GARCH Term Research Articles

Cross-Market Volatility Dynamics Among Crude Oil and South Asian Emerging Markets During the COVID-19 Pandemic.

The COVID-19 pandemic had profound impacts on the global economy, initially triggering a severe economic downturn due to widespread lockdowns, this outbreak generated various economic shocks, a major one being the oil price crash. A brief understanding of how this pandemic is affecting the financial markets, empirical analysis is done on three financial markets. Three stock exchange markets are selected from emerging markets in South Asia (Pakistan, China, and India). The world crude oil market is selected as a reference, to how it transmits volatility to South Asian markets during the COVID-19 period. We employ a bivariate BEKK-GARCH model for analyzing the volatility spillover from November 19, 2019, to March 31, 2021. All markets show negative coefficients with the oil market, indicating that the previous day’s shocks in oil prices reduce today's stock market volatility, with significant values for India and Pakistan. China has a negative coefficient while the rest of the two markets show a positive and significant coefficient means past volatilities in the stock market decrease today’s oil market volatility, likely indicating a reactive or adaptive market behavior to stock volatility. Generally, oil price changes have a significant impact on these markets, with both immediate effects (via ARCH terms) and persistent effects (via GARCH terms). The study's findings offer valuable insights for policymakers, as they help identify the direction of spillovers among interconnected economies. Additionally, portfolio investors can use this information to effectively allocate weights to different asset classes when making investment decisions.

The impact of Central Bank’s Credits to the Federal Government on the Effectiveness of Open Market Operations in Nigeria

This paper investigates the impact of the Central Bank's credit to the Federal Government on the effectiveness of open market operations (OMOs) as a tool of monetary policy in Nigeria from 1993Q1-2021Q4. The monetary instability in Nigeria is a result of the ineffective use of market-based monetary policy instruments which have worsened liquidity conditions and sparked inflationary pressures in the economy. The study used the ARCH/GARCH method in order to analyse the quarterly secondary data collected from the Central Bank of Nigeria and the National Bureau of Statistics. The findings of the study showed fiscal deficits rise or fall as the Central Bank's credit to the Government rises or falls. Also, CBN credits to the Federal Government had a significant negative impact on the effectiveness of OMO sales in Nigeria. The ARCH and GARCH terms in OMOs implied that volatility is persistent in the open market for treasury bills in Nigeria. Furthermore, the OMO sales forecasts showed that the conditional variance of the model converges to a steady quickly stated thereby implying that OMOs are effective. The study concludes that OMOs if more efficiently utilised, could stabilise monetary aggregates and stabilise prices in the economy.

The fluctuation linkages and price volatility risk on agricultural commodity market: Evidence from Vietnamese coffee

This paper uses the DCC-GARCH and Value at Risk (VaR) model to analyze the fluctuation, linkage, and price volatility risk among coffee price series in the period of 2004 - 2020. In terms of the fluctuation, the study points out, the volatility of Vietnamese coffee price and the price of Robusta coffee in two markets were affected by two ARCH terms and GARCH terms at 1 percent level. Meanwhile, the coffee price of Brazil and Colombia is only impacted by the ARCH term. The linkage between Brazil and Colombia is the biggest. The average coefficient linkage among Vietnam with two main competitors is relatively small. In terms of price volatility risk, the price volatility risk of Vietnamese coffee is the smallest and the biggest risk is belonging to Brazilian coffee price. The results obtained would be a valuable reference for stakeholders, policymakers, coffee processing and exporting enterprises, and coffee farmers to clearly understand the fluctuation and linkage among coffee export price series, and thereby have appropriate and effective solutions and strategies in price volatility risk management to sustainable development.

