Price discovery for cross-listed firms with foreign IPOs

We investigate the inter-market return and volatility linkages for an atypical case of firms with foreign IPOs that subsequently cross-listed in their domestic market. In particular, our data set consists of a unique sample of 29 Israeli firms that went public in the US (host market) and then cross-listed in the Israeli market (home market). To estimate the spillover effects, we employ bivariate GARCH models, assuming both constant and dynamic conditional correlation specifications. At the aggregate market level, we find unidirectional mean and volatility spillovers from the US to the Israeli market. For the portfolios of Israeli cross-listed stocks, we report significant spillovers, at both the mean and volatility levels, from the underlying stocks in the Israeli market to their American Depository Receipts (ADRs) but not vice versa. Thus, the home market dominates the host market in the price discovery process in this atypical international cross-listing case, providing new evidence in support of the home bias hypothesis. We also find that external shocks originating from the Middle East peace process have no impact on the conditional correlation between the two markets but external shocks originating from the world and regional markets impact the conditional correlation positively.     

Linkages between Indian and US financial markets: impact of global financial crisis and Eurozone debt crisis

This paper examines inter-linkages between Indian and US equity, foreign exchange and money markets using the vector autoregressive-multivariate GARCH-BEKK framework. We investigate the impact of global financial crisis (GFC) and Eurozone debt crisis (EZDC) on the conditional volatility and conditional correlation estimates derived from the multivariate GARCH model for Indian and US financial markets. Our results indicate that there is significant bidirectional causality-in-mean between the Indian stock market returns and the Rs./USD market returns, and significant unidirectional causality-in-mean from the US stock market returns to the Indian stock market returns. As regards volatility spillovers, we find that volatility in the Indian stock market rises in response to domestic as well as US financial market shocks but Indian financial market shocks do not impact the US markets. Further, impact of the recent crisis episodes on the covariance matrix is found to be significant. We find that volatility in the Indian and US financial markets significantly amplified during GFC. The conditional correlations across asset markets were significantly accentuated in the wake of the two crisis episodes. The impact of GFC on cross-market conditional correlations is higher for majority of the asset market pairs in comparison to the EZDC.     

A Conditional Assessment of the Relationships between the Major World Bond Markets

This paper uses a bivariate GARCH framework to examine the lead‐lag relations and the conditional correlations between 10‐year US government bond returns and their counterparts from the UK, Germany, and Japan. We find that while mean and volatility spillovers exist between the major international bond markets, they are much weaker than those between equity markets. The results also indicate that the correlations between the US and other major bond market returns are time varying and are driven by changing macroeconomic and market conditions. However, in contrast to the finding that the benefits of international diversification in equity markets evaporate during high‐stress periods, we find that the benefits of diversification across major government bond markets do not decrease during periods of extremely high bond market volatility or following extremely negative US and foreign bond returns.     

An international analysis of REITs and stock portfolio management based on dynamic conditional correlation models

This paper analyzes the volatility spillovers and asymmetry between REITs and stock prices for nine countries (Australia, Belgium, Germany, Italy, Japan, The Netherlands, Singapore, the United Kingdom, and the United States) using eight different multivariate GARCH models. We also analyze the optimal weights, hedging effectiveness, and hedge ratios for REIT-stock portfolio holdings with respect to the results. The empirical results indicate that dynamic conditional correlation (DCC) models provide a better fit than the constant conditional correlation models. The DCC with volatility spillovers and asymmetry (DCC-SA) model provides a better fit than the other multivariate GARCH models. The DCC-SA model also provides the best hedging effectiveness for all pairs of REIT-stock assets. More importantly, this result holds for all cases and for all models that we consider, which means that by taking spillover and asymmetry into consideration, hedging effectiveness can be vastly improved.     

Positivity constraints on the conditional variances in the family of conditional correlation GARCH models

In this article, we derive a set of necessary and sufficient conditions for positivity of the vector conditional variance equation in multivariate GARCH models with explicit modelling of conditional correlation. These models include the constant conditional correlation GARCH model of Bollerslev [1990. Review of Economics and Statistics 72, 498–505] and its extensions. Under the new conditions, it is possible to introduce negative volatility spillovers in the model. An empirical example illustrates usefulness of having such conditions in practice.     

Dynamic relationship between exchange rate and stock price: Evidence from China

The paper empirically analyzes the dynamic relationship between Renminbi (RMB) real effective exchange rate and stock price with VAR and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models using monthly data from January 1991 to June 2009. The results show that there is not a stable long-term equilibrium relationship between RMB real effective exchange rate and stock price. There are also not mean spillovers between the foreign exchange and stock markets. Furthermore, the paper examines the cross-volatility effects between foreign exchange and stock markets using likelihood ratio statistic. There exist the bidirection volatility spillovers effects between the two markets, indicating the past innovations in stock market have the great effect on future volatility in foreign exchange market, and vice versa.     

Volatility spillovers and dynamic correlation in European bond markets

This paper examines the dynamic linkages among the European bond markets. We model the price and volatility spillovers from the US bond market and the aggregate Euro area bond market to twelve individual European bond markets using an EGARCH model that allows for a dynamic correlation structure. Our results suggest that significant volatility spillovers exist from both the aggregate Euro area bond market and the US bond market to the individual European markets. Moreover, the introduction of the Euro has strengthened the volatility spillover effects and the cross-correlations for most European bond markets.     

Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection

During the recent European sovereign debt crisis, returns on EMU government bond portfolios experienced substantial volatility clustering, leptokurtosis and skewed returns as well as correlation spikes. Asset managers invested in European government bonds had to derive new hedging strategies to deal with changing return properties and higher levels of uncertainty. In this environment, conditional time series approaches such as GARCH models might be better suited to achieve a superior hedging performance relative to unconditional hedging approaches such as OLS. The aim of this study is to test innovative hedging strategies for EMU bond portfolios for non-crisis and crisis periods. We analyze single and composite hedges with the German Bund and the Italian BTP futures contracts and evaluate the hedging effectiveness in an out-of-sample setting. The empirical analysis includes OLS, constant conditional correlation (CCC), and dynamic conditional correlation (DCC) multivariate GARCH models. We also introduce a Bayesian composite hedging strategy, attempting to combine the strengths of OLS and GARCH models, thereby endogenizing the dilemma of selecting the best estimation model. Our empirical results demonstrate that the Bayesian composite hedging strategy achieves the highest hedging effectiveness and compares particularly favorable to OLS during the recent sovereign debt crisis. However, capturing these benefits requires low transactions cost and efficiently functioning futures markets.     

Volatility Spillovers and Contagion During the Asian Crisis: Evidence from Six Southeast Asian Stock Markets

Using a multivariate generalized autoregressive conditional heteroskedasticity (GARCH-M) model, we investigate volatility spillovers in six Southeast Asian stock markets around the time of the 1997 Asian crisis. We focus on interactions with the U.S. market as a world financial market, and with the Japanese market as a regional financial market. We also use bivariate GARCH-M models to examine the behavior of individual markets and their interactions with other markets in the region. All models lend support to the idea of the "Asian contagion," which started in Thailand and rapidly spread to other markets.     

Investigating the nature of interaction between crypto-currency and commodity markets

This paper investigates the dynamic relationship and volatility spillovers between cryptocurrency and commodity markets using different multivariate GARCH models. We take into account the nature of interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results confirm the presence of significant returns and volatility spillovers, and we identify the GO-GARCH (2,2) as the best-fit model for modeling the joint dynamics of various financial assets. Our findings show significant dynamic linkages and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices. We find that gold can serve as a safe haven in times of economic uncertainty, as it is a good hedge against natural gas and crude oil price fluctuations. We also find evidence of bidirectional causality between crude oil and natural gas prices, suggesting that changes in one commodity's price can affect the other. Furthermore, we observe that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, indicating that these cryptocurrencies may serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our study provides valuable insights for investors and policymakers regarding asset allocation and risk management, and sheds light on the dynamics of financial markets.     

Volatility transmission between oil prices and equity sector returns

This paper employs bivariate GARCH models to simultaneously estimate the mean and conditional variance between five different US sector indexes and oil prices. Since many different financial assets are traded based on these market sector returns, it is important for financial market participants to understand the volatility transmission mechanism over time and across these series in order to make optimal portfolio allocation decisions. We examine weekly returns from January 1, 1992 to April 30, 2008 and find evidence of significant transmission of shocks and volatility between oil prices and some of the examined market sectors. The findings support the idea of cross-market hedging and sharing of common information by investors.     

Does Bitcoin still own the dominant power? An intraday analysis

The study investigates the intraday dynamics and price patterns of the primary cryptocurrencies. The Granger Mackey-Glass (M-G) model is employed to examine the asymmetric and nonlinear dynamic interactions in the first moment using positive and negative returns. The bivariate BEKK-GARCH model is applied to identify cross-market volatility shocks and volatility transmissions in the cryptocurrency market. The intra-cryptocurrency market analysis reveals that Bitcoin contains predictive information that can nonlinearly forecast the performance of other digital currencies when cryptocurrency prices either are rising or declining. The dominant power of Bitcoin is not dismissed using the intraday data. Further, Bitcoin's intraday lagged shocks and volatility induces more rapid and destabilizing effects on the conditional volatility of other currencies than each of the other currencies does on BTC's conditional volatility. The virtual currency markets are dynamically correlated and integrated through first and second-moment spillovers.     

Volatility Transmission in the Real Estate Spot and Forward Markets

How shocks in one market influence the returns and volatility of other markets has been an important question for portfolio managers. In the finance literature, many studies found evidence of volatility spillovers across international markets, as well as between spot and futures markets. Although real estate is often regarded as a good vehicle for diversification, the dynamics of its volatility transmission have been largely ignored. This paper provides the first study to examine volatility spillovers between the spot and forward (pre-sale) index returns of the Hong Kong real estate market through a bivariate GARCH model. Transaction-based indices were used so that our volatility modelling was free from any smoothing problem. Our results showed that real estate returns exhibited volatility clustering, and the volatility of the forward market was more sensitive to shocks than the spot market. Moreover, volatility was mainly transmitted from the forward market to the spot market, but not vice versa.

