Asymmetric GARCH Models Research Articles

COVID-19’s disasters are perilous than Global Financial Crisis: A rumor or fact?

This investigation employed the Asymmetric Power GARCH model and found that COVID-19 substantially harms the US and Japan's market returns. Moreover, COVID-19 has influenced the variance of the US, Germany, and Italy's stock markets more than the Global Financial Crises (GFC). However, GFC indicated a more significant impact on the financial volatility of the Nikkei 225 index and SSEC than COVID-19. The study confirmed the leverage effect for the S&P 500, Nasdaq Composite Index, DAX 30, Nikkei 225, FTSE MIB, and SSEC. The analysis authenticated that the health crisis that befell due to COVID-19 have imperatively originated the financial crisis globally; however, the Asian markets still make available better prospects for portfolio optimization.

Asymmetric Bitcoin Volatility under Structural Breaks

We show that incorporating endogenously-determined structural breaks into an asymmetric GARCH model reduces volatility persistence in Bitcoin prices. We find that both good news and bad news have less impact on volatility if structural breaks are incorporated. Hence, our results may indicate that previous studies, that ignore structural breaks, might have overestimated the impact of news on Bitcoin volatility. In addition, our results suggest that Bitcoin shocks die out more quickly than previously thought. For example, the half-life of a GARCH model drops from 319 to only 34 days. Our paper provides quantitative evidence of the benefits of including both asymmetric effects and structural breaks in a GARCH model to correctly build Bitcoin volatility models.

Investigating the price volatility transmission mechanisms of selected fresh vegetable chains in Greece

PurposeThis paper attempts to model the transmission of volatility between producer and consumer prices in the fresh potato, tomato, and cucumber markets in Greece.Design/methodology/approachThe transmission mechanism of the price volatility is modeled using the most popular multivariate GARCH models while taking into consideration possible asymmetries in the transmission process. The models utilized are the DVECH and the BEKK models from the VECH family, as well as the CCC model and the DCC model from the family of conditional correlation models. The possible asymmetric effects are evaluated using asymmetric GARCH models, as well as estimating volatility impulse responses for independent shocks.FindingsThe results reveal that, in the tomato and cucumber markets, which are regulated by the Common Market Organization of fruits and vegetables, producers are less vulnerable to volatility shocks transmitted from consumers. In contrast, in the non-regulated potato market, producers are affected by spillover effects from consumers.Research limitations/implicationsThis study is limited to a few commodities (i.e. potatoes, tomatoes, and cucumbers); however, it could be extended to additional commodities.Practical implicationsThe results of this study show that, if producers are organized on a cooperative basis within the regulative framework of the CAP, like the tomato and cucumber producers, their place in the food supply chain is strengthened, although the CAP targets more market-oriented agricultural markets that are more exposed to world prices.Originality/valueThe present study attempts to understand the transmission of volatility between producer and consumer prices in the fresh potato, tomato, and cucumber markets in Greece, which is not apparent from previous studies. Furthermore, the volatility clusters that are identified in the present study are associated with certain CAP reforms.

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

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.

Modelling the oil price volatility and macroeconomic variables in South Africa using the symmetric and asymmetric GARCH models

This article employed the ARCH, GARCH and EGARCH models to model the oil price volatility and macroeconomic variables in South Africa for the period 1990Q1 to 2018Q2. The macroeconomic variables used in the study are GDP, inflation, interest rate and exchange rates. According to ARCH (1) and GARCH (1, 1) models, exchange rate and interest rate have a negative effect on the oil price, while GDP and inflation suggesting a positive effect. The results for GDP and inflation imply that a 1% increase in GDP and inflation may lead to an increase in oil price. The negative effect on interest rate and exchange rate led by their negative values implies that a 1% increase in interest rate and exchange rate may lead to a decrease in oil price. The EGARCH (1, 1) model revealed that oil price is negatively affected by all the macroeconomic variables. This implies that a 1% increase in these variables may lead to a decrease in oil price. The symmetric and asymmetric techniques revealed that the South African oil prices are volatile. The article recommends that South African policy makers should have a view on the impact of oil price volatility on the South African economy.

