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Previously elaborated research works, dealing with the political uncertainty effect on stock market, have been primarily concerned with such political events as terrorist attacks, elections, wars, natural catastrophes and financial crashes. Such little research has been concerned with civil uprisings and revolutionary movements, as crucial sources of political uncertainty. The purpose of this paper is to study the impact of political uncertainty (resulting from the Tunisian Revolution) on the volatility of major sectorial stock indices in the Tunisian Stock Exchange (TSE).

The authors apply the fractionally integrated exponential generalized autoregressive conditional heteroscedasticity model (FIEGARCH), which helps maintain a direct shock-persistence as well as a shock asymmetric volatility measurement. This model is applied to the daily returns relevant to nine sectorial stock indices and to the Tunisian benchmark index (TUNINDEX) with respect to three sub-periods (before, during and follows the Tunisian Revolution).

The reached findings suggest that the shock impact throughout the Revolution period on construction, industries, consumer services, financial services, financial companies indices’ sectorial and the TUNINDEX return volatilities have proven to be permanent, while its persistence on the other indices has been discovered to be transitory. In addition, the achieved results appear to reveal a low leverage effect on all indices. This result seems to be very important since the Tunisian Revolution turns out to have a very important effect on the TSE.

The paper’s empirical contribution lies in using the FIEGARCH approach to model the Tunisian sectorial indices’ volatility dynamics, persistence degree and leverage effect. This contribution goes a long way in helping regulators and international investors to further recognize the extent to which political instability does participate in affecting the TSE.

The main purpose of this paper is to examine the asymmetry and long memory properties in the volatility of the stock indices of the PIIGS economies (Portugal, Ireland, Italy, Greece and Spain).

The paper utilizes the wavelets approach (based on Haar, Daubechies‐4, Daubechies‐12 and Daubechies‐20 wavelets) and the GARCH class of models (namely, ARFIMA (p,d′,q)‐GARCH (1,1), IGARCH (1,1), FIGARCH (1,d,0), FIGARCH (1,d,1), EGARCH (1,1) and FIEGARCH (1,d,1)) to accomplish the desired goals.

The findings provide evidence in support of the presence of long range dependence in the various proxies of volatility of the PIIGS economies. The results from the wavelet approach also support the Taylor effect in the volatility proxies. The results show that ARFIMA (p,d′,q)‐FIGARCH (1,d,0) model specification is better able to capture the long memory property of conditional volatility than the conventional GARCH and IGARCH models. In addition, the ARFIMA (p,d′,q)‐FIEGARCH (1,d,1) model is better able to capture the asymmetric long memory feature in the conditional volatility.

This paper has both methodological and empirical originality. On the methodological side, the study applies the wavelet technique on the major proxies of volatility (squared returns, absolute returns, logarithm squared returns and the range) because the wavelet‐based estimator exhibits superior properties in modeling the behavior of the volatility of stock returns. On the empirical side, the paper finds asymmetry and long range dependence in the conditional volatility of the stock returns in PIIGS economies using the GARCH family of models.

The purpose of this paper is to study the volatility spillover among 33 worldwide sovereign Credit Default Swap (CDS) markets and their underlying bond markets.

In contrast to prior studies, the authors incorporate heteroscedasticity, asymmetric leverage effects and long-memory features of sovereign credit spreads simultaneously through a bivariate FIEGARCH model and a Bayesian cointegrated vector autoregressive model.

Similar to the literature, the findings confirm that strong evidence of credit risk spillover between credit markets is accentuated during two recent crisis periods. However, the country-by-country analysis indicates that countries exhibit different sensitivity levels and divergent reactions to financial shocks. Further, the authors show that the bidirectional interrelationship evolves over time and across countries emphasizing the necessity of time-varying national regulatory policies and trading positions.

