial Asset ◽  
Asymmetric Volatility ◽  
High Volatility ◽  
Financial Asset Returns ◽  
Forecasting Volatility

In financial data there is asymmetric volatility, which denotes the different movements on conditional volatility of increase and decrease financial asset returns. The exponential GARCH and threshold GARCH models can be used to capture asymmetric volatility, called leverage effect. The aim of this research is to determine the best model between exponential GARCH and threshold GARCH models, and to know the results of forecasting volatility the LQ-45 stock index using the best model. The research showed that the best model to predicting volatility is EGARCH(2,1), because it has the smallest AIC value compared to other models. Then forecasting volatility of the LQ-45 stock index using EGARCH(2,1) showed that volatility increase from the first period until fourteenth period, this means that it has high volatility.

scholarly journals Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 185
Author(s):  
Oscar V. De la Torre-Torres ◽  
Francisco Venegas-Martínez ◽  
Mᵃ Isabel Martínez-Torre-Enciso
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Garch Models ◽  
Portfolio Performance ◽  
Futures Trading ◽  
Volatility Index ◽  
Investment Level ◽  
Vix Futures ◽  
High Volatility ◽  
The Impact

In the present paper, we test the use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance the performance of a U.S. dollar-based portfolio that invest in the S&P 500 (SP500) stock index, the 3-month U.S. Treasury-bill (T-BILL) or the 1-month volatility index (VIX) futures. For the investment algorithm, we propose the use of two and three-regime, Gaussian and t-Student, MS and MS-GARCH models. This is done to forecast the probability of high volatility episodes in the SP500 and to determine the investment level in each asset. To test the algorithm, we simulated 8 portfolios that invested in these three assets, in a weekly basis from 23 December 2005 to 14 August 2020. Our results suggest that the use of MS and MS-GARCH models and VIX futures leads the simulated portfolio to outperform a buy and hold strategy in the SP500. Also, we found that this result holds only in high and extreme volatility periods. As a recommendation for practitioners, we found that our investment algorithm must be used only by institutional investors, given the impact of stock trading fees.

scholarly journals Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models

2007 ◽  
Vol 12 (2) ◽  
pp. 115-149
Author(s):  
G.R. Pasha ◽  
Tahira Qasim ◽  
Muhammad Aslam
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Garch Models ◽  
Financial Time ◽  
Daily Data ◽  
Asymmetric Garch ◽  
Gjr Model ◽  
Forecasting Volatility ◽  
Tail Distributions

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.

scholarly journals PERAMALAN VOLATILITAS RETURN SAHAM MENGGUNAKAN METODE ASYMMETRIC POWER ARCH (APARCH)

2020 ◽  
Vol 9 (3) ◽  
pp. 157
Author(s):  
JUITA HARYATI SIDADADOLOG ◽  
I WAYAN SUMARJAYA ◽  
NI KETUT TARI TASTRAWATI
Keyword(s):  
Stock Return ◽  
Bad News ◽  
Good News ◽  
Garch Models ◽  
Leverage Effect ◽  
Asymmetric Effect ◽  
Asymmetric Power ◽  
Asymmetric Garch ◽  
Effect Condition

Model APARCH is one of the asymmetric GARCH models. These models are able to capture the incidence of good news and bad news in the volatility. The APARCH model has an asymmetric coefficient to cope with leverage effect by modeling a leverage that has heteroscedasticity and asymmetric effect condition. The results of this research were obtained by the appropriate APARCH model. The model is the APARCH(1,2) model because all parameters are significant. Thus, proceeds from the volatility of stock return for the next 14 days with the model volatility APARCH(1,2) increased from period one to period fourteen.

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