Transmission channels of international financial crises to African stock markets: the case of the euro sovereign debt crisis

This article investigates the effects of the European sovereign debt crisis on African stock markets within a Bayesian shrinkage VAR framework. This method allows us to consider both North African and Sub-Saharan African stock markets, and provides a flexible parsimonious specification. The results reveal varying reactions of the impulse response functions. The most exposed African stock markets are those of Egypt, South Africa and Mauritius, while the least affected stock market is, surprisingly, that of Ivory Coast. Our analysis shows that, in addition to direct transmission, several macroeconomic and market channels, such as commodities, exports, and exchange rates, are relevant. Specifically, countries with strong commercial links to European countries will be most impacted by the crisis. The severity of transmission also depends on the country’s dependence on commodities.     

Extracting portfolio management strategies from volatility transmission models in regime-changing environments: Evidence from GCC and global markets

Unlike previous studies, this paper uses the Multi-Chain Markov Switching model (MCMS) to examine portfolio management strategies based on volatility transmission between six domestic stock markets of Gulf Arab states (GCC) and global markets (i.e., the U.S. S&P 500 index and oil prices) and compares the results with those of the VAR model. Our volatility approach is range-based and not return-based which is traditionally used in estimating the optimal hedge ratios and portfolio weights. The results demonstrate the relative hedging effectiveness of the MCMS model compared to the VAR. We also highlight the time and regime dependency of the optimal hedge ratios and the portfolio weights for each selected pair of the considered markets conditional on the regime of the same market and the regimes of the other market. Policy implications on portfolio strategies under different states are also discussed.     

Are fundamentals still relevant for European economies in the post-Euro period?

A structural VAR model, with stock prices, real economic activity, a short-term interest rate and inflation, was applied to four European countries to investigate whether economic fundamentals play an important role in their national stock markets. The analysis considers the pre- and post-Euro introduction periods. In general, the results suggest a breakdown in the relationship between real economic activity and real stock returns during the post-Euro period. Second, impulse response analyses reveal that (shocks by) fundamental variables still influence somewhat real stock returns for some countries but the extent and nature of their impact differ among countries in the post-Euro period. Finally, an examination of equity risk premiums corroborates the above findings and, overall, they may be interpreted as the equity markets having a mind of their own, disconnected from the fundamentals and that they are significantly affected by foreign rather than country-specific forces.     

Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries

While the relationship between oil prices and stock markets is of great interest to economists, previous studies do not differentiate oil-exporting countries from oil-importing countries when they investigate the effects of oil price shocks on stock market returns. In this paper, we address this limitation using a structural VAR analysis. Our main findings can be summarized as follows: First, the magnitude, duration, and even direction of response by stock market in a country to oil price shocks highly depend on whether the country is a net importer or exporter in the world oil market, and whether changes in oil price are driven by supply or aggregate demand. Second, the relative contribution of each type of oil price shocks depends on the level of importance of oil to national economy, as well as the net position in oil market and the driving forces of oil price changes. Third, the effects of aggregate demand uncertainty on stock markets in oil-exporting countries are much stronger and more persistent than in oil-importing countries. Finally, positive aggregate and precautionary demand shocks are shown to result in a higher degree of co-movement among the stock markets in oil-exporting countries, but not among those in oil-importing countries.     

Oil shocks,policy uncertainty and stock returns in China

This paper examines the interdependence of China's policy uncertainty, the global oil market and stock market returns in China. A structural VAR model is estimated that shows that a positive shock to economic policy uncertainty in China has a delayed negative effect on global oil production, real oil prices and real stock market returns. Shocks to oil market‐specific demand significantly raise China's economic policy uncertainty and reduce the real stock market returns. As measured by a spillover index, the interdependence between these variables has been rising since 2003 as China's influence in the oil market has increased. An equivalent spillover index calculated for the US is smaller and has been largely flat over time.     

Which way does water flow? An econometric analysis of the global price integration of water stocks

Around US$600 billion of investment is desperately needed to address forecasted huge shortages in water supply globally. A number of worldwide investors – so-called water funds – have started to take up this challenge. For these global water investors, knowledge about the extent of integration between the water sectors of financial markets is highly important. According to international portfolio diversification theory, the less (more) integrated markets are, the more (less) benefits there are from international diversification. In this study, we investigate the extent and manner of interdependence among the US, European and Asian water sector of the equity markets based on Vector Autoregression (VAR), Granger causality and impulse response analyses. We find that world water stock market prices are indeed significantly interdependent although this interdependence varies across time periods. Each market quickly responds to shocks from each other and completes its response within 3 days. Hence, for water investors, international diversification that is undertaken just within the water sector will not be beneficial. The result also implies that there is the risk of crossmarket contagion – that is, price volatility spill over across water sectors of different financial markets, and therefore, water authorities in one market should take cognisance of events in other markets.     

Did monetary or technology shocks move euro area stock prices?

