Granger-causality in quantiles between financial markets: Using copula approach

This paper considers the Granger-causality in conditional quantile and examines the potential of improving conditional quantile forecasting by accounting for such a causal relationship between financial markets. We consider Granger-causality in distributions by testing whether the copula function of a pair of two financial markets is the independent copula. Among returns on stock markets in the US, Japan and U.K., we find significant Granger-causality in distribution. For a pair of the financial markets where the dependent (conditional) copula is found, we invert the conditional copula to obtain the conditional quantiles. Dependence between returns of two financial markets is modeled using a parametric copula. Different copula functions are compared to test for Granger-causality in distribution and in quantiles. We find significant Granger-causality in the different quantiles of the conditional distributions between foreign stock markets and the US stock market. Granger-causality from foreign stock markets to the US stock market is more significant from UK than from Japan, while causality from the US stock market to UK and Japan stock markets is almost equally significant.     

Modelling the asymmetric linkages between spot gold prices and African stocks

We model the asymmetric linkages between returns of spot gold prices and African stock markets using wavelets and quantile regression techniques. The maximal overlap discrete wavelet transform technique was employed to decompose the returns into short-, medium-, and long-term series and the quantile regression was employed to explore the nexus by matching their conditional distributions along 0.05 quantile intervals. We find that the relationship between gold and African stocks is frequency-dependent and asymmetric in nature across the various timescales and quantiles. We find a mixture of negative and positive connections across the various quantiles in the short- and medium-terms. In the long-term, whereas the effect of gold is positive for Ghana, Mauritius, and Nigeria; it is negative for Egypt, Morocco, South Africa, and Tunisia. The results possess important implications for risk management as dependencies are not only studied over the entire conditional distribution at once but based on quantiles and further at different frequencies. Investors can make well-informed decisions to mitigate trade risks as they closely match the time heterogeneity in the markets.     

Extreme connectedness of agri-commodities with stock markets and its determinants

In this paper, we quantify the extreme connectedness between agricultural commodity prices with food and beverage stock market returns. We find that the connectedness of returns relies on the degree of the inverse shock, as suggested by the larger impact of the anticipated shock on the upper and lower tails than the estimated shock on the conditional mean. Additionally, the dynamics of the connectedness of returns monitored in the tail differ from the conditional mean. These two outcomes recommend that using conditional averages is limited and imprecise to analyze returns connected with extreme positive/negative events in agricultural commodities and food & beverage indices. Next, we find the determinants of the extent of the connectedness by employing firm level statistics. We find that some of the determinants driving the return spillovers at upper and lower quantiles are quite different from those driving the return spillovers at the middle quantile.     

Gaining a competitive edge through acquisitions: Evidence from the telecommunications industry

I study the announcement effects of all acquisitions in the recent telecom wave on both the acquirers and their industry competitors. I find evidence of negative rival returns (? 0.55%, t-stat = 2.47) by focusing on non-horizontal acquisitions where rivals are less susceptible to experience positive returns due to increased market power or expectation that some will become future targets themselves. I find that this effect is worse for closer rivals defined as having similar size and being in the same primary service area as the acquirer. Competitor returns are positively correlated with those of the acquirers suggesting that the negative impact experienced by competitors is driven by acquisitions in which the acquirer itself is earning negative abnormal returns. Results are broadly consistent with the Competitive Advantage Hypothesis that posits acquisitions are a means of corporate restructuring in a changing environment, awarding the acquirer a competitive edge and thereby making these acquisitions costly for their non-merging competitors.     

Investor sentiment and stock returns: Wenchuan Earthquake

This paper exploits a natural experiment (the Wenchuan Earthquake in China) to study the effects of investor sentiment on stock returns. We find that during the 12 months following the earthquake, stock returns are significantly lower for firms headquartered nearer the epicenter than for firms further away. Further analyses indicate that this pattern of stock returns does not exist before or long after the earthquake, and cannot be explained by actual economic losses or a change in systematic risk. Overall, our evidence is consistent with the interaction of local bias and investor sentiment affecting stock returns.     

