scholarly journals Forecasting of the Nigeria Stock Returns Volatility Using GARCH Models with Structural Breaks

2020 ◽  
pp. 39-50
Author(s):  
Yakubu Musa ◽  
Ibrahim Adamu ◽  
Nasiru Sani Dauran
Keyword(s):  
Stock Returns ◽  
Structural Breaks ◽  
Structural Changes ◽  
Mean Reversion ◽  
Garch Models ◽  
Reversion Rate ◽  
Continuous Increase ◽  
Dummy Variable ◽  
Student T Distribution ◽  
T Distribution

This study examines the stock returns series using Symmetric and Asymmetric GARCH models with structural breaks in the presence of some varying distribution assumptions. Volatility models of Symmetric GARCH (1,1), Asymmetric Power GARCH (1,1) and GJR-GARCH(1,1) models were considered in estimating and measuring shock persistence,  leverage effects and mean reversion rate with structural breaks considering dummy variable  for these structural changes and varying distributions . The skewed student-t distribution is considered best distribution for the models; moreover findings showed the high persistence of shock in returns series for the estimated models. However, when structural breaks were incorporated in the estimated models by including dummy variable in the conditional variance equations of all the models, there was significant reduction of shock persistence parameter and mean reversion rate.  The study found the GJR-GARCH (1,1) with skewed student-t distribution best fit the series. The volatility was forecasted for 12 months period using GJR-GARCH (1,1) model and the values are compared with the actual values and the results indicates a continuous increase in unconditional variance.

scholarly journals VOLATILITY IN STOCK RETURN SERIES OF VIETNAM STOCK MARKET

2011 ◽  
Vol 14 (3) ◽  
pp. 5-21
Author(s):  
Vinh Xuan Vo ◽  
Ngan Thi Kim Nguyen
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Return ◽  
Structural Breaks ◽  
Garch Models ◽  
Financial Policies ◽  
Long Span ◽  
Return Variance ◽  
Daily Stock Returns ◽  
Icss Algorithm

This paper studies the features of the stock return volatility using GARCH models and the presence of structural breaks in return variance of VNIndex in the Vietnam stock market by using the iterated cumulative sums of squares (ICSS) algorithm. Using a long-span data, GARCH and GARCH in mean (GARCH-M) models seems to be effective in describing daily stock returns’ features. About structural breaks, when applying ICSS to standardized residuals filtered from GARCH (1, 1) model, the number of volatility shifts significantly decreases in comparison with the raw return series. Events corresponding to those breaks and altering the volatility pattern of stock return are found to be country-specific. Not any shifts are found during global crisis period. Further evidence also reveals that when sudden shifts are taken into account in the GARCH models, volatility persistence remarkably reduces and that the conditional variance of stock return is much affected by past trend of observed shocks and variance. Our results have important implications regarding advising investors on decisions concerning pricing equity, portfolio investment and management, hedging and forecasting. Moreover, it is also helpful for policy-makers in making and promulgating the financial policies.

DETECTING FOR SMOOTH STRUCTURAL CHANGES IN GARCH MODELS

2015 ◽  
Vol 32 (3) ◽  
pp. 740-791 ◽  
Author(s):  
Bin Chen ◽  
Yongmiao Hong
Keyword(s):  
Structural Breaks ◽  
Structural Changes ◽  
Garch Model ◽  
Parametric Bootstrap ◽  
Garch Models ◽  
Time Varying ◽  
Finite Samples ◽  
Time Varying Parameter ◽  
Break Points ◽  
Quasi Maximum Likelihood

Detecting and modeling structural changes in GARCH processes have attracted increasing attention in time series econometrics. In this paper, we propose a new approach to testing structural changes in GARCH models. The idea is to compare the log likelihood of a time-varying parameter GARCH model with that of a constant parameter GARCH model, where the time-varying GARCH parameters are estimated by a local quasi-maximum likelihood estimator (QMLE) and the constant GARCH parameters are estimated by a standard QMLE. The test does not require any prior information about the alternatives of structural changes. It has an asymptotic N(0,1) distribution under the null hypothesis of parameter constancy and is consistent against a vast class of smooth structural changes as well as abrupt structural breaks with possibly unknown break points. A consistent parametric bootstrap is employed to provide a reliable inference in finite samples and a simulation study highlights the merits of our test.

Oil Price Shocks and Sectoral Stocks Behaviour in Nigeria: How Relevant is Asymmetry and Structural Breaks?