Using a Bayesian vector autoregressive (VAR) model, I investigate the impact of monetary and technology shocks on the euro area stock market. I find an important role for technology surprise shocks, but not monetary shocks, in explaining variations in real stock prices. Specifically, the pronounced boom?Cbust cycle of 1995?C2003 is largely due to technology surprise shocks. The identification method allows me to study the effects of technology news shocks. The responses are consistent with the idea that news on technology improvements has an immediate impact on stock prices. These findings are robust to several modelling choices, including the productivity measure, the specification of the VAR model, and the identifying restrictions.     

上海股市流动性和收益率的动态关系研究

研究流动性与收益率之间的关系是证券市场微观结构理论研究的一个重点。文章采用VAR（向量自回归）方法来考察上海股票市场上流动性与收益率之间的关系。先通过VAR的脉冲响应函数来分析两者之间的影响关系，接着从条件分布的角度探索彼此之间的因果联系。综合考虑实证结果，深入了解上海股市流动性与收益率的变动传递过程。     

OIL PRICE SHOCKS AND STOCK MARKET BOOMS IN AN OIL EXPORTING COUNTRY

This paper analyses the effects of oil price shocks on stock returns in Norway, an oil-exporting country, highlighting the transmission channels of oil prices for macroeconomic behaviour. To capture the interaction between the different variables, stock returns are incorporated into a structural VAR model. I find that following a 10% increase in oil prices, stock returns increase by 2.5%, after which the effect gradually dies out. The results are robust to different (linear and non-linear) transformations of oil prices. The effects on the other variables are more modest. However, all variables indicate that the Norwegian economy responds to higher oil prices by increasing aggregate wealth and demand. The results also emphasize the role of other shocks; monetary policy shocks in particular, as important driving forces behind stock price variability in the short term.     

Oil shocks and stock volatility: new evidence via a Bayesian,graph-based VAR approach

ABSTRACT

This article examines the temporal dependence between three oil shocks and realized volatility in the stock markets of G20 countries between 1994 and 2019. By applying a novel, graphical, Bayesian VAR (BGVAR) model, we calculate unidirectional linkages of oil and stock volatility with a full and segmented sample. The results suggest an overall causality from stock volatility to oil shocks. For certain short, specific periods, the causal direction reverses. Depending on the country and the source of an oil shock, the magnitude and type of the effect can vary considerably. Specific oil-market shocks occur most often in our full sample. In a time-varying structure, oil supply shocks’ impact on stock volatility is more prominent, and net oil-importing countries’ responses to these shocks are greater than for oil-exporting countries. In addition, we find that relationship dynamics can capture market information, such as global economic growth during the 2008–2009 financial crisis.     

Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach

Crude oil price behaviour has fluctuated wildly since 1973 which has a major impact on key macroeconomic variables. Although the relationship between stock market returns and oil price changes has been scrutinized excessively in the literature, the possibility of predicting future stock market returns using oil prices has attracted less attention. This paper investigates the ability of oil prices to predict S&P 500 price index returns with the use of other macroeconomic and financial variables. Including all the potential variables in a forecasting model may result in an over-fitted model. So instead, dynamic model averaging (DMA) and dynamic model selection (DMS) are applied to utilize their ability of allowing the best forecasting model to change over time while parameters are also allowed to change. The empirical evidence shows that applying the DMA/DMS approach leads to significant improvements in forecasting performance in comparison to other forecasting methodologies and the performance of these models are better when oil prices are included within predictors.     

Impacts of China's crash on Asia-Pacific financial integration: Volatility interdependence,information transmission and market co-movement

This paper examines the price and volatility dynamics between China and major stock markets in the Asia-Pacific, investigating the effects of the Chinese stock market crash (2015–2016) for the first time. Employing the Bayesian VAR and BEKK GARCH, we observe that price and volatility spillover behaviours are different during the stable and stress periods. Particularly, price spillovers from China to other regional markets are more significant during a bullish period, showing that ‘good news’ emanating from China has strong impacts on its neighbours during better market condition. In the turbulent period, we observe strong shock spillover effects and enhanced volatility spillovers from China to most Asia-Pacific stock markets. This is because China, as an important trading partner and strategic financial centre shows to exert significant influence on the Asia-Pacific region through various economic channels. We also find that the Asia-Pacific stock markets spill over their shocks to China during the crisis, indicating that China is becoming more integrated with the regional financial markets.     

Does crude oil move stock markets in Europe? A sector investigation

The aim of this article is to investigate the responses of European sector stock markets to oil price changes. We use linear and asymmetric models and study the association of oil and stock prices. Our findings suggest that the strength of this association varies greatly across sectors. Moreover, for some sectors we find strong evidence of asymmetry in the reaction of stock returns to changes in the price of oil.     