Volatility Modelling of Global Financial Crises Effects on the Nigerian Banks

The most appropriate heteroskedastic models for predicting volatility of daily stocks prices of 10 major Nigerian banks are proposed. The banks are Access, United Bank for Africa (UBA), Guaranty Trust, Skye, Diamond, Fidelity, Sterling, Union, ETI and Zenith banks; and these are examined from 2004 to 2014. The models employed are Autoregressive Conditional Heteroscedastic (ARCH(1)), Generalized Autoregressive Conditional Heteroscedastic (GARCH(1, 1)), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH(1, 1)) and Glosten, Jagananthan and Runkle-Generalized Autoregressive Conditional Heteroscedastic (GJR-GARCH(1, 1)). The results show that all the bank returns are highly leptokurtic, significantly skewed and thus non-normal across the four periods except for Fidelity bank during financial crises; findings similar to those of other global markets. Also noticed is the strong evidence for the presence of heteroscedasticity, and that volatility persistence during crisis is higher than before the crisis across the 10 banks, with that of UBA taking the lead, about 11 times higher during the crisis. The same with persistence levels in volatility, which were relatively higher during financial crises across the ten banks compared to before the crises. Findings further indicate that Asymmetric GARCH models outperformed the symmetric GARCH models, especially during the financial crises and post the crises. Thus with these findings, one could generally conclude that Nigerian banks’ returns are volatility persistent during and after the crises, and are characterized by leverage effects of negative and positive shocks during these periods.

Sentiment Analysis of Indian Stock Market Volatility

Abstract Traditional empirical models analyze impact of sentiments on financial market volatility using macroeconomic fundamentals or financial indicators. In this paper, recent methods of text based sentiment analysis of market from relevant news articles regarding economy and financial market are used. Two distinct market sentiments namely, positive and negative sentiments are constructed using different emotions which are identified through standard natural language processing methods. Further, the paper aims at proposing an augmented version of asymmetric GARCH model of conditional volatility for Indian stock exchange, Sensex, during the time period of April 19, 2007 to January 10, 2020 by incorporating aforementioned market sentiments. Empirical findings suggest dominant impact of negative market sentiment over positive one and it also provides evidence of noise trading in financially immature Indian stock market.

Analysis of Risk Premium Behavior in the Tunisian Foreign Exchange Market During Crisis Period

In this paper, we empirically examine time-varying risk premia in the Tunisian foreign exchange market by applying GARCH-M modeling to the TND/Euro and TND/USD parities for 1 to 12 months forecasting horizons. Our ultimate objective is to help better manage the parities and to help provide stability to a foreign exchange market where EMH is very weak. Our findings show 1) a heteroscedasticity of residuals for the TND/Euro (all forecasting horizons) and the TND/USD(for 1 month) indicating a lack of stability of their volatility; 2) a non-significant standard deviation of the risk premium against the presence of ARCH and GACH in all cases. On the other hand, the descriptive analysis of the risk premium and term premium variables show an asymmetric distribution. We used, therefore, the asymmetric GARCH model, E-GARCH. Our normality tests show, however, that the GARCH model neither allows for residuals smoothing nor improves the AIC.

Are global ethanol markets a ‘one great pool’?

The main objective of this empirical work is to test whether the global ethanol markets move together. To serve this purpose, prices of the US and Brazilian ethanol sectors have been analyzed. Employing the asymmetric multivariate GARCH model, we document significant price and volatility spillover effects between the ethanol markets under study. Such findings indicate that world ethanol markets move together and they are not regionalized. These results have important implications. For example, given that ethanol markets are codependent, information flow across ethanol markets could guide policymaking and the formulation of hedging strategies for contagion risk prevention during ethanol market downturns. In addition, this type of information on ethanol markets is also crucial for investors to maintain well-diversified portfolios, and for opening (closing) investment positions.

