Evidence Of Volatility Clustering Research Articles

Estimating Return Volatility of Stock Market Indices: The Case of Boursa Kuwait

This study empirically investigates the conditional variance (volatility) in the daily returns of Boursa Kuwait’s market index, along with seven sectoral indices, for the period of 13 May 2012 to 1 March 2018. The returns results exhibited leptokurtosis, were skewed left, and were not normally distributed; additionally, there was evidence of ARCH effects. The symmetric GARCH model results demonstrated evidence of volatility clustering and persistence; the GARCH-M model results showed a negative relationship between the indices’ returns and risk. The conditional variance (volatility) is affected by positive shocks, leading to positive asymmetric values of EGARCH and negative asymmetric values of TGARCH. From this, it can be inferred that good news has a greater impact on the volatility of index returns than bad news. The research results are crucial for investors, risk forecasters, and policy makers.

Global Oil Price Shocks and Effects on Economic Growth: An Econometric Investigation of Nigeria

This study is necessitated for the reason that global oil price shocks are bound to affect the pace of economic growth in Nigeria. Given that Nigeria is a net oil-exporting country makes it particularly vulnerable to oil price fluctuations. The study made use of secondary data covering the period from 1990 to 2019. While the Augmented Dickey-Fuller unit root test was used for preliminary analysis; ordinary least square (OLS) regression analysis was used for short-run estimates. A combination of Johansen Co-integration test, Vector Auto Regression analysis, Granger causality test, Variance Decomposition, Impulse Response tests and the ARCH/ GARCH modelling techniques were used for long run estimation All the tests helped to confirm the integrity of our models. The findings of the study indicate that, in the short run, there was sufficient evidence to show that oil price changes have a significant effect on economic growth. For the long run test, the Trace statistics and Max Eigenvalue tests point to a case of non-integration. At a ten year horizon, 71.31% of the variance in economic growth is explained by shocks; while the balance of 28.69% was accounted for by the changes in the global price of crude oil. In other words, the growth of the Nigerian economy has to do with the economy itself and to some extent, fluctuations or instability associated with the global prices of oil shocks. The ARCH/GARCH analysis indicates that there exists a first-order ARCH effect and that the GARCH in mean term was also significant. Succinctly put, the above results suggest that though erratic, there is evidence of volatility clustering of oil price on economic growth in Nigeria. The study, therefore, recommends that Nigeria splay down on the continued dominance of primary production and export and low-value addition. There is a need for a paradigm shift. Nigeria’s economic growth should be driven by a diversified production structure, essentially driven by growth in manufacturing as it would increase job offer, raise productivity and incomes. Otherwise, the Nigerian economy will remain trepid, fragile and susceptible to shocks emanating from global oil price fluctuations. Poverty is likely to persist in Nigeria without a robust manufacturing sector where innovation and technology would improve value addition and raise productivity. Lastly, since an average economy is cyclical, whence the Nigerian economy can pull through the present economic recession occasioned by the Coronavirus pandemic, she must learn to save for the rainy day. Nigeria should draw lessons from history and from past mistakes in order to avert the vagaries associated with oil price volatilities and consequent budget alignment and re-alignments.

Volatility Estimation Using Symmetric and Asymmetric Models in Oil Exporting Emerging Markets

The study empirically investigates the volatility pattern of thirteen emerging economies which are predominantly oil exporting countries. It is based on the time series data which consists of monthly closing price data of their index for a ten-year period from 01 January 2008 to 31 December 2017. Emerging markets are considered as investment destinations due to the presence of risk premium which has made the stock markets of these countries more volatile. Added to this is that these countries underwent crisis due to the sharp decline in crude oil prices as they were primarily dependent on oil exports. Hence it is a significant to study the volatility behavior of these countries. The study has been done by employing both symmetric and asymmetric models of generalized autoregressive conditional heteroscedastic. As per Akaike Information Criterion (AIC), Log likelihood and Schwarz Information Criterion (SIC) the study provides evidence that GARCH (1,1) and TGARCH(1,1) estimations are found to be the most appropriate model that fits symmetric and asymmetric volatility respectively for all the thirteen countries. There was evidence of volatility clustering and leptokurtic in all the countries considered in the study. While EGARCH model revealed no support of existence of leverage on the stock returns, TGARCH supported existence of leverage in case of four countries. The tests for asymmetries in volatility indicate the size effect of the news, reaffirmed through the results of sign bias tests and news impact curves, which indicate that the size effect is stronger for bad news than the good news for countries which supported existence of leverage.