Analysis of the Volatility of Wind Energy Production in Romania Applying the EGARCH Model

Purpose: The objective of the paper is to highlight the volatility of wind energy production, the renewable source of energy whose output is the most difficult to predict due to its dependence on climatic factors. The present research helps to analyze the risks assumed both by the producers who choose to invest in wind farms and the operator who has to balance the national energy system in case of production fluctuations. Many times, these risks are taken over, without their knowledge, by the final consumers who have to pay higher prices for the energy consumed. Design/methodology/approach: In this study, the EGARCH model was applied with the help of EVIEWS 12 software on a data series updated at least every 10 hours (1405 entries) regarding the evolution of wind energy production from January 2021 to January 2023. As a rule, the EGARCH model is used to analyse the volatility of indices in the financial field, but the present study, as well as others before, showed that it also fits perfectly in the case of renewable energy, the statistical tests applied to the results proving this fact. Findings: The results of the study show a GARCH term of 0.936, which points out a volatility comparable to some of the riskiest international stock indices. Research limitations/implications: This volatility is reflected through increases in balancing costs, ultimately borne by the energy consumers on the national market. Against this background, it is necessary to increase the stability of the national energy system, so that the balancing process would be less expensive and less dependent on imports. The solutions are varied - from the creation of stable non-polluting production capacities, such as the nuclear ones, to the thorough analysis of the characteristics of the areas where production systems from renewable sources are installed. It must be mentioned that the main limitation of this study is the long period considered, because the accuracy of the results could be better if the model was applied several times for data from consecutive, shorter periods. Originality/value: This study contributes to the theoretical identification of methods to forecast the volatility of renewable energy production and to highlight the vulnerabilities of increasing the share of this energy in the European energy mix. It tests the use of heteroscedastic models to estimate the volatility of the wind power supplied to the system, unlike most studies in the literature, which analyze the volatility of the energy market price. In addition, it proposes some solutions that could reduce or control this instability and ensure the regional energy security.

Modelling of Daily Price Volatility of South Africa Property Stock Market Using GARCH Analysis

The study examined the volatility of the daily market price of listed property stocks on the Johannesburg Stock Exchange (JSE) for a 10year period (2008-2017). The study used daily prices from January 2, 2008 to December 29, 2017 of twelve (12) quoted property companies out of the twenty-seven (27) listed on Johannesburg Stock Exchange (SA REIT Association, 2020). The study computed the average daily price of the selected (12) property stocks and was used as a proxy for the daily market price for the property stock market in the analysis. The study modelled SA-REIT market price volatility using generalised autoregressive conditional heteroskedasticity (GARCH 1, 1). The GARCH model reported that the previous day's information of both the daily market price (ARCH term) and the volatility (GARCH term) have a positive and significant (p<.05) effect on the current day’s daily market price volatility in the property stock market. The result of the model implies that investment in the property stock market is strongly driven by positive news on daily price than a negative shock; meaning that South Africans' investors are more sensitive and exhibit a sharp response to good news on daily market price than bad news when thinking of investing in listed property company shares on Johannesburg Stock Exchange.

Volatilities in Market Shares through Financial Fundamentals for Global Automobile Industry

The proposition defined for this study is investigating the volatile behavior of automobile market share of top manufacturing nations in the presence of financial fundamentals; the time series data of market shares of the top 26 automobile manufacturing industry nations from 2002-2014 were calculated from the collected data of automotive sales of stated nations while stated automotive sales data were collected from the data stream (EIKON) whereas, the change in closing shares prices and trading volume of stocks (financial fundamentals) for the same stated period of these nations were collected from the Yahoo! Finance. The GARCH 1,1 process was used and the findings confirmed that there is no GARCH 1,1 process in any of the given series as the ARCH and GARCH terms are found insignificant in the presence of stated conditional variables. Therefore, lagged autoregressive investigation for this study revealed that econometrical volatilities in the stated market shares defined in previous time grid (lag 01) do not predict volatility significantly in a current time grid for all the nations.

