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The purpose of this study is to assess alternative financial stress indicators for India in terms of tracing crisis events, mapping with the business cycle and the macroeconomic effect of stress indices.

The study constructs the composite indicator of systemic stress of Hollo, Kremer and Lo Duca (2012) for India using two different methods for computing time-varying cross-correlation matrix, namely, exponentially weighted moving average (EWMA) and dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH). The derived indices are evaluated with widely used, equal variance and principal component weighting indices in terms of tracing stress events, mapping with the business cycles and the macroeconomic effect. For this purpose, the study identifies various episodes of financial stress and uses the business cycle dates in the sample covering from January 2001 to October 2018.

The results suggest that stress indices based on EWMA and DCC-GARCH accurately identify the well-known stress periods and capture the recession dates and show an adverse effect on economic activity. Primarily, the DCC-GARCH-based stress index emerges as a better indicator of stress because it efficiently locates all the major-minor events, traces the build-up of stress and reverts to the normal level during stable times.

The DCC-GARCH-based stress index is a very useful indicator for policymakers in regularly monitoring India’s financial conditions and providing timely identification of systemic stress to avoid adverse repercussion effects of the financial crisis.

The 2007–2008 financial crisis and subsequent recurrent instability in the financial markets highlighted the requirement for an appropriate financial stress indicator for a timely assessment of the system-wide financial stress. To the authors’ knowledge, this is the first study that incorporates the systemic nature of financial stress in the construction of stress indices for India and provides a holistic evaluation of the financial stress from an emerging country’s perspective.

This paper investigates the price discovery, volatility spillover, and asymmetric volatility spillover effects between the KOSPI 200 market and its futures contracts market. The investigation was performed using the VECM-DCC-GARCH approach. In the case of returns, we found a significant unidirectional information flow from the futures market to the spot market; this implies that the KOSPI 200 futures market plays an important role on the price discovery in the spot market. In addition, we found a strong bi-directional casualty involving the volatility interaction between the spot and futures markets; this implies that market volatility originating in the spot market will influence the volatility of the futures market and vice versa. We also found strong asymmetric volatility spillover effects between the two markets.

Cost estimation is a major concern while planning projects on public–private partnership (PPP) terms in developing countries. To bridge the gap of the right approximation of cost of capital, this study aims to sermon a significant role of investor’s risk perception as unsystematic risk in PPP-based energy projects.

To investigate the effective mechanism of determining cost of capital and valuing the capital budgeting, a pure-play method has been acquired to measure systematic risk. In addition, dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models have been applied to calculate weighted average cost of capital.

Initially, a joint cost of capital of energy-related projects has been calculated using DCC-GARCH and pure-play method. Investors risk perception has been discussed through market point of view using country risk premium modeling. Latter yearly betas have been calculated using DCC signifying the final outcomes that applying a dynamic model can provide a better cost estimation in emerging economies.

The findings are implicating that due to the involvement of international investors, domestic risk is linked with country risk. In such situations, market-related information is a key factor to find out the market performance, helping in the estimation of cost of capital through capital asset pricing model (CAPM). High dynamic nature of emerging economies causes an impediment in the estimation of cost of capital. Consequently, to calculate risk in dynamic markets, this study has acquired DCC model that can predict the value of beta factor.

Study contributes to the body of knowledge by addressing an important issue of investor’s risk perception and effective implication of CAPM using pure-play and DCC-GARCH when data is not promptly available in dynamic situations.

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.

The purpose of this paper is to econometrically investigate the level of financial co-integration of the least developed countries (LDCs) of Asia and Pacific region. In addition, the paper also tested the co-integration of LDCs with the world’s second largest economy “China.” For this, the paper employed the foreign exchange data sets of respective LDCs. It also aimed to assess the dynamic conditional correlation (DCC) between the foreign exchange rates of LDCs and China, and further, examined the past and current level of their co-relational dependence.

The authors created data sets namely LDCs of Asia and Pacific, LDCs of SAARC, LDCs of ASEAN, LDCs of Pacific, LDCs of SAARC and ASEAN, LDCs of ASEAN and Pacific, and LDCs of SAARC and Pacific. In addition, the authors tested the co-integration of these seven groups with China, and thus, making a total of 14 data sets. The analysis was carried out using the Johansen and Gregory-Hansen multivariate co-integration econometric techniques. To assess the DCC, multivariate DCC GARCH model was employed.

It was found that at the intra-regional level, exchange rates of LDCs of SAARC, ASEAN and Pacific were co-integrated and showed the existence of 1-3 co-integrating equations. At inter-regional level SAARC-ASEAN, ASEAN-Pacific and SAARC-Pacific were also co-integrated and showed 1-3 co-integrated equations. However, on the inclusion of China in the study, the degree of co-integration of exchange rate of China with LDCs of SAARC and ASEAN increased, while with Pacific, the result was mixed. Conditional correlation estimated of multivariate DCC GARCH model suggested that except for Afghanistan, there was an upward shift in the correlation dynamics of exchange rates of LDCs with China, post global financial crisis.