Liability driven investment with alternative assets: Evidence from Brazil

We study the portfolio choice problem for an asset-liability investor who invests in stocks, equity mutual funds, government bonds, short term interest, hedge funds, listed real estate, and commodities futures available in Brazil. Inflation and real interest play as important risk sources. We estimate the asset classes and liabilities time-varying conditional covariance structure using an asymmetric multivariate dynamic conditional correlation GARCH model and compare the asset-liability portfolio's global minimum variance allocation with Brazilian pension funds' market portfolio. The conditional covariance structure provides insights about the complex dynamic relationships between the asset classes and liabilities. We find that some (though not all) Brazilian alternative assets render strong diversification and liabilities hedging benefits for asset-liability investors. There are significant strategic asset allocation differences between the market portfolio and the liability driven portfolio as given by our model. We, therefore, question the Brazilian pension funds' allocation.

Bayesian analysis of periodic asymmetric power GARCH models

Abstract In this paper, we set up a generalized periodic asymmetric power GARCH (PAP-GARCH) model whose coefficients, power, and innovation distribution are periodic over time. We first study its properties, such as periodic ergodicity, finiteness of moments and tail behavior of the marginal distributions. Then, we develop an MCMC algorithm, based on the Griddy-Gibbs sampler, under various distributions of the innovation term (Gaussian, Student-t, mixed Gaussian-Student-t). To assess our estimation method we conduct volatility and Value-at-Risk forecasting. Our model is compared against other competing models via the Deviance Information Criterion (DIC). The proposed methodology is applied to simulated and real data.

An Investigation of Dynamic Price Movements of the Cryptocurrency Coin in Korea

This paper investigates the dynamic price movements of cryptocurrency market in Korea by employing asymmetric DCC multivariate GARCH and risk decomposition model to reflect the time-varying integration process. We find that the law of one price does not hold between Korean and developed markets like U.S. and Japan, implying that emerging cryptocurrency market can be exploited as a scapegoat of arbitragers. Specifically, the price spreads of 20 to 30 percent between BTC-KRW and BTC-USD persist, exhibiting a sign of economic speculative bubble in Korean cryptocurrency market. Additionally, while there are significant price and volatility spillover effects between cryptocurrency markets of U.S. and Japan, the feedback effects do not exist in the case of Korean market. Our analyses also indicate that the pricing in Korea is mostly based on domestic factors rather than global factors. Finally, we show that this arbitrage opportunity in Korean market has disappeared after a government regulation, which includes banning foreigners and minors from opening new cryptocurrency accounts and prohibiting initial coin offerings (ICOs). The results suggest that a suitable regulation is important to eliminate bubbles.

MODELING AND FORECASTING EXCHANGE RATE VOLATILITY IN EEC COUNTRIES

This main objective of this paper is to examine the properties of the GARCHmodel and its usefulness in modeling and forecasting the volatility of exchange ratemovements in selected EEC countries (Romania, Hungary and Serbia). The dailyreturns of exchange rates on Hungarian forint (HUF), Romanian lei (ROL) andSerbian dinar (RSD), all against the US dollar are analyzed during the period 03.January 2000 to 15. April 2013 in respect. In order to measure the involved risk,symmetric and asymmetric GARCH models are applied. The accuracy of exchangerate volatility forecast is evaluated through reference to the most commonly usedcriteria. These include a Mincer-Zarnowitz regression based test, Mean AbsoluteError (MAE), Root Mean Square Error (RMSE) and Diebold and Mariano test (DMtest). The results of Mincer-Zarnowitz regression test for selected exchange ratereturn series showed a clear lack of explanotory power and sub-optimality of theTGARCH model. The results of the Mean Absolute Error (MAE) and the Root MeanSquare Error (RMSE) for the forecasted volatility showed that symmetric modelbetter predict conditional variance of the exchange rate returns, but estimating resultsindicating that the parameters of forecasts are not satisfactory, i.e. models have littlepredictive power. Results for Diebold-Mariano test results for Diebold-Mariano testshowed that symmetric model outperforming TGRACH forecast in case of Hungarianforint and Serbian dinar sample series, and that only in case of Romania lei TGARCHoutperforming the GARCH forecast.

