FINANCIAL CRISES AND INTERNATIONAL STOCK MARKET VOLATILITY TRANSMISSION

This paper examines the interplay between stock market returns and their volatility, focusing on the Asian and global financial crises of 1997–98 and 2008–09 for Australia, Singapore, the UK, and the US. We use a multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model and weekly data (January 1992–June 2009). Based on the results obtained from the mean return equations, we could not find any significant impact on returns arising from the Asian crisis and more recent global financial crises across these four markets. However, both crises significantly increased the stock return volatilities across all of the four markets. Not surprisingly, it is also found that the US stock market is the most crucial market impacting on the volatilities of smaller economies such as Australia. Our results provide evidence of own and cross ARCH and GARCH effects among all four markets, suggesting the existence of significant volatility and cross volatility spillovers across all four markets. A high degree of time‐varying co‐volatility among these markets indicates that investors will be highly unlikely to benefit from diversifying their financial portfolio by acquiring stocks within these four countries only.     

A joint analysis of market indexes in credit default swap,volatility and stock markets

This paper analyses the joint dynamics of the CDS, volatility and stock markets using both VAR and Markov regime-switching VAR models with market index data. It shows that the joint behaviour of the three markets is better characterized by the Markov model with two regimes corresponding to low- and high-volatile market conditions. The relationship between changes in the market indexes under a regime is consistent with theory and persistent; the information transmission process of shocks to the markets is similar for the two regimes with a more important role for CDS shock; and the volatility in the money market is an important determinant of regime-switching. The findings have practical implications, particularly for hedging strategies with market indexes under different market conditions.     

A conditional autoregressive range model with gamma distribution for financial volatility modelling

The commonly used conditional autoregressive range model with Weibull distribution (henceforth WCARR) suffers from serious inlier problem. We conjecture that this problem is due to a misspecified distribution to the disturbance, and propose a conditional autoregressive range model with gamma distribution (henceforth GCARR) to model the volatility of financial assets. In this paper, we first discuss the theoretical properties of the GCARR model and then compare its empirical performance with the WCARR. Empirical studies are performed on a broad set of stock indices in different countries over different time horizons. Consistent with the conjecture, we find that the GCARR model can reduce not only the inlier problem but also the outlier problem of the WCARR model. The results indicate that our GCARR model describes the dynamics of the range-based volatility better than the WCARR model and thus serves as a better benchmark.     

Spillover Effects of the 2008 Financial Crisis in Latin America Stock Markets

This paper examines the transmission of the 2008 US financial crisis to four Latin American stock markets using daily stock returns from 2006 to 2010, analyzing before, during and after the 2008 financial crisis. The empirical evidence presents a financial contagion by showing persistently higher and more volatile pair-wise conditional correlations during the crisis period. This indicates there are structural changes in mean and volatility of the correlation coefficients due to the 2008 financial crisis in Latin American markets. The results here could be useful in international portfolio diversification decision-making in Latin American region. In addition, the predicting the volatility in different markets could be a useful input for reducing financial instability in crisis episodes to policy makers.     

Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market

We examine whether real or spurious long memory characteristics of volatility are present in stock market data. We empirically distinguish between true and spurious long memory characteristics by analysing different types and measurements of volatility, utilising different sampling frequencies and evaluating different financial markets. Because it is well known that long memory characteristics observed in data can be generated by either non-stationary structural breaks or slow regime-switching models, we additionally assess how the results of the analyses change during crisis periods by considering the effects of the US subprime mortgage crunch. The results support the presence of long memory characteristics that vary for diverse types and measurements of volatility, different financial markets, and distinct sampling periods, such as the pre-crisis and crisis periods. This result suggests that empirical investigations must be particularly careful in addressing long memory issues.     

Islamic financial markets and global crises: Contagion or decoupling?

This paper investigates the contagion effects of the global financial crisis (GFC) and Eurozone sovereign debt crisis (ESDC) on Islamic equity and bond markets. Using a sample of Islamic stock indices from various developed and emerging markets and the global Islamic stock and bond (sukuk) indices, we explore asymmetric conditional correlation dynamics across stable and crisis periods and across the two crises. The results fail to provide strong contagion evidence between conventional and Islamic equity and bond indices, supporting the decoupling hypothesis of the Islamic securities. Our findings imply that Islamic equities and bonds may provide a cushion against risk and instability, particularly in periods of turmoil. The small number of contagion cases mostly relates to the ESDC and developed Islamic stock indices. The findings also show that the Islamic emerging stock indices in the BRICS provide the most effective international portfolio diversification benefits compared to the Islamic developed indices.     

