Terrorism and capital markets: The effects of the Madrid and London bomb attacks

Using event study methodology and GARCH family models, the paper investigates the effects of two terrorist incidents – the bomb attacks of 11th March 2004 in Madrid and 7th July 2005 in London – on equity sectors. Significant negative abnormal returns are widespread across the majority of sectors in the Spanish markets but not so in the case of London. Furthermore, the market rebound is much quicker in London compared to the Spanish markets where the attackers were not suicide bombers. Nevertheless, the overall findings point to only a transitory impact on return and volatility that does not last for a long period.     

中国封闭式基金价格报酬过度波动的经验分析

本文研究了中国封闭式基金价格报酬与净资产报酬的数据特征及其影响关系 ,主要的结果是 :( 1 )中国封闭式基金的价格报酬相对于基金的净资产报酬一方面存在过度波动 ,另一方面又存在反映不足 ;( 2 )通过检验表明 ,投资者情绪风险对价格报酬过度波动具有显著的影响 ,而Fama的三因素风险因子对价格报酬的过度波动几乎没有解释力 ;( 3 )封闭基金价格报酬的过度波动表明 :由于投资者行为使基金股票价格相对于基金净值存在额外的系统风险 ,封闭式基金折价正是对这种系统风险的一种补偿。     

Is market liquidity less resilient after the financial crisis? Evidence for US Treasuries

Understanding market liquidity resilience, i.e. the capacity of liquidity to absorb shocks, of United States Treasuries is crucial from a financial stability standpoint. The conventional resilience measure has limitations due to the use of the liquidity level. We propose a new complementary approach to analyze resilience based on liquidity volatility. For this purpose, we focus on the link between returns volatility and liquidity volatility, which is a relatively unexplored field. We fit a bivariate conditional correlation (CC-) GARCH model for the 10-year bond returns and five liquidity indicators from January 2003 to June 2016 to analyze persistence and spillovers between these variables in a parsimonious way. We find that after the crisis, spillovers between liquidity volatility and returns volatility are higher, feedback loops are more likely and volatility persistence is lower, which is consistent with a lower resilience. Our results help to explain recent episodes of high volatility in this market.     

Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility

We show that historical volatility from high frequency returns outperforms implied volatility when standardized returns by historical volatility tends to be normally distributed. For the FTSE 100 futures, we find that historical volatility using high frequency returns outperforms implied volatility in forecasting future volatility. However, we find that implied volatility outperforms historical volatility in forecasting future volatility for the S&P 500 futures. The results also indicate that historical volatility using high frequency returns could be an unbiased forecast for the FTSE 100 futures.     

Modelling conditional moments and correlation with the continuous hidden-threshold-skew-normal distribution

A key issue in modelling conditional densities of returns of financial assets is the time-variation of conditional volatility. The classic econometric approach models volatility of returns with the generalized autoregressive conditional heteroscedasticity (GARCH) models where the conditional mean and the conditional volatility depend only on historical prices. We propose a new family of distributions in which the conditional distribution depends on a latent continuous factor with a continuum of states. The distribution has an interpretation in terms of a mixture distribution with time-varying mixing probabilities. The distribution parameters have economic interpretations in terms of conditional volatilities and correlations of the returns with the hidden continuous state. We show empirically that this distribution outperforms its main competitor, the mixed normal conditional distribution, in terms of capturing the stylized facts known for stock returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence.     

Official central bank interventions and exchange rate volatility: Evidence from a regime-switching analysis

In this paper, we investigate the effect of central bank interventions on the weekly returns and volatility of the DEM/USD and YEN/USD exchange rate returns. In contrast with previous analyses, we allow for regime-dependent specifications and investigate whether official interventions can explain the observed volatility regime switches. It is found that, depending on the prevailing volatility level, coordinated central bank interventions can lead to either a stabilizing or a destabilizing effect. Our results lead us to challenge the usual view that such interventions always imply increases in volatility.     