Volatility spillovers and the effect of news announcements

We examine the effect of US and European news announcements on the spillover of volatility across US and European stock markets. Using synchronously observed international implied volatility indices at a daily frequency, we find significant spillovers of implied volatility between US and European markets as well as within European markets. We observe a stark contrast in the effect of scheduled versus unscheduled news releases. Scheduled (unscheduled) news releases resolve (create) information uncertainty, leading to a decrease (increase) in implied volatility. Nevertheless, news announcements do not fully explain the volatility spillovers, although they do affect the magnitude of volatility spillovers. Our results are robust to extreme market events such as the recent financial crisis and provide evidence of volatility contagion across markets.     

Hedging with futures: Efficacy of GARCH correlation models to European electricity markets

European electricity markets have been subject to a broad deregulation process in the last few decades. We analyse hedging policies implemented through different hedge ratios estimation. More specifically we compare naïve, ordinary least squares, and GARCH conditional variance and correlations models to test if GARCH models lead to higher variance reduction in a context of high time varying volatility as the case of electricity markets. Our results show that the choice of the hedge ratio estimation model is central on determining the effectiveness of futures hedging to reduce the portfolio volatility.     

Interdependence and Volatility Spillovers Under Market Liberalization: The Case of Istanbul Stock Exchange

This paper analyzes stock returns and volatility relations between the Istanbul Stock Exchange (ISE) and the global market as represented by stock markets in the US, the UK, Japan and Germany. Results from monthly data and multivariate cointegration tests suggest that the ISE became significantly integrated in the global market only in the period following market liberalization in late 1989. We also find evidence based on GARCH estimations that capital liberalization actually mitigated, rather than intensified, volatility in the ISE. Our results further suggest that the Asian crisis in mid‐1997 and the consequent Russian economic meltdown in mid‐1998 are partly responsible for the recent excessive volatility in the Turkish market. The results also identify the US and the UK markets as dominate sources of volatility spillovers for the ISE, even in the period following the Asian‐Russian crises. Consequently, it appears that the two matured markets of the US and the UK shoulder significant responsibility for the stability and financial health of smaller emerging markets like the ISE.     

Volatility spillovers between oil prices and the stock market under structural breaks

This paper employs univariate and bivariate GARCH models to examine the volatility of oil prices and US stock market prices incorporating structural breaks using daily data from July 1, 1996 to June 30, 2013. We endogenously detect structural breaks using an iterated algorithm and incorporate this information in GARCH models to correctly estimate the volatility dynamics. We find no volatility spillover between oil prices and US stock market when structural breaks in variance are ignored in the model. However, after accounting for structural breaks in the model, we find strong volatility spillover between the two markets. We compute optimal portfolio weights and dynamic risk minimizing hedge ratios to highlight the significance of our empirical results which underscores the serious consequences of ignoring these structural breaks. Our findings are consistent with the notion of cross-market hedging and sharing of common information by financial market participants in these markets.     

Firm size and volatility analysis in the Spanish stock market

Using Spanish stock market data, this paper examines volatility spillovers between large and small firms and their impact on expected returns. By using a conditional capital asset pricing model (CAPM) with an asymmetric multivariate GARCH-M covariance structure, it is shown that there exist bidirectional volatility spillovers between both types of companies, especially after bad news. After estimating the model, a positive and significant price of risk is obtained. This result is consistent with the volatility feedback effect, one of the most popular explanations of the asymmetric volatility phenomenon, and explains why risk premiums are much more sensitive to negative return shocks coming from the whole market or other related markets.     

MEAN AND VOLATILITY SPILLOVERS ACROSS MAJOR NATIONAL STOCK MARKETS: FURTHER EMPIRICAL EVIDENCE

This paper provides additional insight into the nature and degree of interdependence of stock markets of the United States, Japan, the United Kingdom, Canada, and Germany, and it reports the extent to which volatility in these markets influences expected returns. The analysis uses the multivariate GARCH-M model. Although they are considered weak, statistically significant mean spillovers radiate from stock markets of the U.S. to the U.K., Canada, and Germany, and then from the stock markets of Japan to Germany. No relation is found between conditional market volatility and expected returns. Strong time-varying conditional volatility exists in the return series of all markets. The own-volatility spillovers in the U.K. and Canadian markets are insignificant, supporting the view that conditional volatility of returns in these markets is “imported” from abroad, specifically from the U.S. Significant volatility spillovers radiate from the U.S. stock market to all four stock markets, from the U.K. stock market to the Canadian stock market, and from the German stock market to the Japanese stock market. The results are robust and no changes occur in the correlation structure of returns over time.     

Spillovers of stock return volatility to Asian equity markets from Japan and the US

This paper examines the magnitude of return and volatility spillovers from Japan and the US to seven Asian equity markets. I construct a volatility spillover model that deals with the US shock as an exogenous variable in a bivariate EGARCH for Japan and Asian markets. First, only the influence of the US is important for Asian market returns; there is no influence from Japan. Second, the volatility of the Asian market is influenced more by the Japanese market than by the US. Third, there exists an adverse influence of volatility from the Asian market to the Japanese market.