Forecasting Index Return Volatility of The Chittagong Stock Exchange of Bangladesh using GARCH Models

Purpose: The aim of this research is to identify the best-fitted model(s) for estimating and forecasting the return volatility of the Chittagong Stock Exchange (CSE) in Bangladesh. Methodology: The study analyzes the returns of the Chittagong Stock Exchange's (CSE) daily Selective Categories Index (CSCX) from February 4, 2013 to December 31, 2021 (as a full sample) and from July 1, 2021 to December 30, 2021 (for forecasting). The researcher used GARCH family approaches considering different error distributions, to find the well-suited model(s) for the CSCX index. The researchers used ARMA to develop the mean equation based on two popular model selection criteria: Schwarz's (1978) Bayesian information criterion (SBIC) and Akaike's (1974) information criterion (AIC). The data has been analyzed using the application software E-Views 10. Findings: The ARMA (0,1) has been adopted as the mean equation for GARCH specifications. Under all three types of error distributions, the ARCH and GARCH terms, along with the leverage terms of asymmetric models, were found to be statistically significant in all the accepted combinations of the model. The models GARCH (1,2), TGARCH (1,2), and PARCH (1,2) under generalized error distributions and EGARCH (2,1) under Student’s t error distributions have been selected as the best-fitted models for estimation. Whereas, based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), and theil inequality (TI), EGARCH (1, 2), TGARCH (1, 2), and PARCH (1, 2) under generalized error distributions, and GARCH (1, 2) under student’s t error distributions and normal error distributions are found to have superior out-of-sample forecasting abilities. Practical implications and originality: This is an original research work that will help the investors and other stakeholders of the Bangladeshi stock market to estimate and forecast market volatility more efficiently. Limitations: Due to its extensive features, this study was unable to incorporate a few additional ARCH and GARCH models.

Volatility Estimation When the Zero-Process is Nonstationary

Financial returns are frequently nonstationary due to the nonstationary distribution of zeros. In daily stock returns, for example, the nonstationarity can be due to an upwards trend in liquidity over time, which may lead to a downwards trend in the zero-probability. In intraday returns, the zero-probability may be periodic: It is lower in periods where the opening hours of the main financial centers overlap, and higher otherwise. A nonstationary zero-process invalidates standard estimators of volatility models, since they rely on the assumption that returns are strictly stationary. We propose a GARCH model that accommodates a nonstationary zero-process, derive a zero-adjusted QMLE for the parameters of the model, and prove its consistency and asymptotic normality under mild assumptions. The volatility specification in our model can contain higher order ARCH and GARCH terms, and past zero-indicators as covariates. Simulations verify the asymptotic properties in finite samples, and show that the standard estimator is biased. An empirical study of daily and intradaily returns illustrate our results. They show how a nonstationary zero-process induces time-varying parameters in the conditional variance representation, and that the distribution of zero returns can have a strong impact on volatility predictions.

An Empirical Analysis on the Volatility of Return of CSI 300 Index

In order to better observe the trend of the stock market, this paper selects the daily closing price data of CSI 300 index from April 12, 2016 to September 30, 2021, and makes an empirical analysis on the logarithmic return of CSI 300 index. It is found that: (1) the return series of the CSI 300 index shows the statistical characteristics of peak, thick tail, bias, asymmetry and persistence. The ARMA (2,3) model can effectively fit the yield series and predict the future trend to a certain extent. (2) The residuals of ARMA model show obvious cluster effect and ARCH effect (conditional heteroscedasticity). GARCH (1,1) model can better fit the conditional heteroscedasticity, so as to eliminate the ARCH effect. (3) By constructing GARCH (1,1) model, it is found that the sum of ARCH term coefficient and GARCH term coefficient is very close to 1, indicating that GARCH process is wide and stable, the impact on conditional variance is lasting, and the market risk is large, that is, the impact plays an important role in all future forecasts.

Sources Allocation on Risk Performance of Egyptian Insurance Companies

The main objective of current paper was to investigate the relationship between sources allocations measured by (external sources to total assets- free investment and allocated investment to total assets) on risk performance measured by financial risk (standard deviation of return on equity) business risk (standard deviation of return on assets) using ARDL and GARCH model, using a sample of 19 Egyptian insurance companies over a 21 year period form 1999 – 2019, the findings indicate that There is a significant negative linear relationships between the independent variable in terms of capital structure and assets structure on dependent variable standard deviation of return on equity (Y1) at a Significant level less than (0.05). Also there is a significant positive linear relationship between the independent variable in terms of assets structure and dependent variable standard deviation of return on assets (Y2) at a significant level less than (0.001). with regard to ARCH and GARCH result shows the There is a significant positive effect of the ARCH term, measured as the lag of the squared residual from the mean equation, and the GARCH term, Last period’s forecast variance, at a significant level less than (0.05). It means that the high volatility in the conditional variance of standard deviation of return on assets y2. also The sum of the two parameters of: (α+β) in the GARCH model (1, 1) whether the random error are distributed according to normal distribution approaches the positive one, which indicates that the two conditions of non-negative variance, and the variance is not inflated are satisfied, and that Indication of the continuity of volatility shocks in the standard deviation of return on assets (Y2).