Daily happiness and stock returns: The case of Chinese company listed in the United States

Existing literature exclusively focuses on the association between local investor sentiment and local stock market performance. In this paper, we investigate the contemporaneous and the lead-lag relationship between local daily happiness sentiment extracted from Twitter and stock returns of cross-listed companies, i.e., the Chinese companies listed in the United States. The empirical results show that: 1) by respectively controlling for the firm capitalization, liquidity and volatility, there exists the largest skewness on the Most-happiness subgroup. (2) There exist bi-directional relationships between daily happiness sentiment and market variables, i.e., the stock return, range-based volatility and excess trading volume. (3) There are significantly positive stock returns, higher excess trading volume and higher range-based volatility around the daily happiness sentiment spike days. These findings not only suggest that there exists significant interdependence between online activities and stock market dynamics, but also provide evidence for the existence of “home bias”.     

Financial crises and regime-dependent dynamics

Generalized with the regime-dependent beliefs and regime-switching dynamics, the simple market-maker framework established by Day and Huang (1990) is capable to model all types of crises, that is, sudden crisis, disturbing crisis and smooth crisis, and to offer economic and dynamic justifications on how and why these crises appear. Moreover, the model simulations verify the salient qualitative and statistical properties commonly observed in the real financial data such as fat tails, volatility clustering, long range dependence, leverage effect and other stylized facts. Additionally, the model replicates the various chart patterns widely applied in the technical analysis.     

Responses of international stock markets to oil price surges: a regime-switching perspective

We propose an enhanced regime-switching model to investigate the relationships between oil price surges and stock market cycles in five oil-dependent countries. Our model accounts for the joint effects of the West Texas Intermediate (WTI) and Brent oil markets and simultaneously captures asymmetry, volatility persistence and regime shifts contained in the underlying financial data. We find that stock market returns strongly exhibit a regime-switching behaviour, but they react differently to the increases in the price of oil. More precisely, the conditional volatility of studied stock markets during the bear market phases is found to be less affected by oil price surges than during the bull market phases. Whether the effects of oil shocks are positive or negative depends greatly on the degree of reliance on imported oil, the share of the cost of oil in the national income and the degree of improvement in energy efficiency of a given country. Finally, the relatively opposite effects of the WTI and Brent oil markets suggest the potential of substitution between them as well as the necessity of a diversification strategy of oil supply sources.     

Regime dependent volatilities and correlation in international securitized real estate markets

This paper studies the role of regime shifts and time-varying volatilities in market integration in a Markov-switching volatility regime environment among the US, European and Asian developed securitized real estate markets. With a two-state volatility model, the study finds the co-dependence, co-movement and synchronization of volatility regime at the high volatility state are stronger between the US and European securitized real estate markets. Although correlations among the markets are higher in a high volatility regime than in a low volatility regime, there is limited evidence of contagious effects during the high volatility periods between some markets. Moreover, the unsecuritized real estate markets are different from their securitized equivalent in the volatility regime characteristics, correlation pattern and level, as well as the extent of correlation change and contagion effect in high volatility state. Thus, the regime-switching results from stock markets may not be automatically extended to the corresponding public real estate markets, and requires rigorous empirical scrutiny.     

Real exchange rate volatility,financial crises and exchange rate regimes

This article examines real exchange rate (RER) volatility in 80 countries around the world, during the period 1970 to 2011. Two main questions are raised: are structural breaks in RER volatility related to changes in exchange rate regimes or financial crises? And do these two events affect the permanent and transitory components of RER volatility? To answer these, we employ two complementary procedures that consist in detecting structural breaks in the RER series and decomposing volatility into its permanent and transitory components. Our results suggest that structural breaks in RER volatility coincidence with financial crises and certain changes in nominal exchange rate regimes. Moreover, our findings confirm that RER volatility does increase with the global financial crises and detect that the more flexible the exchange rate regime, the higher the volatility of the RER using a de facto exchange rate classification.     

Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

A range-based volatility approach to measuring volatility contagion in securitized real estate markets

We use a newly-developed time-varying range-based volatility model to capture the dynamics of securitized real estate volatility. The novelty of the model is the use of a smooth transition copula function to capture the nonlinear comovements between major REIT markets in the presence of structural changes. We then investigate the impact of extreme events on the volatility dependence in a broad set of 13 developed countries over the period from 1990 to 2012. We find that information transmission through the volatility channel can exhibit either bi- or uni-directional causality. In addition, financial contagion following the subprime crisis is found between the U.S. and Australia.     