The Spatial Heterogeneity of the Time-varying Impact of Shocks on Volatility: Some Evidence from MSA Housing Markets

Executive Summary. In this study, we investigate the time-varying response of conditional variance to endogenous and exogenous shocks. Using MSA-level monthly data spanning 31 years and two asymmetric volatility models, we find evidence of volatility clustering and the asymmetric effects of good and bad news on conditional variance. The conditional variances of “superstar” cities suggest that they strongly respond to good news but mildly respond to bad news. Conversely, the conditional variances of Southern and Midwestern cities in the U.S. respond strongly to bad news but marginally to good news. The evidence has important implications for the pricing of real estate derivatives and on the adoption of dynamic hedging strategies after major shocks.

Exchange rate volatility in West African countries: is there a shred of Spillover?

PurposeThe purpose of this paper is to evaluate selected West African currencies/US dollar exchange rates for the evidence of volatility spillover. Specifically, the paper examines West African CFA franc, Gambian dalasi and Nigerian naira exchange rates in relation to the USD, for any evidence of shock and volatility spillover.Design/methodology/approachThe author employs multivariate GARCH (1,1)–BEKK model which enables the evaluation of the interaction within the volatility of two or more series because of its capability to detect volatility spillover among time series observations, as well as the persistence of volatility within each series.FindingsThe major findings of this study are as follows: there is evidence of volatility clustering in West African CFA franc, Gambian dalasi and Nigerian naira exchange rates in relation to the USD. There is evidence of bi-directional shock and volatility spillover between the Nigerian naira and West African CFA franc/USD exchange rates, and uni-directional shock spillover from the Gambian dalasi to the West African CFA franc/USD exchange rates. There is, however, no evidence of exchange rate shock and volatility spillover between Nigerian naira and Gambian dalasi.Originality/valueAlthough considerable literature exists on the volatility of exchange rate in West Africa and comparative analysis of exchange rates volatility in few countries of West Africa, there is absence of empirical studies on exchange rate volatility spillover among countries in the region. Since containing exchange rate volatility is one of the major objectives of monetary policy, understanding the nature and direction of exchange rate volatility spillover would propel formulation exchange rate policies that would minimise exchange rate uncertainty and entrench sustainable development. In addition, the nature of exchange rate volatility spillover between West African countries would provide basis for international traders and foreign portfolio investors to develop effective strategies for hedging against exchange rate shocks that are propagated across countries by designing appropriate risk management techniques.

Value and Size Effects in the Stock Market of the Philippines

Several market abnormalities, such as the small size effect or the value effect, have been found in the stock markets across the world. In this article it is analyzed the case of the stock market of the Philippines. The Philippines, while having a relatively large economy and a capital market with long history, has attracted less research than other Asian countries, such as China or Japan. This is perhaps due to the much larger size of the economies and capital markets of those countries. Nevertheless the stock market of the Philippines is important enough to warrant attention. It will be shown that in recent years there is no indication of a value effect or a small size effect in the stock market of the Philippines, which is surprising given the amount of articles finding such results in other countries. The results were consistent when using the entire dataset as well as when comparing each year individually. It was also found, using weekly returns, that value and growth stocks as well as small and large companies present volatility clustering, which is a result more consistent with the existing literature in other markets. There are less evidence of volatility clustering when using monthly returns rather than when using weekly returns.

Modeling the stock price returns volatility using GARCH(1,1) in some Indonesia stock prices

In the financial field, volatility is one of the key variables to make an appropriate decision. Moreover, modeling volatility is needed in derivative pricing, risk management, and portfolio management. For this reason, this study presented a widely used volatility model so-called GARCH(1,1) for estimating the volatility of daily returns of stock prices of Indonesia from July 2007 to September 2015. The returns can be obtained from stock price by differencing log of the price from one day to the next. Parameters of the model were estimated by Maximum Likelihood Estimation. After obtaining the volatility, natural cubic spline was employed to study the behaviour of the volatility over the period. The result shows that GARCH(1,1) indicate evidence of volatility clustering in the returns of some Indonesia stock prices.