A New Look at Weekdays Effect of Stock Prices in the Weak Form: Evidence from Nigerian Stock Exchange

Efficiency intensity of stock exchange market has policy implications in any economy. The focus of this work is to assess the intensity of efficiency in the weak form of the Nigerian stock exchange market. This study stands to examine predictability level of Monday trading, Tuesday trading, Wednesday trading, Thursday trading and Friday trading in the Nigerian Stock Exchange (NSE). Monday NSE all share index covered August 6, 2018 to October 28, 2019; Tuesday NSE all share index covered August 7, 2018 to October 29, 2019; Wednesday NSE all share index covered August 8, 2018 to October 30, 2019; Thursday NSE all share index covered August 9, 2018 to October 31, 2019; Friday NSE all share index covered August 10, 2018 to November 1, 2019. The data was analyzed employing Jarque-Bera check for normality, ADF unit root test, graphs and GARCH term. The Jarque-Bera examination for normality indicates that all the data are normally spread. The ADF unit root shows that the factors employed were not stationary @ level series but stationary @ 1st difference. The GARCH term shows that all the variables were not significant at 5% level. From the above findings, stock prices on Mondays, Tuesdays, Wednesdays, Thursdays and Fridays stock prices studied exhibited significant random walk, the investigation therefore concludes that, NSE market is efficient in the weak form. It is thus recommended that Government should continue to allow the invisible hands to determine the direction of stock prices in order to boast NSE trading activities.

Exchange Rate Volatility and International Trade-in

This study investigates the impact of exchange rate volatilities on international trade in Nigeria. The research is carried under the assumption that exchange rate volatilities are deemed to impact on the volume of export and import trading activities. The study made use of Secondary data from 1996 to 2018. Econometric tools were used to ascertain relationships. The paper established a mixed result between the variables under review. While some of the tests did not provide adequate and predictive information on the relationship between exports, imports and real effective exchange rate, others did. The VAR model estimates indicate an inverse relationship between Export, Import and REER in current periods. A unit increase in export and import in a particular year leads to about 0.9% and 0.4% decrease in REER respectively. Variance decomposition analysis suggests that the shocks partially explain fluctuations in REER, as well as exports and imports. The Impulse response analysis indicates a negative association between export and real effective exchange rate while it was majorly positive for imports throughout the ten periods. The causal effect reveals that import causes exports but that exports do not granger cause imports. The ARCH modelling approach suggests the existence of a first-order Arch effect and a significant GARCH term. Though the Coefficient of GARCH in a mean term is negative; it produced a singular covariance which by itself is not unique. Results show evidence of volatility of REER clustering on import and export trading activities in Nigeria. This could have serious implications for growth in Nigeria, as a reduction in the growth of exports could reduce the foreign exchange earnings available for the financing of developmental projects. At the same time, a decline in imports could affect domestic production and consumption. It could also impinge negatively on the balance of payment positions for Nigeria. In line with these observations, monetary and fiscal interventions are required to mitigate the adverse effects since financial shocks often exacerbate exchange rate volatilities.