Price connectedness between green bond and financial markets

We study price connectedness between the green bond and financial markets using a structural vector autoregressive (VAR) model that captures direct and indirect transmission of financial shocks across markets. Using heteroskedasticity to identify the structural VAR model parameters, our empirical findings reveal that the green bond market is closely linked to the fixed-income and currency markets, receiving sizeable price spillovers from those markets and transmitting negligible reverse effects. We also show that, in contrast, the green bond market is weakly tied to the stock, energy and high-yield corporate bond markets. These findings have implications in terms of portfolio and risk management decisions for environmentally aware investors holding positions in green bonds.     

Understanding the recent performance of China's stock market

Employing time-series extrapolation and an out-of-sample forecast based on a bivariate VAR (vector auto-regression), we argue that the current boom of China's stock market represents a recovery that corrects the previous divergence of the stock market from the aggregate economic performance. Nevertheless, we caution that the speed of the recent rise in stock prices is alarming. If the current speed continues, then the stock market will soon become overheated in the sense that the level of stock prices will exceed the level justified by economic fundamentals.     

Forecasting house prices for the four census regions and the aggregate US economy in a data-rich environment

This article considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated by dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:01–2009:03, based on an in-sample of 1968:02–1994:12, relative to a random walk model, a small-scale VAR model comprising just the five real house price growth rates and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the five real house price growth rates. In addition to the forecast comparison exercise across small-, medium- and large-scale models, we also look at the ability of the ‘optimal’ model (i.e. the model that produces the minimum average mean squared forecast error) for a specific region in predicting ex ante real house prices (in levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or Bayesian) perform the best for the North East, Mid-West, West census regions and the aggregate US economy and equally well to a small-scale VAR for the South region. The ‘optimal’ factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.     

Financialization and commodity prices – an empirical analysis for coffee,cotton, wheat and oil

Commodity prices have crucial implications for developing countries. The question whether the financialization of commodity derivative markets has contributed to high and volatile commodity prices has been controversially debated. Building on limitations in the empirical literature, we estimate a multivariate Vector Autoregressive (VAR) model to assess the effect of different groups of financial investors (index investors and money managers) as well as fundamental and macroeconomic variables on the prices of coffee, cotton, wheat and oil. We find that, in contrast to index investors, money managers’ net long positions have a large statistically significant effect on commodity prices. This calls for policy interventions as commodity derivative markets may cease to perform their fundamental developmental roles.     

The effect of oil prices on stock prices: fresh evidence from asymmetric causality tests

This article investigates the causal impact of oil prices on stock prices in each G7 market as well as in the world market. An asymmetric causality test developed by Hatemi-J is used for this purpose. Since the underlying data appears to be non-normal with time-varying volatility, we use bootstrap simulations with leverage adjustments in order to produce more reliable critical values than the asymptotic ones. Based on symmetric causality tests, we find no causal effect of oil prices on the stock prices of the world market or any of the G7 countries. However, when we apply an asymmetric causality test, we find that increasing oil prices cause stock prices to rise in the world, the U.S. and Japan while decreasing oil prices cause stock prices to fall in Germany. This may imply that the world, the U.S. and Japanese stock markets consider increases in oil prices as an indicator of good news as this may mean that there is an increase in oil demand due to an expected growth in the economy while the German stock market treats decreasing oil prices as a signal of an expected contraction in the economy.     

Dynamic causality between the U.S. stock market,the Chinese stock market and the global gold market: implications for individual investors’ diversification strategies

This paper proposes a generalization of the prior VAR and EGARCH model to explore the linkage between returns and volatility transmissions in the U.S. stock market, the Chinese stock market, and the global gold market from 10 July 1996 to 20 July 2018. We found that past returns of the U.S. stock market can predict the current returns of the other two markets, and that significant reciprocal volatility transmission existed within and across all three markets. We further implemented average out-of-sample (OOS) forecasting to show that a risk-adjusted portfolio, such as mean-variance with sample estimator, does not outperform an equal-weighted portfolio. This provides insights for individual investors and helps to explain the ongoing disagreement in the portfolio literature concerning the effectiveness of risk-adjusted portfolios and equal-weighted portfolios when the number of assets is small.     

The Relationship Between Economic Factors and Equity Markets in Central Europe

This paper investigates the possibility that newly-emerging equity markets in Central Europe exhibit semi-strong form efficiency such that no relationship exists between lagged values of changes in economic variables and changes in equity prices. We find that while there are connections between the real economy and equity market returns in Poland and Hungary, these links occur with lags, suggesting the possibility of profitable trading strategies based on public information and rejecting semi-strong efficiency. For the Czech Republic the situation is more complex. In recent periods, little connection exists between lagged economic variables and equity market returns. Although this finding might be viewed as consistent with semi-strong efficiency, in fact there is also little connection between current economic values and stock prices in the Czech Republic. Thus, instead of processing information efficiently, the Czech market appears to be entirely divorced from the real world. It is suggested that the difference in the current status of these markets may be due to the different methods by which they were created.