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The encrypted money market has attracted the attention of investors all over the world. Among the encrypted currency, bitcoin is undoubtedly the most popular. Because blockchain technology is the crucial support of bitcoin, exploring the relationship between bitcoin and the blockchain index is necessary.

This paper uses the Granger causality test to explore the correlation between bitcoin and the blockchain index. Furthermore, their volatility is analyzed by a GARCH-class model.

The results show that no significant correlation exists between bitcoin and the blockchain index; external shocks aggravate the volatility of bitcoin and the blockchain index, and the volatility has a certain degree of sustainability; and blockchain index has obvious leverage, namely, its decline has a stronger impact.

The volatility of bitcoin and the blockchain index is crucial for investors.

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.

This paper aims to employ GARCH-class models (GARCH, IGARCH and CGARCH) to estimate the volatility persistence on crude oil, US, Gulf Corporation Council (GCC), Brazil, Russia, India and China (BRIC) stock markets. Also, the paper investigates the volatility spillover and the dynamic conditional correlation between crude oil, US stock index and stock indices of GCC and BRIC countries. The results prove a high degree of volatility persistence in the crude oil and stock markets. Based on the BEKK-GARCH and DCC-GARCH results, the paper finds strong evidence of the contagion effect of the oil shock and US financial crisis of 2008 on GCC and BRIC stock markets.

In the beginning, the paper uses univariate GARCH models to estimate the volatility persistence of the oil market, US stock market, and GCC and BRIC stock markets. Then, the paper uses a trivariate BEKK-GARCH model of Malik and Hammoudeh to examine the volatility spillover between oil market, US stock market and stock markets for GCC and BRIC countries. Finally, the paper analyses the dynamic conditional correlation between US market and each stock market of GCC and BRIC countries using the DCC-GARCH model. Also, the paper estimates the dynamic conditional correlation between oil market and all stock markets.

The results indicate the contagion effect of the oil shock and US financial crisis of 2008 on the GCC stock markets which are among the most important oil-exporting countries and also on BRIC stock markets which are among the emergent countries which are characterized by high economic growth level.

The contribution of this paper is to investigate the existence of contagion effect between oil market, US stock market and two panels of emerging stock markets which have different economic characteristics, the GCC and BRIC countries, during the oil shock and US financial crisis period of 2008-2009.

The purpose of this paper is to examine the short- and long-run spillover effect of international financial instability on emerging South Asian stock markets. The paper also investigates the financial integration regionally.

Granger causality test is used for short-run causal relations. Since results of preliminary test highlight the significant autocorrelations in stock returns, GARCH class models with extreme shocks in international financial market are utilized to test the long-run spillover impact on stock returns.

Results indicate significant short- and long-run spillover impacts of international financial instability on the stock returns. They highlight the significant co-integration of South Asian stock markets with the international market. Significant correlations in stock returns and volatility reveal the degree of regional integration to be high between India, Pakistan and Sri Lanka.

Business, political and market conditions of South Asian stock markets are fundamentally different from each other. These economies were liberalized at different time, which in turn may affect the degree of integration with international equity markets and regionally alike.

Financial liberalization has linked the South Asian stock markets to the rest of the world. Stock prices move in the same line with the emergence of global expected and unexpected economic shocks. The benefits that arise from the diversification of funds will be eradicated in the long run. Investors with long investment horizons will not actually benefit from portfolio diversification in South Asian equity markets. The Bangladesh stock market does not respond to volatility in international market in the short run and may be a good destination for short-term investment.

Pioneer efforts are made by utilizing a novel approach with the use of net volatility change in world financial instability for measuring the short- and long-run impacts. Given the emergence of South Asian stock markets, new insights into their vulnerability to world financial shocks provide interesting findings for portfolio diversification.

This paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories in Hong Kong were analyzed from February 1993 to February 2019 to test whether volatility clusters are present in the real estate market. Real estate price determinants were also investigated.