Predicting Short-term Market Returns and Volatility Using Index of Consumer Sentiment

This study examines the short-term predictive ability of Index of Consumer Sentiment (ICS) about Indian stock market returns. Monthly values of ICS, six broad market indices, and nine sectorial indices are collected. The paper finds significant contemporaneous co-movement between S&PBSE500 and other indices. There is no contemporaneous co-movement between ICS and 15 indices. With one lag, ICS Granger cause two sectorial indices and four broad market indices. The one period ahead predictive regression model finds that sentiment has some predictive power of small cap, mid cap and BSE500 index returns. The effect is negative and statistically significant. The predictive regression result indicates that following month of high consumer sentiment, small cap, midcap, and BSE500 index returns decline and vice-versa. However, there is no association between ICS and large cap index and sentiment has no predictive ability of large cap index. The result of variance model indicate that ARCH term and GARCH term are insignificant indicating that the market has no long memory and new shocks will not persist to many future periods. The paper finds no volatility clustering and volatility persistence except in case of small cap index. This paper finds presence of noise trade and investors over-reaction in small cap stocks. EGARCH result supports for the presence of leverage effect, and confirms negative impact of consumer sentiment on small cap stocks.

Oil Price Volatility, Exchange Rate Movements and Stock Market Reaction: The Nigerian Experience (1985-2017)

Given the observed volatility in crude oil prices in the international oil market and the role which oil and gas play in the Nigerian economy, this paper is an attempt to investigate the impact of crude oil prices and foreign exchange rate movements on stock market prices in Nigeria. In addition, the paper examined whether there is any volatility pass-through between the dollar price of Nigerian crude oil, foreign exchange rate of the Naira and stock market prices respectively. Data employed for the study are monthly values of the Nigerian Stock Exchange (NSE) All-Share Index (ASI), Dollar price of Nigerian Crude Oil (DPO) and the Official Exchange Rate of the Naira to the US Dollar (FXR) from January, 1985 to August, 2017. The methodology adopted for the study include the ADF unit root tests, Johansen co-integration tests, the ECM technique, Granger causality tests, variance decomposition as well as the GARCH(1,1) to model the volatility relationships among the variables. Findings reveal that there is one long-run dynamic co-integrating relationship among the variables ASI, DPO and FXR while the ECM results indicate that Crude oil price (DPO) significantly impact on Stock market prices. The Granger causality test reports a bi-directional causality relationship between ASI and DPO and a unidirectional causality running from FXR to ASI. The ARCH-GARCH volatility analysis demonstrates vividly that stock market prices in the NSE exhibit ARCH effect with a significant and positive first order ARCH term. The GARCH term is also positive and significant indicating that previous month’s stock market price volatility significantly influences current stock market volatility in the NSE. In addition, findings show that the volatility of dollar price of Nigerian oil (DPO) in the world oil market is significantly transmitted to the volatility of stock market prices in Nigeria. The pass-through effect of the volatility of exchange rate (FXR) to the volatility of stock market prices is also positive and significant. These findings offer significant informational signal to policy makers, portfolio managers/advisors and the investing public in achieving optimal asset and portfolio profile.

What drives Tadawul All Stock Index of the Saudi Stock Market

This paper aims to detect factors, which drive Tadawul All Stock Index (TASI) of the Saudi Stock Market during the period August 2005 May 2017. The application of Autoregressive Distributed Lag model (ARDL) and Bounds test revealed that there is no long-run relationship exist if consumer price index, interest rate, oil prices, general price level (P) and government expenditure are included in the model. While price-earnings ratio, nominal effective exchange rate (NEER), money supply (M3), and long term credit (L) relate significantly to TASI in both the short-run and long-run with an error correction term -0.22 i.e. disequilibrium takes four and half month to correct. Half of explanatory variables are fixed namely NEER, and L, while PE and M3 are dynamic. The GARCH model presented that news plays significant role in the volatility of stock market returns. Smaller GARCH term than ARCH indicates that a shock will take short time to die out.

VOLATILITY IN STOCK MARKETS: A COMPARISON OF DEVELOPED AND EMERGING MARKETS OF THE WORLD

Volatility in markets is the growing area of crucial attention which is being analysed by many academicians over the world. The reason being that with the passage of time, the probability of deviation of the prices from the initial intrinsic value increases. In this research we have tried to model the volatility of two indices: MSCI emerging markets index and MSCI world index with the use of ARCH and GARCH models. The volatility clustering and ARCH effect were seen and the models were constructed. Both the ARCH and GARCH terms were found to be significant in both the market indices. It was found that in emerging markets, yesterday’s volatility had greater influence in explaining today’s volatility while in case of developed markets, both yesterday’s volatility and information had immense influence in explaining today’s volatility. The information is of immense use to the finance professionals and investors and can help them in taking correct portfolio decisions .