2020 ◽  
Author(s):  
Ismail Fasanya ◽  
Oluwasegun B. Adekoya ◽  
Temitope F. Odudu
Keyword(s):  
Stock Returns ◽  
Structural Breaks ◽  
Structural Changes ◽  
Global Financial Crisis ◽  
Oil Price ◽  
Jel Codes ◽  
Monthly Data ◽  
The Global Financial Crisis ◽  
The Arab Spring ◽  
The Relationship

Abstract In this paper, we model the relationship between oil price and stock returns for selected sectors in Nigeria using monthly data from January 2007 to December 2016. We employ both the Linear (Symmetric) ARDL by Pesaran et al. (2001) and Nonlinear (Asymmetric) ARDL by Shin et al. (2014) and we also account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models. Our results indicate that the strength of this relationship varies across sectors, albeit asymmetric and breaks. We identify two structural breaks that occur in 2008 and 2010/2011 which coincidentally correspond to the global financial crisis and the Arab spring (Libyan shut-downs), respectively.Moreover, we observe strong supportfor asymmetry and structural breaks for some sectorsin the reaction of sector returns to movement in oil prices.These findings are robust and insensitive when considering different oil proxy.While further extensions can be pursued, the consideration of asymmetric effects as well as structural breaks should not be jettisoned when modelling this nexus.JEL codes: C22; C51; G12; Q43

Financial Time Series Analysis via Backtesting Approach

2020 ◽  
Author(s):  
Monday Osagie Adenomon
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stock Returns ◽  
Stock Price ◽  
Financial Time Series ◽  
Information Criteria ◽  
Financial Time ◽  
Student T Distribution ◽  
Bank Stock ◽  
T Distribution

This book chapter investigated the place of backtesting approach in financial time series analysis in choosing a reliable Generalized Auto-Regressive Conditional Heteroscedastic (GARCH) Model to analyze stock returns in Nigeria. To achieve this, The chapter used a secondary data that was collected from www.cashcraft.com under stock trend and analysis. Daily stock price was collected on Zenith bank stock price from October 21st 2004 to May 8th 2017. The chapter used nine different GARCH models (standard GARCH (sGARCH), Glosten-Jagannathan-Runkle GARCH (gjrGARCH), Exponential GARCH (Egarch), Integrated GARCH (iGARCH), Asymmetric Power Autoregressive Conditional Heteroskedasticity (ARCH) (apARCH), Threshold GARCH (TGARCH), Non-linear GARCH (NGARCH), Nonlinear (Asymmetric) GARCH (NAGARCH) and The Absolute Value GARCH (AVGARCH) with maximum lag of 2. Most the information criteria for the sGARCH model were not available due to lack of convergence. The lowest information criteria were associated with apARCH (2,2) with Student t-distribution followed by NGARCH(2,1) with skewed student t-distribution. The backtesting result of the apARCH (2,2) was not available while eGARCH(1,1) with Skewed student t-distribution, NGARCH(1,1), NGARCH(2,1), and TGARCH (2,1) failed the backtesting but eGARCH (1,1) with student t-distribution passed the backtesting approach. Therefore with the backtesting approach, eGARCH(1,1) with student distribution emerged the superior model for modeling Zenith Bank stock returns in Nigeria. This chapter recommended the backtesting approach to selecting reliable GARCH model.

scholarly journals Structural Breaks and Volatility Spillovers: The Case of the US and Canadian Stock Markets

2019 ◽  
Vol 11 (2) ◽  
pp. 30
Author(s):  
Chikashi Tsuji
Keyword(s):  
Stock Returns ◽  
Structural Breaks ◽  
Structural Break ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Dummy Variables ◽  
Volatility Spillovers ◽  
Dummy Variable ◽  
The Us ◽  
Parameter Values

This paper investigates the relations of structural breaks and volatility spillovers by using the US and Canadian stock return data. Specifically, applying spillover MGARCH models without and with structural break dummy variables to the two stock returns, this study derives the following interesting evidence. (1) First, we reveal that for both the US and Canadian stock returns, the volatility persistence parameter values in our spillover MGARCH models decline when structural break dummy variables are incorporated. (2) Second, we further clarify that when we do not take structural breaks into account, the spillover effect was unidirectional from Canada to the US. However, when we take structural breaks into consideration, the results from our spillover MGARCH model with structural break dummies demonstrate that the volatility spillover effects between the US and Canada become bidirectional. (3) Third, we furthermore reveal that around the Lehman Brothers bankruptcy in 2008, the time-varying volatilities derived from our spillover MGARCH model with structural break dummy variables show slightly higher values than those volatilities from our spillover MGARCH model with no structural break dummy variable.

scholarly journals A Seasonality in the Pakistani Equity Market: The Ramadhan Effect

1998 ◽  
Vol 37 (1) ◽  
pp. 77-81 ◽  
Author(s):  
Fazal Husain
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Economic Activity ◽  
Seasonal Pattern ◽  
Equity Market ◽  
Significant Decline ◽  
Garch Models ◽  
Dummy Variable ◽  
Religious Rituals ◽  
The Mean

This paper attempts to explore a seasonal pattern, the Ramadhan effect, in the Pakistani equity market. Ramadhan, the holy month of fasting, is expected to affect the behaviour of stock market in Pakistan where the environment in Ramadhan is different from other months as people devote more time to perform religious rituals and the general economic activity slows down. The effects of Ramadhan on mean return and stock returns volatility are examined by including a dummy variable in regressions and GARCH models respectively. The analysis indicates a significant decline in stock returns volatility in this month although the mean return indicates no significant change.