Individual and institutional herding and the impact on stock returns: Evidence from Taiwan stock market

Using high frequency intraday data, this paper investigates the herding behavior of institutional and individual investors in the Taiwan stock market. The study finds evidence of herding by both investors but a stronger herding tendency among institutional than among individual investors. Institutional investors herd more on firms with small capitalizations and lower turnovers and they follow positive feedback strategies. The portfolios that institutional investors herd buy outperform those they sell by an average of 1.009% during the 20 days after intense trading episodes. By contrast, individual investors herd more on firms with small sizes and higher turnovers, and they crowd to buy (sell) stocks with negative (positive) past returns. The portfolios that individual investors herd buy underperform those they sell by an average of − 0.829% during the following 20 days. Moreover, these return differences of both investors are more pronounced under a market with higher pressure and among small stocks. These findings suggest that the herding of institutional investors speeds up the price-adjustment process and is more likely to be driven by correlated private information, while individual herding is most likely to be driven by behavior and emotions.     

Asymmetries of the intraday return-volatility relation

This study investigates the asymmetry of the intraday return-volatility relation at different return horizons ranging from 1, 5, 10, 15, up to 60 min and compares the empirical results with results for the daily return horizon. Using data on the S&P 500 (SPX) and the VIX from September 25, 2003 to December 30, 2011 and a Quantile-Regression approach, we observe strong negative return-volatility relation over all return horizons. However, this negative relation is asymmetric in three different aspects. First, the effects of positive and negative returns on volatility are different and more pronounced for negative returns. Second, for both positive and negative returns, the effect is conditional on the distribution of volatility changes. The absolute effect is up to five times larger in the extreme tails of the distribution. Third, at the intraday level, there is evidence of both autocorrelation in volatility changes and cross-autocorrelation with returns. This lead-lag relation with returns is also very asymmetric and more pronounced in the tails of the distribution. These effects are, however, not observed at the daily return horizon.     

Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors

This paper extracts an investor sentiment indicator for the 30 DJIA stocks based on the textual classification of 289,024 online tweets posted on the so-called StockTwits, and examines its contemporaneous and predictability effects on the dispersion of stock returns using the quantile regression technique. We find that both contemporaneous and predictability effects of sentiment are heterogeneous throughout the return distribution. Specifically, sentiment is positively contemporaneously associated with stock returns at higher quantiles. However, it is a strong negative predictor of future returns at lower quantiles. Overall, our findings are broadly consistent with most behavioural theories and show that sentiment mainly affects the valuation of assets in extreme market conditions.     

Stock returns,trading volume,and volatility: The case of African stock markets

The presence of the African Stock Markets (ASMs) in the global frontier markets indices confirms their global portfolio diversification role. This study investigates the asymmetric and intertemporal causality among the stock returns, trading volume, and volatility of eight ASMs. Results based on the linear model reveal that return generally Granger cause trading volume. However, evidence from the quantile regression shows that lagged trading volume has a negative causal effect on returns at low quantiles and positive causal effects at high quantiles. This evidence is consistent with volume-return equilibrium models, disposition and overconfidence models, and information asymmetry models. The positive causal effects of volatility on volume support the dispersion of beliefs model. In contrast, intertemporal evidence of contemporaneous and lagged causal relationships from trading volume to volatility supports the mixture of distribution hypothesis, sequential information acquisition hypothesis, and dynamic efficient market hypothesis. Volume-return and return-volume causality dynamics are quantile-specific and therefore driven by market conditions. However, the volume-volatility causality is dependent on volatility regimes. The linear model results confirm how model misspecification can distort and even reverse empirical evidence relative to nonlinear models.     