Modeling and Forecasting the Volatility of Gasoline Prices Using Symmetric and Asymmetric GARCH Models in Erbil City

The paper aims to compare the performance of several univariate symmetric and asymmetric GARCH volatility models in modeling and forecasting the volatility of daily Gasoline prices in Erbil city. This paper chooses the GARCH, GARCH-M, TGARCH, E-GARCH and Power GARCH model to analyze the daily return of Gasoline under three different error distributions: normal distribution, student-t distribution and generalized error distribution and then compare the results and choose the appropriate model to forecast the volatility. The sample is divided into two subsamples. The first subsample is called in-sample data set (Training sample) used to estimate the ARMA-GARCH models for underlying data and the second subsample is called out-sample data set (Testing sample) used to investigate the performance of volatility forecasting. As a result of analyses, we conclude that the best model fits the volatility of Gasoline returns series is AR(2)-Power GARCH(2,1,1) non-linear asymmetric model with innovation student-t distribution (d.f =10), and has better forecasting performance than others models. This result is important in many fields of finance such as investment decisions, asset pricing, portfolio allocation and risk management. Keywords : Conditional Variance; Volatility Clustering; Symmetric and Asymmetric GARCH Models; Error Distribution; Volatility Forecasting; Root Mean Square Error (RMSE)

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

This study investigated the impact of volatility shock persistence on the conditional variance in the Nigerian stock returns using symmetric and asymmetric higher order GARCH family models in the presence of random level shifts and non-Gaussian errors. The study utilised Bai and Perron methodology to detect structural breakpoints in the conditional variance of daily stock and volume of trade returns in the Nigerian stock market from 2nd January, 1998 to 22nd March, 2017. The study employed symmetric GARCH (3,2) and GARCH (2,1)-M models to estimate volatility of asset returns, symmetric GARCH (2,2) and GARCH (2,1)-M to model volatility of volume of trade returns and asymmetric EGARCH (2,2), TGARCH (3,2) and PGARCH (2,3) models to measure the volatility of asset returns as well as asymmetric EGARCH (2,1), TGARCH (1,3) and PGARCH (3,2) models to estimate volatility of volume of trade returns. These models were optimally selected using information criteria and log likelihood as the best fitting symmetric and asymmetric GARCH models to estimate the conditional volatility of asset and volume of trade returns in the Nigerian stock market with and without structural breaks. Results revealed that when random level shifts were ignored in volatility models, the shocks persistence were very high with long memory and variance explosion. But when the random level shifts were incorporated into the GARCH models, there was a significant reduction in the volatility shocks persistence and long memory. Moreover, volatility half-lives also declined drastically while accounting for these sudden level shifts in variance. The study found asymmetry without leverage effects as well as a positive risk-return tradeoff for both asset and volume of trade returns in the Nigerian stock market. The Nigeria banking reform of 2004, the Global Financial and Economic Crises, as well as other local events in Nigeria, were found to have negative and significant impacts on the Nigerian stock market. The study provided some policy recommendations.

Scandinavia: Towards the European Monetary Union?

The EU faces the most important structural crisis of its history, especially after the debt crisis of 2010 in the Euro Area, where the weaknesses of the European common currency were totally unveiled. The main purpose of this paper is to examine if the economies of Scandinavia are historically vulnerable and bound to the European economy (Euro Area). This would aid the further integration of the EU in the long run. Our analysis used the real effective exchange rates of Denmark, Norway, Sweden and Iceland, as a research instrument, by using the Error Correction Model (ECM) and the Asymmetric Dynamic Conditional Correlation GARCH model. Our empirical findings highly support that the economies of Denmark, Norway and Sweden are positively bound to the European economy. On the other hand, the economy of Iceland does not show integration with the European economy, despite its EEA membership. Furthermore, we found out that the currencies of Sweden, Norway and Denmark are more vulnerable to the market positive shock responses of the euro instead of negative shock responses. Finally, our study proposes that Sweden and Denmark might need to re-examine a possible EMU membership and Norway may reconsider a potential EU status. However, there are not enough evidence to provide a similar policy for Iceland.