Autoregressive conditional heteroscedasticity–Lagrange multiplier test is used to examine the volatility clustering effects in these four kinds of real estate. An autoregressive and moving average model–generalized auto regressive conditional heteroskedasticity (GARCH) model was used to identify real estate price volatility determinants in Hong Kong.

There was volatility clustering in all four kinds of real estate. Determinants of price volatility vary among different types of real estate. In general, housing volatility in Hong Kong is influenced primarily by the foreign exchange rate (both RMB and USD), whereas commercial real estate is largely influenced by unemployment. The results of the exponential GARCH model show that there were no asymmetric effects in the Hong Kong real estate market.

This volatility pattern has important implications for investors and policymakers. Residential and commercial real estate have different volatility determinants; investors may benefit from this when building a portfolio. The analysis and results are limited by the lack of data on real estate price determinants.

To the best of the authors’ knowledge, this paper is the first study that evaluates volatility in the Hong Kong real estate market using the GARCH class model. Also, this paper is the first to investigate commercial real estate price determinants.

The BEKK GARCH class of models presents a popular set of tools for applied analysis of dynamic conditional covariances. Within this class the analyst faces a range of model choices that trade off flexibility with parameter parsimony. In the most flexible unrestricted BEKK the parameter dimensionality increases quickly with the number of variables. Covariance targeting decreases model dimensionality but induces a set of nonlinear constraints on the underlying parameter space that are difficult to implement. Recently, the rotated BEKK (RBEKK) has been proposed whereby a targeted BEKK model is applied after the spectral decomposition of the conditional covariance matrix. An easily estimable RBEKK implies a full albeit constrained BEKK for the unrotated returns. However, the degree of the implied restrictiveness is currently unknown. In this paper, we suggest a Bayesian approach to estimation of the BEKK model with targeting based on Constrained Hamiltonian Monte Carlo (CHMC). We take advantage of suitable parallelization of the problem within CHMC utilizing the newly available computing power of multi-core CPUs and Graphical Processing Units (GPUs) that enables us to deal effectively with the inherent nonlinear constraints posed by covariance targeting in relatively high dimensions. Using parallel CHMC we perform a model comparison in terms of predictive ability of the targeted BEKK with the RBEKK in the context of an application concerning a multivariate dynamic volatility analysis of a Dow Jones Industrial returns portfolio. Although the RBEKK does improve over a diagonal BEKK restriction, it is clearly dominated by the full targeted BEKK model.

This paper aims to investigate the behavior of volatility of Islamic equity indices toward fundamental risk factors. It focuses on the degree and structure of sensitivity to commodity price changes, global risk perception and term premium and whether crises and fragility periods have shaped the degree and structure of this sensitivity.

Quantile regression incorporating structural changes and GARCH-class model are used to establish how sensitivities are varying across volatility distribution depending on global events. The data are daily series of return indices, over the period spanning from January 1, 2001 until January 22, 2018.

The results show significant sensitivity to fundamental factors. The sensitivity is identified for different regional indices and intensified across quantiles. Speculation has shaped the structure of sensitivity at normal time, but correction holds at time of crisis. The results reveal that even if they share common features, commodities cannot be considered as homogeneous asset class. Indeed, the exact relationship cannot be observed at normal time in presence of speculation and information delay. However, at time of financial fragility and periods of crisis, the sensitivity is assigned with the plausible sign.

The obtained results present several policy implications as well for academics, portfolio managers and policy-makers. It opens new research paths for academic research, it helps in investment decisions, provides lessons for portfolio diversification, both for price discovery and hedging. The results serve as well to implement effective macroeconomic stabilization policies and even fiscal policies to counteract any inflationary impact of fundamental price changes on investors and Islamic banks.

This paper contributes to empirical literature by dealing with the sensitivity of Islamic equity indices to commodity prices and term premium along with the effect of investor sentiment. It pays attention to the financial stability of Islamic stock markets by investigating the sensitivity at normal time, during fragility periods and periods of crisis. It considers the financialization process of commodity markets and includes the term premium to control for rational expectations on term structure of interest rates and the VIX (Volatility index) as global risk perception to control for safety and risk aversion.