基于极端分位回归的股市收益与交易量相依性研究

依据中国行业股市收益和交易量时间序列数据,引入政策效应变量,运用分位数回归理论时间序列模型,考量股市收益与交易量相依性关系.结果显示,中国行业股市收益与交易量之间相关关系存在差异,且在高分位点呈现正相关,在低分位点呈现负相关.结果表明,中国股市投资者存在显著的羊群效应,政策效应对不同行业收益和交易量相依性的影响存在异质性.鉴此,投资者宜减少部分行业股票配置,政府应建立高效的风险管理机制,尽量减缓股市波动.     

Causality in quantiles and dynamic stock return–volume relations

This paper investigates the causal relations between stock return and volume based on quantile regressions. We first define Granger non-causality in all quantiles and propose testing non-causality by a sup-Wald test. Such a test is consistent against any deviation from non-causality in distribution, as opposed to the existing tests that check only non-causality in certain moment. This test is readily extended to test non-causality in different quantile ranges. In the empirical studies of three major stock market indices, we find that the causal effects of volume on return are usually heterogeneous across quantiles and those of return on volume are more stable. In particular, the quantile causal effects of volume on return exhibit a spectrum of (symmetric) V-shape relations so that the dispersion of return distribution increases with lagged volume. This is an alternative evidence that volume has a positive effect on return volatility. Moreover, the inclusion of the squares of lagged returns in the model may weaken the quantile causal effects of volume on return but does not affect the causality per se.     

Large returns,conditional correlation and portfolio diversification: a value-at-risk approach

This paper, using daily returns on 30 Dow Jones Industrial stocks for the period 1991-1999, investigates the possibility of portfolio diversification when there are negative large movements in the stock returns (i.e. when the market is bearish). We estimate the quantiles of stock return distributions using non-parametric and parametric methods that are widely being used in measuring value-at-risk (VaR). We find that the average conditional correlation of 30 stocks is much higher when the large movements are negative than that when the market is 'usual'. Further, we find that, contrary to the results of previous studies, there is no notable difference between the average conditional correlations when the large movements are positive and when the market is 'usual'. Moreover, it is evident from the results of the conditional CAPM that the portfolio's diversifiable and non-diversifiable risks, as measured by the error variance of the CAPM and beta respectively, are highly unstable when the market is bearish than that when it is 'usual' or bullish. The overall results suggest that the possibility of portfolio diversification would be eroded when the stock market is bearish. These findings have implications for portfolio diversification and risk management in particular and for finance in general. The ideas presented in this paper can be utilized for testing contagion in the international financial markets, a much-researched topic in international finance.     

The Relation Between Relative Order Imbalance and Intraday Futures Returns: An Application of the Quantile Regression Model to Taiwan

Adopting the quantile regression model, this paper describes the positive relation between relative order imbalance and intraday futures returns. The positive connection is relatively stronger for lower quantiles of intraday futures returns than for higher quantiles. However, the connection vanishes within 30 minutes. The results reflect the compensation of the uncertainty and the absence of liquidity for relatively lower returns in the Taiwan futures market. Furthermore, this paper finds evidence supporting an L-shaped pattern for intraday futures returns.     

The determinants of quantile autocorrelations: Evidence from the UK

We empirically analyse the cross-sectional determinants of stock return autocorrelations in the UK in different quantiles of conditional return distributions. Autocorrelations in low quantiles are predominantly positive, whereas those in the remaining quantiles are negative. Autocorrelations in different quantiles depend on different sets of firm and trading characteristics: when returns are normal or high, prices react quickly to information, are driven by positive feedback traders, instantaneous news arrivals, and overshoot, trades are predominantly motivated by hedging/liquidity needs, and measured autocorrelations can be biassed by the bid–ask bounce effect and nonsynchronous trading. However, when returns are unusually low, prices are driven by information arriving sequentially and react sluggishly to it, and are influenced by trading on private information and/or negative feedback traders.     