Based on a large data set that covers the recent two financial crises and using complex methods, the work focuses on sovereign tensions that have repercussions on banks’ solvency and refinancing conditions. Yet, the study is a hot topic since that during crisis periods in the financial markets, direct and indirect interconnections increase between sovereign risk and banking risk. Using new econometric approaches, the results show that each country exhibits a different behavior toward the credit risk which is relevant to both portfolio managers and policy makers. The time-varying spillover effects detected between markets are an accurate indicator of financial stability, allowing policy makers to put in place personalized economic policies. On the other hand, markets’ participants could take advantages of the results by adjusting their trading and hedging positions on the dynamic co-movements. The findings reveal, as well, that the sovereign crisis has more weakened the global financial and banking system than the subprime crisis. The authors previously tackled the cross-country contagion phenomenon in the CDS markets, and this manuscript builds on the prior study to enhance the obtained results.

This paper compares the out-of-sample forecasting performance of three long-memory volatility models (i.e., fractionally integrated (FI), break and regime switching) against three short-memory models (i.e., GARCH, GJR and volatility component). Using S&P 500 returns, we find that structural break models produced the best out-of-sample forecasts, if future volatility breaks are known. Without knowing the future breaks, GJR models produced the best short-horizon forecasts and FI models dominated for volatility forecasts of 10 days and beyond. The results suggest that S&P 500 volatility is non-stationary at least in some time periods. Controlling for extreme events (e.g., the 1987 crash) significantly improved forecasting performance.

The purpose of this paper is to investigate the time series behavior of co‐movements among 11 European real estate securities markets, with each other as well as between country‐averages, over the sample period from January 1999 to January 2010 by utilizing the asymmetric dynamic conditional correlation (ADCC) technique, long‐memory tests and multiple structural break methodology.

First the ADCC from the multivariate GJR‐GARCH model is used to estimate the pair‐wise conditional correlations between the 11 securitized real estate markets. Then, the 11 country‐average conditional correlation series is subject to a battery of four long‐memory tests to form an “on the balance of evidence” picture; the semi‐parametric Geweke and Porter‐Hudak procedure and Robinson test, as well as the non‐parametric Hurst‐Mandelbrot R/S and Lo's modified R/S tests. Finally, the Bai and Perron's multiple structural break methodology seeks to test whether the average conditional correlations are subject to regime switching via the detection of breaks in the co‐movements of real estate securities returns.

Low to moderate conditional correlations are found for these European real estate securities market and a higher level of correlation in the aftermath of the global financial crisis. The long‐memory correlation effect is present for nine European real estate securities markets. In addition, the conditional correlations are subject to regime switching with two structural breaks in four country‐average correlation series. Across the regimes, a higher level of correlation is linked to a higher level of volatility and a lower level of return, and this happened around the global financial crisis period.

The findings that national real estate securities correlations exhibit time‐varying and asymmetric behavior can help investors understand how real estate securities will co‐move in different market scenarios (e.g. “crisis” and “non‐crisis” times). Moreover, the process of dynamic covariance analysis and forecasting (the ultimate objective in portfolio management) should not rely too much on short‐term autoregressive moving average models. Instead, a combination of some appropriate long‐range dependence models and regime‐switching specifications is needed.

This paper offers useful insights into the time series behavior of average dynamic conditional correlations in European public property markets.

This chapter investigates the presence of a difference in the systemic risk level between Islamic and conventional banks in Bangladesh. The authors compare systemic resilience of three types of banks: fully fledged Islamic banks, purely conventional banks (CB), and CB with Islamic windows. The authors use the market-based systemic risk measures of marginal expected shortfall and systemic risk to identify which type is more vulnerable to a systemic event. The authors also use ΔCoVaR to identify which type contributes more to a systemic event. Using a sample of observations on 27 publicly traded banks operating over the 2005–2014 period, the authors find that CB is the least resilient sector to a systemic event, and is the one that has the highest contribution to systemic risk during crisis times.

This paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research.

The author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding.

This work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily.

The author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals.

To the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.

In this chapter, we investigate the effect of long memory in volatility on the accuracy of emerging stock markets risk estimation during the period of the recent global financial crisis. For this purpose, we use a short (GJR-GARCH) and long (FIAPARCH) memory volatility models to compute in-sample and out-of-sample one-day-ahead VaR. Using six emerging stock markets index, we show that taking into account the long memory property in volatility modelling generally provides a more accurate VaR estimation and prediction. Therefore, conservative risk managers may adopt long memory models using GARCH-type models to assess the emerging market risks, especially when incorporating crisis periods.