From Returns to Tweets and Back: An Investigation of the Stocks in the Dow Jones Industrial Average

A sizeable percentage of investors are using social media to obtain information about companies (Cogent Research [2008]). As a consequence, social media content about firms may have an impact on stock prices (Hachman [2011]). Various studies utilize social media content to forecast stock market-related factors such as returns, volatility, or trading volume. The objective of this article is to investigate whether a bidirectional intraday relationship between stock returns and volatility and tweets exists. The study analyzed 150,180 minute-by-minute stock price and tweet data for the 30 stocks in the Dow Jones Industrial Average over a random 13-day interval from June 2 to June 18, 2014 using a BEKK-MVGARCH methodology. Findings indicate that 87% of stock returns are influenced by lagged innovations of the tweets data, but there is little evidence to support that the direction is reciprocal, with only 7% of tweets being influenced by lagged innovations of the stock returns. Results further show that the lagged innovations from 40 percent of stock returns affect the current conditional volatility of the tweets, while 73 percent of tweets affect the current conditional volatility of stock returns. Moreover, there is strong evidence to suggest that the volatility originating from the returns to the tweets persists for 33 percent of stocks; the volatility originating from the tweets to the returns persists for 73 percent of stocks. Last, 53 percent of stocks exhibit both immediate and persistent impacts from returns to tweets, while 90 percent of stocks exhibit both immediate and persistent impacts from tweets to returns. These results may help traders achieve superior returns by buying and selling individual stocks or options. Also, asset and mutual fund managers may benefit by developing a social media strategy.     

Australian Stock Market Volatility: 1875–1987*

This paper investigates the volatility of monthly Australian stock returns over the period 1875–1987. There has been extensive work on this question in the United States, but little with data outside that country. Our analysis centres upon whether the 'stylized facts' regarding returns in the US also hold true for Australia. We find that there are both similarities and differences. There is little evidence for asymmetry in Australian returns but strong persistence of shocks into volatility. What is particularly interesting in the Australian series is the large volatility of the last two decades, an experience not matched in the US data     

Politics, stock markets, and model uncertainty

The available evidence on the effects of political variables on both returns and volatility of aggregate stock indices is scant and mixed. Applying Bayesian Model Averaging to a panel dataset of 17 parliamentary democracies spanning the post-war period until 1995, we test the robustness of political variables in explaining stock returns and stock return volatility. While we find that the influence of political variables on excess returns is weak, there is evidence of some political variables explaining return volatility.     

股市收益率与波动性长期记忆效应的实证研究

股票市场长期记忆效应问题是近来金融实证研究的一个热点.多数的研究集中在收益率长期相关性的考察上,较少有对波动率序列的研究.然而,波动率的长期记忆性不仅会导致金融市场上的波动持久性特征,而且将对波动率的预测与衍生证券定价产生重要的影响.基于此,本文通过修正的R/S分析与ARFIMA模型对我国股市收益率及其波动性的长期相关性进行了实证研究.结果表明:中国股市具有显著的非线性特征,虽然收益率序列的自相关性较弱,但波动性序列却表现出显著的长期记忆效应.这一结论将为研究股票价格行为特征与金融经济学理论提供新的方向.     

Oil Price Shocks and Volatility in Australian Stock Returns

This paper examines the effect of oil shocks on return and volatility in the sectors of Australian stock market and finds significant effects for most sectors. For the overall market index, an increase in oil price return significantly reduces return, and an increase in oil price return volatility significantly reduces volatility. An advantage of looking at sector returns rather than a general index of stock returns is that sectors may well differ markedly in how they respond to oil price shocks. The energy and material sectors (as expected) and the financial sector (surprisingly) are out of step (in different ways) with results for the other sectors and for the overall index. A rise in oil price increases returns in the energy and material sectors and an increase in oil price return volatility increases stock return volatility in the financial sector. Explanation for the negative (positive) association between oil return (oil return volatility) and returns (volatility of returns) in the financial sector must be based on the association via lending to and/or holdings of corporate bonds issued by firms with significant exposure to oil price fluctuations and their speculative positions in oil‐related instruments.     