Market Impact on financial market integration: Cross-quantilogram analysis of the global impact of the euro

We contribute to the literature by providing a more comprehensive understanding of the impact the euro has had on financial market integration with economies of different characteristics outside and within the European market via inclusion of market conditions influence on the level of financial integration. Our paper employs the recently developed cross-quantilogram (Han et al., 2016) approach to examine quantile dependence between the conditional stock return distributions of Germany and the UK with that of three common currency groups within EMU (Finland, France, and Italy), two global leading markets (the US and Japan), and two of the most promising emerging markets (China and India). We find three key results. First, both the EU membership and the common currency union affect the degree of financial market integration. Nevertheless, disentangling the effects of EU membership from the common currency shows that the common currency group has an additional impact on financial integration, as the degree of dependence is stronger in the common currency group than in the sovereign currency group and other groups. Second, there is a heterogeneous dependence structure, which is strongly observed for the UK and German stock returns with that of developed (the US and Japan) and emerging markets (India and China). Third, cross-quantile correlations change over time, especially in low and high quantiles, indicating that they are prone to jumps and discontinuities in the dependence structure. As far as we are aware, this is the first study in this field employing a cross-quantilogram method to examine the impact of different market conditions on the correlations, making our study a pioneer in the field of stock market integration.     

Momentum is really short-term momentum

We demonstrate the estimation biases that arise when stock returns from 12 month prior and 2 month prior are included within intermediate and recent past momentum profits. These biases lead to an overestimation of intermediate past momentum but an underestimation of recent past momentum in the US market. There is no significant difference between the predictability of stock performance in the intermediate past and the recent past once we exclude these two months from the construction of momentum strategies in the US and each of the 26 major international markets.     

Estimation of tail‐related risk measures in the Indian stock market: An extreme value approach

The purpose of the study is to estimate tail-related risk measures using extreme value theory (EVT) in the Indian stock market. The study employs a two stage approach of conditional EVT originally proposed by McNeil and Frey (2000) to estimate dynamic Value at Risk (VaR) and expected shortfall (ES). The dynamic risk measures have been estimated for different percentiles for negative and positive returns. The estimates of risk measures computed under different quantile levels exhibit strong stability across a range of the selected thresholds, implying the accuracy and reliability of the estimated quantile based risk measures.     

Time Variation in the Covariance between Stock Returns and Consumption Growth

The conditional covariance between aggregate stock returns and aggregate consumption growth varies substantially over time. When stock market wealth is high relative to consumption, both the conditional covariance and correlation are high. This pattern is consistent with the “composition effect,” where agents' consumption growth is more closely tied to stock returns when stock wealth is a larger share of total wealth. This variation can be used to test asset‐pricing models in which the price of consumption risk varies. After accounting for variations in this price, the relation between expected excess stock returns and the conditional covariance is negative.     

Asymmetric Price and Volatility Adjustments in Emerging Asian Stock Markets

This paper tests the hypothesis that stock returns in emerging stock markets adjust asymmetrically to past information. The evidence suggests that both the conditional mean and the conditional variance respond asymmetrically to past information. In agreement with studies dealing with developed stock markets, the conditional variance is an asymmetrical function of past innovations, rising proportionately more during market declines. More importantly, the conditional mean is also an asymmetrical function of past returns. Specifically, positive past returns are more persistent than negative past returns of an equal magnitude. This behaviour is consistent with an asymmetric partial adjustment price model where news suggesting overpricing (negative returns) are incorporated faster into current prices than news suggesting underpricing (positive returns). Furthermore, the asymmetric adjustment of prices to past information could be partially responsible for the asymmetries in the conditional variance if the degree of adjustment and the level of volatility are positively related.     

Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

This paper applies the vector AR-DCC-FIAPARCH model to eight national stock market indices' daily returns from 1988 to 2010, taking into account the structural breaks of each time series linked to the Asian and the recent Global financial crisis. We find significant cross effects, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks, and the power of returns that best fits the volatility pattern. One of the main findings of the model analysis is the higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets. The fact that during the crisis the conditional correlations remain on a high level indicates a continuous herding behaviour during these periods of increased market volatility. Finally, during the recent Global financial crisis the correlations remain on a much higher level than during the Asian financial crisis.