Detection of Breakpoints in Volatility

In this article, we test for the presence of structural breaks in volatility by two alternative approaches: the Iterative Cumulative Sum of Squares (ICSS) algorithm and wavelet analysis. Specifically, we look at the effect of the outbreak of the Asian crisis and the terrorist attacks of September 11, 2001 on Emerging Asia. Europe. Latin America and North America's stock markets. In addition, we focus on the behavior of interest rates in Chile after the Central Bank switched its monetary policy interest rate from an inflation-indexed to a nominal target in August 2001. Our estimation results show that the number of shifts detected by the two methods is substantially reduced when filtering out the data for both conditional heteroskedasticity and serial correlation.

A Time Series Analysis of Major Indexes Using GARCH Model with Regime Shifts

There has always been a great interest in learning about changes in the volatility patterns of stocks and other time series due to exogenous shocks. Researchers and investors have also been curious to study the effect of unanticipated shocks on persistence of volatility over time. This paper studies three major indexes and utilizes the Iterated Cumulative Sums of Squares (ICSS) algorithm to capture time periods of sudden changes in volatility. The findings suggest that persistence of shocks to volatility is not as high as generally perceived. Volatility persistence declines significantly when regime shifts are combined with a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This paper provides important implications for investors and financial researchers.

The Impact of Structural Breaks on Volatility Linkages between Asian Stock and Oil Futures Markets

This paper investigates the impact of structural breaks on volatility spillovers between Asian stock markets (China, Hong Kong, India, Indonesia, Japan, Korea, Singapore, and Taiwan) and the oil futures market. To this end, we apply the bivariate DCC-GARCH model to weekly spot indices during the period 1998-2015. The results reveal significant volatility transmission for the pairs between the Asian stock and oil futures markets. Moreover, we find a significant variability in the time-varying conditional correlations between the considered markets during both bullish and bearish markets, particularly from early 2007 to the summer of 2008. Using the modified ICSS algorithm, we find several sudden changes in these markets with a common break date centred on September 15, 2008. This date corresponds to the collapse of Lehman Brothers which is considered as our breakpoint to define the global financial crisis. Also, we analyse the optimal portfolio weights and time-varying hedge ratios based on the estimates of the multivariate DCC-GARCH model. The results emphasize the importance of overweighting optimal portfolios between Asian stock and the oil futures markets.

Effect of Volatility Changes on Emerging Stock Markets: The Case of Jordan

<p>The Middle East financial markets have experienced several unexpected volatility shifts during the last two decades had recorded a serious impact on these markets and caused a financial turmoil that has elevated the uncertainties in the region. In view of this, more empirical findings should be learned and documented for future benefits. As one of the affected countries, Jordan was chosen as a case to provide empirical insight on the matter. This paper analyzed the behavior of Jordan’s stock market (Amman Stock Exchange, ASE) during the intervals of high uncertainty. It highlighted the impact of volatility on this market in terms of its efficiency and returns, during 2004-2012 periods, by utilizing the iterated cumulative sums of squares (ICSS) algorithm, GARCH and GARCH-M models. Sudden changes in volatility seem to arise from the evolution of emerging stock markets, exchange rate policy changes and financial crises. Evidence also reveals that when sudden shifts are taken into account in the GARCH models, the persistence of volatility is reduced significantly in every series. Research results provided significant empirical evidence for positive risk-return relationship in the stock exchange. Moreover, this study also found that the stock market, across all sectors, was more sensitive to global news events as compared to the local events. The asymmetrical responses to good and bad news were also an important characteristic of the ASE market behavior.</p>

VOLATILITY IN STOCK RETURN SERIES OF VIETNAM STOCK MARKET

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.

MODELING DYNAMIC CORRELATIONS AND SPILLOVER EFFECTS OF COUNTRY RISK: EVIDENCE FROM RUSSIA AND KAZAKHSTAN

Oil economies in the Former Soviet Union (FSU) region, with geographical proximity to each other, are usually impacted by some common risk factors, which make their country risks closely correlated. This paper focuses on correlation between country risks and investigates the spillovers of country risk returns (CRR). Taking Russia and Kazakhstan for example, firstly, this paper identifies the structural breaks in CRR series, using iterated cumulative sums of squares (ICSS) algorithm. Secondly, on the assumption that there may be similarity in structural breaks of CRR series of the two countries, Vector Autoregression (VAR) process and Granger causality test are used to identify whether there are mean spillovers of CRR series. Finally, the volatility spillovers are captured by using multivariate conditional volatility models in the framework of the BEKK models. Empirical results show that (1) there are significant unidirectional mean spillovers from Russia to Kazakhstan; (2) there are asymmetric bidirectional volatility spillovers between Russia and Kazakhstan; and volatility spillover effects from Russia to Kazakhstan are stronger.