A Comparison of GARCH Models for Volatility Estimation of Returns on Securities in Resource Group of Thai Stock Market

The objectives of this study are to estimate volatility of returns on securities in ResourceGroup of Thai Stock Market using basic specifications of ARCH/GARCH/EGARCH model, and to measure skewness and kurtosis of the error term distribution from models selection.A test of ARCH Effect is performed in order to examine whether all data fitt with the ARCH family, and then ARCH (1, 1), GARCH (1,1) and EGARCH (1,1) are developed. A conditional variance equation from ARCH (1,1) is 2 0 1 1 2 t = + t− σ α α ε , and fromGARCH (1,1) is in a form 2 1 1 2 0 1 1 2 σ t = α +α at− + β σ t− and for EGARCH (1,1) is 1 1 1 2 1 1 2 ln( ) ln( ) − − − − = + − + + t t t t t t σ ε γ σ ε σ ω β σ α . The Akaike Criterion (AIC) and Schwarz criterion (SC) statisticsare used in model selection for non-nested alternatives-smaller values of the AIC and SC are preferred. For testing normality, a test statistic is Jarque-Bera and if the residual are normally distributed, the histogram should be bell-shaped and the Jarque-Bera statistic should not be significant, and if the null hypothesis is rejected, then innovation would be introduced through student’s t distribution. After all these steps, skewness and kurtosis are calculated in the study. Results of the study showed that 6 out of 30 securities data fitted with ARCH (1,1) model with insignificance statistical results which indicated that there must be a reconsideration of applying higher order of ARCHinstead of ARCH (1,1) model. There were 5 securities fitted with GARCH (1,1) model, with the same pattern, i.e., lower value for the coefficient of the ARCH term and higher value for the GARCH term which lead to a high fluctuations in GARCH. This finding indicated that the asset return was expectedly large in either the upward or the downward direction, and the estimation results indicated that GARCH (1,1) is appropriated to explain volatility cluster and estimate value of risk on security return. There were 19 securities that data esimation indicated that EGARCH (1,1) model was an appropriate form of variance equation, and showed that all volatility estimation were about 50% or about at moderated level. The skewness and Kurtosis of all residuals distributions showed that all skewness values were greater than zero (skewed to the right) and all Kurtosis values were greater than 3 (Leptokurtic distribution). This means that for each security, the daily price was concentrated on the left side of the mean value, and there were some extreme values to the right side. For the kurtosis, the distribution had a thicker tail which meaned that there was a high probability that the set of data would gain such extreme values