Consistent High-precision Volatility from High-frequency Data

Estimates of daily volatility are investigated. Realized volatility can be computed from returns observed over time intervals of different sizes. For simple statistical reasons, volatility estimators based on high-frequency returns have been proposed, but such estimators are found to be strongly biased as compared to volatilities of daily returns. This bias originates from microstructure effects in the price formation. For foreign exchange, the relevant microstructure effect is the incoherent price formation, which leads to a strong negative first-order autocorrelation ρ(1)≃40 per cent for tick-by-tick returns and to the volatility bias. On the basis of a simple theoretical model for foreign exchange data, the incoherent term can be filtered away from the tick-by-tick price series. With filtered prices, the daily volatility can be estimated using the information contained in high-frequency data, providing a high-precision measure of volatility at any time interval.
(J.E.L.: C13, C22, C81).     

Modeling the volatility of futures return in rubber and oil—A Copula-based GARCH model approach

This paper attempts to make use of a Copula-based GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) Model to find out the relationships between the volatility of rubber futures returns in the Agricultural Futures Exchange of Thailand (AFET) and other four main markets, namely, the volatility of rubber futures returns in the Singapore Commodity Exchange (SICOM), the volatility of rubber futures returns, crude oil returns, and gas oil returns in the Tokyo Commodity Exchange (TOCOM). The results illustrate that the Student-t dependence only shows better explanatory power than the Gaussian dependence structure and the persistence pertaining to the dependence structure between rubber futures returns in AFET and oil futures returns, namely, crude oil futures returns and gas oil futures returns in TOCOM. Whereas, the Gaussian dependence shows better explanatory ability between rubber futures returns in AFET and other rubber futures returns, namely, the volatility of rubber futures in SICOM and TOCOM. For the multivariate Copula model, all the parameters between AFET and other variables are significant. Based on these results, with the liberalization of agricultural trade and the withdrawal of government support to agricultural producers, there is in many countries a new need for price discovery and even physical trading mechanisms, a need that can often be met by commodity futures exchanges. Hence, this paper recommends that the government supports the hedge mutual funds that can be invested in every commodities futures exchange in the world. It can also put the funds together that will contribute farmers to invest in each commodities futures market.     

Does international oil volatility have directional predictability for stock returns? Evidence from BRICS countries based on cross-quantilogram analysis

While numerous studies have investigated the relationship between oil volatility and stock returns, it is surprising that little research has examined the quantile dependence and directional predictability from oil volatility to stock returns in BRICS (Brazil, Russia, India, China, and South Africa) countries. We address this issue by using the cross-quantilogram model proposed by Han et al. (2016). The empirical results show that, overall, oil volatility has a directional predictability for the stock returns in BRICS countries. When the oil volatility is in a low quantile (lower than its 0.1 quantiles), it is less likely to show either a large loss or a large gain in the stock market. In contrast, there is an increased likelihood of either large loss or a large gain in the stock market when the oil volatility is in a high quantile (higher than its 0.9 quantiles). The directional predictability from the oil volatility to stock returns depends on the net position of oil imports and exports of these BRICS countries in the oil market. The net oil exporters (Russia and Brazil) are less likely to have large gains and large losses in the stock market than are the net oil importers (India, China, and South Africa) when the oil volatility is in a low quantile. The net oil exporters are more likely to have large gains and large losses than are the net oil importers when the oil volatility is in a high quantile. The results are robust to change in the variable of oil volatility and the sample interval.     