Asia and Pacific region constituted of 53 countries, of which 13 were LDCs. Enhanced financial integration among LDCs of Asia-Pacific region and also between LDCs and major economies of the region like China will strengthen economic and financial integration efforts in the region.

The present paper attempted a comparative assessment of the co-movements of the foreign exchange markets of LDCs, the countries which have remained largely neglected in academic discourses on financial integration.

The purpose of this paper is to investigate the short- and long-run dynamic linkages between selected cryptocurrencies, several major world currencies and major equity indices. The results show that despite sharing some common characteristics, the cryptocurrencies do not reveal any short- and long-term stochastic trends with exchange rates and/or equity returns. The dynamics of each cryptocurrency with the Chinese Yuan appears to be more turbulent than that with the other exchange rates. Each cryptocurrency appears to follow its own trend in the global financial market and is independent of the exchange rates or the global stock markets, thus making them suitable for inclusion in global investment portfolios.

The cryptocurrencies examined are Bitcoin, Dash, Ethereum, Monero, Stellar and XRP. In addition, data were collected on major exchange rates with respect to the US dollar, namely, the euro, British pound, Japanese yen and Chinese Yuan. Finally, the following major stock market indices were selected: SP500, DAX, DJIA, CAC, FTSE, NIKKEI, Hang Seng and Shanghai. The study applied vector autoregressive (VAR) model and Engle’s (2002) dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) specification.

First, it was found that cryptocurrencies do not interact with each other because their correlations are weak and do not share a common long-run path; thus they are not cointegrated. Second, impulse response analysis from the VAR models indicate different reactions of each cryptocurrency to both exchange rate and equity shocks and that cryptocurrencies appear to be isolated from market-driven shocks. Third, the ups and downs in the cryptocurrencies’ dynamic conditional correlations (from the DCC-GARCH models) indicate that all cryptocurrencies were susceptible to speculative attacks and market events.

This paper examines the dynamic linkages among the most important cryptocurrencies with major exchange rates and equity markets and, to the best of the authors’ knowledge, is the first paper to do so. Thus, interested market agents would gain valuable insights as to whether this new form of asset might be used for conducting monetary policies and portfolio construction on a global setting.

The paper contributes to the scant literature on the dynamic linkages among major cryptocurrencies and global financial assets. In general, given the differential relationships of each crypto with the equity markets, one could infer that they represent a decent short-run investment vehicle within a well-diversified, global asset portfolio (as they may increase the returns and reduce the overall risk of the portfolio).

The purpose of this paper is to propose an empirical procedure for examining the time‐varying features of cross‐market correlations in selected Gulf stock markets.

The paper directly infers the cross‐market linkages from the stock data using a multivariate dynamic conditional correlation GARCH model (DCC‐GARCH). The paper attempts to date the structural breaks in the time‐paths of the conditional correlation indices to investigate whether the cross‐market comovement encompasses significant changes in nature or not.

Conditional cross‐market correlations between studied markets are shown to be time‐varying, past‐dependent and subject to structural breaks. However, the comovements are still small within the Gulf region and insignificant between the Gulf stock markets and the world market.

Even though the paper attempted to relate the observed changes in market linkages to major economic and political events that the Gulf region experienced during the sample period, a more careful, in‐depth analysis is needed since the primary objectives of this paper consist only of measuring stock market comovements and detecting their possible structure changes.

For global investors, there is still room for international and regional diversification in Gulf markets, given the low degree of comovements documented in the study.

The application of the DCC‐GARCH model and structural change test in a linear framework appears to be suitable for studying the time‐varying properties of cross‐market linkages between markets in the Gulf region. It also provides information about the degree of financial integration of the studied markets with the world stock market through an analysis of the conditional correlation coefficients.

This paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.

First, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.

Using the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.

This study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.

The important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

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.

This paper aims to examine the contagion effects of Greece, Ireland, Portugal, Spain and Italy (GIPSI) and US stock markets on seven Eurozone and six non-Eurozone stock markets.

In this paper, a dynamic conditional correlation (DCC) model popularly known as DCC-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model given by Engle (2002) is applied to estimate the DCCs across sample markets.

Analyzing the Eurozone crisis period, the empirical results suggest that among GIPSI stock markets, Spain, Italy, Portugal and Ireland appear to be most contagious for Eurozone and non-Eurozone markets. The study finds that France, Belgium, Austria and Germany in Eurozone and UK, Sweden and Denmark in non-Eurozone are strongly hit by the contagion shock.

The findings of the study have significant implications for the concerned regulatory authorities, as it may provide an important direction for further policy research with regard to financial integration in the European Union (EU). From global investors’ perspective, the EU-based diversification strategies seem to be inefficient especially during Eurozone crisis.

To the best of the authors’ knowledge, this is the first study that examines the issue of financial contagion of Eurozone crisis for a large basket of stock markets of European countries comprising seven Eurozone and six non-Eurozone markets for the period 2009-2012. The study uses the Markov regime switching model to identify crisis period and utilizes the DCC estimates of DCC-GARCH to examine the patterns of financial contagion. The finding of this study is quite interesting and is different in several ways than existing studies in the literature.