An empirical test of calendar anomalies for the Indian securities markets

Purpose– The purpose of this paper is to test prominent calendar anomalies for Indian securities markets those are commonly reported for advanced markets.Design/methodology/approach– The study considers closing values of 11 different indices of National Stock Exchange India, for the period 1994-2014. By using dummy variable regression technique, five different calendar anomalies namely day of the week effect, month of the year effect, mid-year effect, Halloween effect, and trading-month effect are tested. Also, the evidence of volatility clustering has been tested through the application of generalized autoregressive conditional heteroscedasticity (GARCH)-M models.Findings– The results display weak evidence in support of a positive Wednesday effect. The results also display weak evidence in support of a positive April and December effect. The results show strong evidence in support of a positive September effect. The Halloween effect was not found significant. The test of mid-year effect provides evidence that the returns obtained on the second-half or the year are considerably higher than those obtained during the first half. The test of interactions effects showed possible presence of interactions among various effects. The GARCH-based tests display strong evidence in support of volatility clustering.Practical implications– The results have several implications for investors, regulators, and researchers. For investors, the trading strategies based on results obtained have been discussed. Similarly, certain key implications for regulators have been described.Originality/value– The originality of the paper lies in the long time frame and multiple indices covered. Also, the study analyses five different calendar anomalies and the interactions among these effects. These analyses provide useful insights regarding returns predictability for the Indian securities markets.

Financial Integration Among the ASEAN 5 + 3 Stock Markets: A Preliminary Look at the First 10 Years of the New Millenium

The purpose of this study is to investigate the financial integration of the stock markets of the ASEAN 5 + 3 countries. These countries include Indonesia, Malaysia, Philippines, Singapore, Thailand, China, Japan, and South Korea. The research determined the stock return volatility for each country’s index during the first decade of the new millennium. The findings showed that there is the presence of integration and co-integration with Philippine index’s return with the index’s returns of the following countries: Indonesia, Singapore, and Thailand. Furthermore, there is evidence of volatility clustering in these stock markets. The study concluded with the policy implications of greater integration in light of the planned cross trading among four ASEAN bourses, namely, Philippines, Singapore, Thailand, and Malaysia by 2012.

Market Liberalization and Trading in Korea

This paper reports on the trading behavior of major participants, investment trust companies, banks, and foreigners in South Korea in the period after the currency markets were liberalized and the limits on foreign investments were lifted. It was found that trading in the spot currency market was impacted by volatility in the daily Won/USD rates. As the daily unexpected range expanded (narrowed), daily spot trading volume and volatility increased (decreased). This is evidence of asymmetric trading behavior on the part of market participants. It was found that only investment trust companies adjusted their spot positions by trading USD futures as a response to unexpected volatility changes of the exchange rate. There is evidence of volatility clustering of the trading volatilities across Korean markets and trader types and no signs of market instability was found.

FORECASTING FORWARD EXCHANGE RATE RISK PREMIUM IN SINGAPORE DOLLAR/US DOLLAR EXCHANGE RATE MARKET

In this research, monthly forward exchange rates are evaluated for possible existence of time varying risk premia in Singapore forward foreign exchange rates against US dollar. The time varying risk premia in Singapore dollar is modeled using non-Gaussian signal plus noise models that encompass non-normality and time varying volatility.The results from signal plus noise models show statistically significant evidence of time varying risk premium in Singapore forward exchange rates although we failed to reject the hypotheses of no risk premium in the series. The results from Gaussian versions of these models are not much different and are in line with Wolff (1987) who also used the same methodology in Gaussian settings.Our results show statistically significant evidence of volatility clustering in Singapore forward exchange rates. The results from Gaussian signal plus noise models also show statistically significant evidence of volatility clustering and non-normality in Singapore forward foreign exchange rates. Additional tests on the series show that exclusion of conditional heteroskedasticity from the signal plus noise models leads to false statistical inferences.

JUMP, NON-NORMAL ERROR DISTRIBUTION AND STOCK PRICE VOLATILITY — A NONPARAMETRIC SPECIFICATION TEST

This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, GARCH-jump model with Normal, and Student t-distribution assumption as well as nonparametric specification test of these models. We fit these models to Dhaka stock return index from 20 November 1999 to 9 October 2004. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return and the jump probability were significantly higher after 27 November 2001. The model introducing GARCH jump effect with normal and Student t-distribution assumption can better fit the volatility characteristics. We find that RW-GARCH-t, RW-AGARCH-t RW-IGARCH-t and RW-GARCH-M-t can pass the nonparametric specification test at 5% significance level. It is suggested that these four models can capture the main characteristics of Dhaka stock return index.