The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method

A recent strand in the literature emphasizes the role of news-based economic policy uncertainty (EPU) and equity market uncertainty (EMU) as drivers of oil price movements. Against this backdrop, this paper uses a kth-order nonparametric quantile causality test, to analyse whether EPU and EMU predict stock returns and volatility. Based on daily data covering the period of 2 January 1986 to 8 December 2014, we find that, for oil returns, EPU and EMU have strong predictive power over the entire distribution barring regions around the median, but for volatility, the predictability virtually covers the entire distribution, with some exceptions in the tails. In other words, predictability based on measures of uncertainty is asymmetric over the distribution of oil returns and its volatility.     

R&D, patents and stock return volatility

Recent finance literature highlights the role of technological change in increasing firm specific (idiosyncratic) and aggregate stock return volatility, yet innovation data is not used in these analyses, leaving the direct relationship between innovation and stock return volatility untested. The paper investigates the relationship between volatility and innovation using firm level patent data. The analysis builds on the empirical work by Mazzucato (Rev Econ Dyn 5:318–345, 2002; J Evol Econ 13(5):491–512, 2003) where it is found that stock return volatility is highest during periods in the industry life-cycle when innovation is the most ‘radical’. In this paper we ask whether firms which invest more in innovation (more R&D and more patents) and/or which have more important innovations (patents with more citations) experience more volatility in their returns. Given that returns should in theory be higher, on average, for higher risk stocks, we also look at the effect of innovation on the level of returns. To take into account the competition between firms within industries, firm returns and volatility are measured relative to the industry average. We focus the analysis on firms in the pharmaceutical industry between 1974 and 1999. Results suggest that there is a positive and significant relationship between volatility, R&D intensity and the various patent related measures—especially when the innovation measures are filtered to distinguish the very innovative firms from the less innovate ones.     

On the robustness of alternative rankings methodologies: Australian and New Zealand economics departments, 1988 to 2002

Theoretical considerations appear to support the conjecture that stock returns are positively related to growth in the long run. However, the empirical literature does not give unanimous support to the theory. Based on a stochastic general equilibrium model it is argued that the long-run relationship between stock returns and per capita income growth is ambiguous and depends on output volatility. Using a century of data for 20 Organization for Economic Co-operation and Development (OECD) countries it is shown that the relationship between stock returns and growth is positive over the period 1916–1951, in which output volatility was persistent. Outside this period no relationship between stock returns and growth is found. These findings are consistent with the predictions of the theoretical model.     

What are returns outside trading hours capturing for volatility of individual stocks?

This study is the first to harness the negative returns and squared returns outside trading hours, trading volume and leverage effects in an augmented heterogeneous autoregressive model for forecasting volatility of individual stocks. Besides significant leverage effects and trading volume impact, we find that an increase in the negative returns is associated with a decline in volatility, but an increase in the squared returns is associated with a rise in volatility. This new finding suggests that the negative returns and squared returns outside trading hours are capturing additional leverage effects and additional volatilities, respectively. Moreover, the relations display differences amongst various firm categories which arise from firm heterogeneity.     

Modelling the Impact of Overnight Surprises on Intra-daily Volatility

In this paper we evaluate the impact that stock returns recorded between market closing and opening the next business day have on intra-daily volatility. A simple test shows that the estimated volatility clustering of the intra-daily returns may be affected by a market opening surprise bias. An extension of the standard GARCH model is suggested here to include the effect of this surprise and is applied on a sample of largely traded US stocks. The performance of two specifications in which this effect is included is evaluated in an out-of-sample forecasting exercise relative to their standard counterparts.     

Modeling Price Volatility Linkages between Corn and Wheat: A Multivariate GARCH Estimation

The present paper attempts an empirical investigation on price volatility linkages between two important agricultural commodities: corn and wheat, by using a multivariate GARCH-BEKK model. Evidence of bidirectional linkages were found between corn and wheat in terms of returns and volatility. Multivariate conditional Student’s-t distribution results show a unidirectional volatility transmission from corn to wheat. Diagnostic tests reveal that the Student’s-t distribution will better model the volatility of returns when compared to the Gaussian distribution. Overall, the results show that the conditional variances and covariances between agricultural commodity market returns exhibit significant changes over time.