Interactions between real economic and financial sides of the US economy in a regime-switching environment

This objective of this study is to examine the linkages between real (economic) and financial variables in the United States in a regime-switching environment that accounts explicitly for high volatility in the stock market and high stress in financial markets. Since the linearity test shows that the linear model should be rejected, we employ the Markov-switching VECM to examine the same objective using the Bayesian Markov-chain Monte Carlo method. The regime-dependent impulse response function (RDIRF) highlights the increasing importance of the financial sector of the economy during stress periods. The responses and their fluctuations are significantly greater in the high-volatility regime than in the low-volatility regime.     

Virtual integration of financial markets: a dynamic correlation analysis of the creation of the Latin American Integrated Market

This article investigates the role of virtual integration of financial markets on stock market return co-movements. In May of 2011, the Chilean, Colombian and Peruvian stock markets virtually integrated their stock exchanges and central securities depositories to form the Latin American Integrated Market (MILA). We utilize the dynamic conditional correlation model proposed by Engle (2002) to identify a statistically significant positive correlation between these markets. Moreover, we find strong evidence that the creation of the MILA increased the levels of dynamic correlation between stock returns. A higher correlation was also found during the dot-com bubble and the 2007 financial crises. Our results imply a decline in gains from international diversification by holding portfolios consisting of diverse stocks of these countries.     

Forecasting volatility in the Indian equity market using return and range-based models

In this article, we assess the time-varying volatility of the National Stock Exchange in the Indian equity market using unconditional estimators and asymmetric conditional econometric models. The volatility estimate and forecast is computed from the interday return and intraday range-based data of the exchange’s flagship index, CNX NIFTY, for the time period spanning 1 January 2009 through 31 December 2013. These are our findings: First, we determine that the time-varying volatility of the index is asymmetric with qualities of stationarity and leptokurtic distribution. Second, the one-step-ahead volatility forecast derived from the univariate time series parameters through the GJR-GARCH ?????process indicates that the model evaluation criteria of the autoregressive process tends towards range-based models vis-à-vis a return-based model. The validity of this methodology is further analysed with the superior predictive ability test, the outcome of which supports the use of range-based conditional models. Finally, among the evaluated range-based model variants, the model confidence set procedure favours the Yang–Zhang estimator as being better suited to forecast the exchange’s volatility than the ones by Parkinson, Garman–Klass and Rogers–Satchell.     

A Bayesian approach to model changes in volatility in the Mexican stock exchange index

We model the changes in volatility in the Mexican Stock Exchange Index using a Bayesian approach. We study the time series with a wide set of models characterized by a Markov switching heterogeneity. The advantage of this approach is that it allows for a broader spectrum of possible models since the estimation of the moments of the parameters is done using the finite mixture distribution MCMC method, without relying on assumptions about large sampling and mathematical optimization. This is particularly relevant for emerging markets’ financial data because of its special characteristics, like being more susceptible to jumps and changes in volatility caused by exchange rate swings, financial crises and oil and commodity prices. For model comparison, we use the marginal likelihood approach and the bridge sampling technique. The best representation of the data is given by a switching model with three states rather than any other autoregressive linear or non-linear model. The periods of volatility found by the model coincide with different financial crisis. Whereas other studies of volatility for the same market impose the Markovian model that captures changes in volatility, we let our model to be defined in an endogenous way.     

Realized EquiCorrelation: a bird’s-eye view of financial stress on equity markets

This article addresses the issue of measuring the level of aggregate financial stress on stock markets, which is a central issue for investors and policy-makers. To this end, Realized EquiCorrelation (REC) is obtained by plugging realized volatility as an input into the Dynamic EquiCorrelation (DECO) model where both the continuous and jump components of realized volatility are considered. An application is provided for the 20 major stock markets over January 2000–May 2014 using intra-day data. The results remarkably pick up financial stress periods.     

Banking,currency, stock market and debt crises in Spain, 1850–1995

What type of crisis is generated when debt increases? We extend the literature by framework by introducing currency and stock market crises in the analysis. We apply our proposal to the case of Spain, since this is a country that has experienced a very important amount of financial crises from the nineteenth century onwards. We find the same results as the previous literature for the determinants of banking and debt crises but substituting external and public debt with perpetual debt and where perpetual debt has a less important role than crises in the private sector. Moreover, we find evidence in favour of the hypothesis that currency crises depend strongly and positively on financial centre crises and negatively and mildly on perpetual debt. We justify the negative relalionship due to an inflation tax. We also find evidence in favour of the hypothesis that stock market crises depend only positively and strongly on financial centre crises.