scholarly journals MEASURING VALUE AT RISK USING GARCH MODEL - EVIDENCE FROM THE CRYPTOCURRENCY MARKET

2021 ◽  
Vol 9 (2) ◽  
pp. 63-84
Author(s):  
Cosmos Obeng
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Information Communication ◽  
Managerial Decision ◽  
Fund Managers ◽  
Daily Data ◽  
Student T Distribution ◽  
Data Frequency ◽  
T Distribution

There is a growing interest in the activities of the crypto market by various stakeholders. These stakeholders generally include investors, entrepreneurs, governments, fund managers, climate activists, institutional managers, employees with surplus funds, and crypto miners. This study aims to investigate the accuracy of the GARCH models for measuring and estimating Value-at-risk (VaR) using the Cryptocurrency index for future investment and managerial decision making. Because of this, the present study uses the top 30 Cryptocurrencies index in terms of Market capitalization excluding stable coins to determine the best GARCH models. Many entrepreneurs, institutional managers, fund managers, and other stakeholders have recently included cryptocurrency in their investment portfolio because of the increase in transactions and high returns growth in the global financial market with its associated high returns and volatility. Information communication technology has paved the way for such activities in the global markets. The daily data frequency was applied because of the availability of the data. The empirical analysis has been carried out for the period from January 2017 to December 2020 for a total of 1461observation. The returns volatility is estimated using SGARCH and EGARCH models. The findings evidenced that, using both normal distribution and Student t distribution, EGARCH provides a better measure and estimate than SGARCH concerning high persistence and volatility. Against this background, the present study also examined Backtesting to estimate Value at Risk. Interestingly, the findings of the available study would provide industry players, practitioners, entrepreneurs, and investors the maximum edge on how to use or measure such variables against others to make investment decisions. Also, the findings would subsequently contribute more insight into academia on the study area.

scholarly journals Modeling the Exchange Rate Volatility Using the BRICS GARCH-type Models

2021 ◽  
Vol 12 (5) ◽  
pp. 166
Author(s):  
Lebotsa Daniel Metsileng ◽  
Ntebogang Dinah Moroke ◽  
Johannes Tshepiso Tsoku
Keyword(s):  
Financial Markets ◽  
Exchange Rates ◽  
Exchange Rate Volatility ◽  
Garch Models ◽  
Time Series Models ◽  
Univariate Time Series ◽  
Student T Distribution ◽  
Different Types ◽  
Exchange Rates Volatility ◽  
T Distribution

The paper models the performance of GARCH-type models on BRICS exchange rates volatility. The levels of interdependence and dynamic connection among the BRICS financial markets using appropriate univariate time series models were evaluated for the period January 2008 to January 2018. The results revealed the presence of ARCH effects in the BRICS exchange rates. The univariate GARCH models for the BRICS exchange rates were fitted to the data using Student t-distribution. The GARCH (1,1) model found the unconditional volatility for each of the BRICS exchange rates series while EGARCH (1,1) and TGARCH (1,1) models presented the leverage effect. Moreover, the EGARCH (1,1) model illustrated that the asymmetric effects dominate the symmetric effects except for South Africa. The TGARCH (1,1) model on the other hand revealed contrary findings. The paper recommends a study be considered to draw comparison on the different types of GARCH models on the time varying integrated data other than the ones used in the paper.

scholarly journals Structural Breaks and Volatility Persistence of Stock Returns: Evidence from the US and UK Equity Markets

2018 ◽  
Vol 5 (6) ◽  
pp. 76
Author(s):  
Chikashi Tsuji
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Structural Breaks ◽  
Garch Models ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Persistence ◽  
The Us ◽  
Parameter Values

This paper quantitatively investigates the effects of structural breaks on stock return volatility persistence by using the US and UK stock market index return data. Applying two kinds of representative univariate GARCH models of standard GARCH and EGARCH models, we derive the following interesting findings. (1) First, we find that for both the US and UK stock market returns, the volatility persistence parameter values of standard GARCH models decrease when structural breaks are taken into account. (2) Second, we further reveal that for both the US and UK stock market returns, the volatility persistence parameter values of EGARCH models again decline when structural breaks are taken into consideration.

scholarly journals U.S. STOCK MARKET P/E RATIOS, STRUCTURAL BREAKS, AND LONG-TERM STOCK RETURNS

2018 ◽  
Vol 19 (1) ◽  
pp. 110-123 ◽  
Author(s):  
Chung BAEK ◽  
Ingyu LEE
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Structural Breaks ◽  
Structural Changes ◽  
Market Returns ◽  
Stock Market Returns ◽  
Long Run ◽  
Cumulative Sum Control ◽  
The Relationship

Our study investigates structural changes in the market P/E ratio and shows how structural changes affect long-term stock market returns. Using the cumulative sum control chart and the Bai-Perron algorithm, we identify multiple structural breakpoints in the market P/E ratio and find that those structural changes are significantly perceived over the long run. Unlike previous studies that do not consider structural changes, our study is the first one that shows how structural changes asymmetrically influence long-term stock returns depending on the high or low P/E period. This implies that structural changes in the market P/E ratio play an important role in explaining long-term stock returns. We propose that structural changes should be taken into account in some manner to establish the relationship between P/E ratios and long-term stock returns.

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