A Study of Nonlinear Dynamics in Equity Market Index: Evidence from Turkey

(ProQuest: ... denotes formulae omitted.)1. IntroductionSuccessful modeling of asset prices is very crucial for investors. It has been shown in a vast number of studies that asset prices exhibit non-linear structures (Hsieh, 1991; Lim and Brooks, 2011; Cariani, 2014). The non-linear dynamics in financial assets are of critical concern because they are inconsistent with market efficiency. In addition, using a linear model to price securities may result in an incorrect asset price and exploitable profit opportunities. Most of the recent empirical literature has focused mainly on discovering whether financial markets are characterized by non-linear behaviors. However, understanding the nature and function of non-linear dependence driving the market price has become increasingly more important in implementing a successful and accurate forecast leading to a profitable trade.The purpose of this study is to examine whether the Istanbul Stock Exchange (ISE) stock markets are governed by non-linear dynamics. The popularity of the ISE equity index has been increasing in proportion to the rapidly growing economy of Turkey. Approximately 60 percent of the securities traded in the ISE are owned by foreign investors. Therefore, the equity market in Turkey has become more important for global investors as well.This study aims to answer two important questions about the ISE index: First, is there a persistent dependency in the equities traded in the Istanbul Stock Exchange? Second, if the dependency persists in the ISE equity index, what is the form of the dependency? In other words, is the dependence due to linear, non-linear, or chaos dynamics? This paper evaluates the non-linear dynamics of the ISE 100 index using rich variety techniques, including the BDS test, the chaos test, the Markov Chain test, the time reversibility test, the and duration dependence test.There have been an extensive number of studies carried out to reveal non-linear structures in financial time series. In addition, some studies specifically analyzed the ISE for non-linear dependence and chaotic structures (Ozer and Ertokatli, 2010; Ozkaya and Ozkaya, 2012; Odabasi et.al., 2004). This study will make significant contributions to the current literature because none of the studies were as systematic and comprehensive as this study in analyzing dependence issues step by step. In the first step, a BDS test is applied to the log ISE index return series to diagnose for any form of dependency1. The BDS result reports evidence of dependence in the ISE returns. When several ARIMA models are fitted into the ISE time series and the residuals are tested again for dependence, dependence continues to persist. In the next step, some nonlinear models are employed to further investigate whether nonlinearity could possibly be one of the forms of dependency. When the ISE return is modelled as one significant GARCH term and one threshold order, the BDS test fails to reject the presence of dependency. Further K-map and Z-map analysis shows that the dependency in the ISE return is not caused by erratic and chaotic behavior in the Turkish equity market. A Markov chain test shows that the dependence is not a short-term dependence. A time reversibility test further provides supporting evidence that the dependence in the ISE 100 index is nonlinear and the non-linearity comes from the asymmetric innovations. Lastly, in accordance with the duration dependence test, the non-linear dependence evident in the ISE return is not consistent with the characteristic of a rational bubble.The remainder of the paper is organized as follows. In the next section, we review the literature for nonlinear dependency in security prices. This section also provides a comprehensive review of stock returns behavior in Turkey. Section 3 describes our data and summarizes the descriptive statistics of the ISE 100 index and three macro-economic variables including Turkish Lira/$ exchange rate, interbank overnight rate, and inflation in Turkey. …

Modelling stock return volatility dynamics in selected African markets

This paper examines whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002–2012. In the univariate portion of the paper, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures. In the multivariate context, the single-state and Markov-switching models are comparatively assessed according to their usefulness in constructing optimal stock portfolios. Accounting for structural breaks in the conditional variance process, conventional GARCH effects remain important in capturing heteroscedasticity. However, those univariate models including a GARCH term perform comparatively poorly when used for forecasting purposes. In the multivariate study, the use of Markov-switching variance–covariance estimates improves risk-adjusted portfolio returns relative to portfolios constructed using the more conventional single-state models. While there is evidence that some Markov-switching models can provide better forecasts and higher risk-adjusted returns than those models which include GARCH effects, the inability of the simpler Markov-switching models to fully capture heteroscedasticity in the data remains problematic.

Modelling Conditional Volatility in Shariah Index of GCC Countries

This paper investigates the nature and behaviour in Shariah market of GCC countries with daily returns spanning from 01/12/2008 to 31/08/2013. In order to model the volatility, GARCH (1, 1), E- GARCH (1, 1) and T- GARCH (1, 1) have been employed. After detecting the ARCH effect, the residuals were modelled with the above tools. The results of GARCH (1, 1) were highly significant at 1% significance level. The high significance of α (ARCH term) and β (GARCH term) implies that past volatility highly influences the current volatility of all the series under study. The asymmetrical GARCH models outperformed the symmetrical GARCH models. By the application of E-GARCH (1, 1) and T-GARCH (1, 1) it was found that that there was leverage effect in the all the indices with few exceptions. The results of TGARCH (1, 1) showed that there was no asymmetry in the returns of Bahrain and Oman when compared to all other indices. With regard to E-GARCH (1, 1) results it was witnessed that Bahrain, Saudi Arabia, Qatar and United Arab Emirates were highly significant at 1% level with negative coefficients of leverage effect indicating that negative news highly influences the market than positive news, but Kuwait and Oman were the exceptions in the conditional modelling. In both the cases Oman stood out with a negative result. The results of the study provides insight in to volatility structure of Shariah markets more specifically the indices of GCC countries, which is of great help for the investors in Shariah index of GCC countries.