The purpose of this paper is to investigate the global influence of crude and refined oil futures prices on Dow Jones Islamic equity indices (DJIMI) during the recent global financial crisis under structural breaks in the conditional volatility of oil futures prices.

It aims at exploring the long-run and the short-run elasticity and causal relationships using an ARDL bound testing approach and a vector error correction model.

The main findings confirm the presence of long-run relationship for DJIM emerging markets index compared to other global and sub-regional developed indexes. Speed of adjustment to the long-run equilibrium is moderate and the effect of structural breaks, produced from nonlinear volatility model with long memory (LM), is overall not pronounced for that relationship. Short-run causality is bi-directional but long-run Granger causality does not run from refined oil to the DJIMI and crude oil.

The paper demonstrates the implicit extent of international financial integration of Islamic stock markets in light of the global influence of oil prices.

The findings offer some highlights to researchers, portfolio managers and policymakers.

The paper gives an answer to an identified need to test the position of Islamic equity markets as booming Islamic investment and socially responsible investment areas to the global influence of the new soaring path of oil markets. It uses as well bounds testing approach and tests weak and strong causalities under structural breaks. It considers as well LM behavior in oil prices along with the asymmetry property in oil prices.

This paper aims to investigate the predictability of stock returns under risk and uncertainty of a set of 11 emerging equity markets (EEMs) during the pre- and post-crash periods.

Listed indices are considered to serve the proxy of stock markets with a structural break in data for the period: 2000-2014. As preliminary results highlight the significant autocorrelations in stock returns, Threshold-GARCH (1,1) model is used to estimate the conditional volatility, which is further decomposed into expected and unexpected volatility.

Results highlight that the volatility has symmetric impact on stock returns during the pre-crash period and asymmetric impact during the post-crash period. While testing the relationship of stock returns, a significant positive (negative) relationship is found with expected volatility during the pre-crash (post-crash) periods. The stock returns are found positively related to unexpected volatility.

Business, political and other market conditions of sample stock markets are fundamentally different. These economies were liberalized in different years, which may affect the degree of integration with international equity markets.

The findings highlight that investors consider the impact of expected volatility in forecasting of stock returns during the growth period. They realize returns in commensurate to risk of their portfolios. However, they significantly reduce their investments in response to expected volatility during the recession period. The positive relationship between stock returns and unexpected volatility highlights the fact that investors realize extra returns for exposing their portfolios to unexpected volatility.

Pioneer efforts are made by using T-GARCH (1,1) procedure to analyse the problem. Given the emergence of emerging equity markets, new insight in dynamics of stock returns provide interesting findings for portfolio diversification under risk and uncertainty.

The purpose of the paper is to analyze the impact of quantitative easing (QE) performed in the USA on relationship between assets mainly from mining and oil industries. Based on the empirical results, the method of diversified portfolio creation has been proposed.

Nine DCC-GARCH-type models have been estimated for each group centered around a main asset: a company from the oil or mining industry, the appropriate currency pair for its market of origin, commodities which could be used for the diversification of risk involved in investing in a portfolio containing the company, and the largest company from the same industry listed on the US market. Each series of conditional correlations was analyzed with regard to the changes that occurred during the various stages of QE.

The correlations are shown to be stabilizing in the successive stages of QE. The most significant changes in the distribution of correlations can be observed after the first stage of QE. The effects of QE are evident not only in the USA but also in other countries; however, the level of its influence varies between different markets and assets. It is possible to diversify the inflation, currency and market portfolio risk by appropriately chosen asset decomposition.

The DCC model is limited, so to provide more precise results, more sophisticated models can be estimated and compared.

The paper investigate the fact of stabilization in financial markets relations. The findings may prove the validity of continuation of QE. A portfolio creation method has been proposed – it has been stated that including commodity in portfolio is more appropriate then only-bond–equity mix.

The new approach of analyzing financial stability has been proposed – the control for stability of conditional correlation.