Intraday return volatility in Saudi Stock Market: An evidence from Tadawul All Share Index

The association between risk and return is a significant concept in finance that has been studied in the past to a large extent. The stock market volatility is closely associated with the risk. The current study examines the intraday volatility pattern of stock market of Saudi Arabia by reviewing the stocks of Tadawul All Share Index (TASI). We obtain return data at 5-minute frequency from the SASEIDX starting on 25 October 2017 and ending on 9 May 2018. We examine the stock market volatility by using different symmetric and asymmetric GARCH models and observe that, the symmetric GARCH models showed a significant positive association between risk and return. Similarly, the asymmetric GARCH models show that the estimates were significant and the leverage estimate was negative and significant, indicating a no-leverage effect in the return series. Moreover, the asymmetric results suggest that negative shocks do not entail to future higher volatility than positive shocks. Therefore, the symmetric and asymmetric GARCH models are comfortable to capture the volatility of Saudi stock market from Intraday data.

Modelling Volatility Dynamics of Cryptocurrencies Using GARCH Models

Cryptocurrencies have become increasingly popular in recent years attracting the attention of the media, academia, investors, speculators, regulators, and governments worldwide. This paper focuses on modelling the volatility dynamics of eight most popular cryptocurrencies in terms of their market capitalization for the period starting from 7th August 2015 to 1st August 2018. In particular, we consider the following cryptocurrencies; Bitcoin, Ethereum, Litecoin, Ripple, Moreno, Dash, Stellar and NEM. The GARCH-type models assuming different distributions for the innovations term are fitted to cryptocurrencies data and their adequacy is evaluated using diagnostic tests. The selected optimal GARCH-type models are then used to simulate out-of-sample volatility forecasts which are in turn utilized to estimate the one-day-ahead VaR forecasts. The empirical results demonstrate that the optimal in-sample GARCH-type specifications vary from the selected out-of-sample VaR forecasts models for all cryptocurrencies. Whilst the empirical results do not guarantee a straightforward preference among GARCH-type models, the asymmetric GARCH models with long memory property and heavy-tailed innovations distributions overall perform better for all cryptocurrencies.

Relationship Between the Volatility of Stock Returns and the Volatility of Macroeconomic Variables: A Case of Turkey

In this paper, we examined the relationship between BIST-100 Index (SPI) and a set of macroeconomic variables volatility using Vector Autoregressive (VAR) model. The relationship between the stock market and macroeconomic variables has been subjected to serious economic research. A stock market plays important role for the reallocation of funds in many sectors of an economy. The macroeconomic factors make investors to choose the stock because investors are interested to know about the factors affecting the working of stock to manage their portfolios. Some investors show the stock prices volatility is based on directional trend in the stock prices but actually volatility is amount of fluctuation in stock prices. For this purpose we used the volatility of the variables. This study period 2006-2018 stock market using monthly data for Turkey is to examine the relationship between stock return volatility and macroeconomic volatility. We used the macroeconomic variables volatility these are industrial production (IP), money supply (M1), inflation rate (CPI), US dollar equivalent exchange rate (EX) and oil prices (OIL). We used montly data for the period between january 2006 and december 2018. Asymmetric GARCH models are used for the series volatility. The best performing GARCH model in these models are considered as volatlity. Exchange rate and industrial production index have an important effect on stock market volatility.

Modelling and forecasting long memory time series with Exponential and Switching GARCH models

There is some evidence that structural change or stochastic regime switching and long memory are intimately related concepts. However, long memory and regime switching in a stochastic process are properties that could be easily confused in a financial study. Using a modelling approach, the aim is to distinguish regime switching behaviour from long memory for the financial time series. In an empirical study the forecasting performance of symmetric, asymmetric, long memory and Markov switching GARCH model are compared using Tehran stock market daily returns. The results indicate that in out of sample performance, long memory exponential GARCH (FIEGARCH) model outperforms the competing models. To ensure the validity of the results, the value at risk (VaR) forecasts are obtained for each model and a loss function is calculated. A simple rule for distinguishing between long memory and structural break in financial and economic time series is suggested.