Modeling the Conditional Covariance Between Stock and Bond Returns: A Multivariate GARCH Approach

To analyze the intertemporal interaction between the stock andbond market returns, we assume that the conditional covariancematrix follows a multivariate GARCH process. We allow for asymmetriceffects in conditional variances and covariances. Using dailydata, we find strong evidence of conditional heteroskedasticityin the covariance between stock and bond market returns. Theresults indicate that not only variances, but also covariancesrespond asymmetrically to return shocks. Bad news in the stockand bond market is typically followed by a higher conditionalcovariance than good news. Cross asymmetries, that is, asymmetriesfollowed from shocks of opposite signs, appear to be importantas well. Covariances between stock and bond returns tend tobe relatively low after bad news in the stock market and goodnews in the bond market. A financial application of our modelshows that optimal portfolio shares can be substantially affectedby asymmetries in covariances. Moreover, our results show sizablegains due to asymmetric volatility timing.     

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.     

STOCK MARKET REACTION TO GOOD AND BAD INFLATION NEWS

This article shows that differentiating between good and bad inflation news is important to understanding how inflation affects stock market returns. Summing positive and negative inflation shocks as in previous studies tends to wash out or mute the effects of inflation news on stock returns. More specifically, we find that, depending on the economic state, positive and negative inflation shocks can produce a variety of stock market reactions. We conclude that the effect of inflation on stock returns is conditional on whether investors perceive inflation shocks as good or bad news in different economic states.     

Multifrequency news and stock returns

Equity prices are driven by shocks with persistence levels ranging from intraday horizons to several decades. To accommodate this diversity, we introduce a parsimonious equilibrium model with regime shifts of heterogeneous durations in fundamentals, and estimate specifications with up to 256 states on daily aggregate returns. The multifrequency equilibrium has higher likelihood than the Campbell and Hentschel [1992. No news is good news: an asymmetric model of changing volatility in stock returns. Journal of Financial Economics 31, 281–318] specification, while producing volatility feedback 10 to 40 times larger. Furthermore, Bayesian learning about volatility generates a novel trade-off between skewness and kurtosis as information quality varies, complementing the uncertainty channel [e.g., Veronesi, 1999. Stock market overreaction to bad news in good times: a rational expectations equilibrium model. Review of Financial Studies 12, 975–1007]. Economies with intermediate information best match daily returns.     

Asymmetry in return and volatility spillover between equity and bond markets in Australia

We document asymmetry in return and volatility spillover between equity and bond markets in Australia for daily returns during the period 1992–2006 using a bivariate GARCH modelling approach. Negative bond market returns spillover into lower stock market returns whereas good news originating in the equity market leads to lower bond returns. Bond market volatility spills over into the equity market but the reverse is not true. Transmission of bond volatility into equity volatility depends in a complex way upon the respective signs of the return shocks in each market.     

ASYMMETRIC COVARIANCE,VOLATILITY, AND THE EFFECT OF NEWS

We propose that covariance (rather than beta) asymmetry provides a superior framework for examining issues related to changing risk premiums. Accordingly, we investigate whether the conditional covariance between stock and market returns is asymmetric in response to good and bad news. Our model of conditional covariance accommodates both the sign and magnitude of return innovations, and we find significant covariance asymmetry that can explain, at least in part, the volatility feedback of stock returns. Our findings are consistent across firm size, firm leverage, and temporal and cross‐sectional aggregations.     

Stock market overreactions to bad news in good times: a rational expectations equilibrium model

This article presents a dynamic, rational expectations equilibriummodel of asset prices where the drift of fundamentals (dividends)shifts between two unobservable states at random times. I showthat in equilibrium, investors' willingness to hedge againstchanges in their own 'uncertainty' on the true state makes stockprices overreact to bad news in good times and underreact togood news in bad times. I then show that this model is betterable than conventional models with no regime shifts to explainfeatures of stock returns, including volatility clustering,'leverage effects,' excess volatility, and time-varying expectedreturns.     

SENSITIVITY OF INVESTOR REACTION TO MARKET DIRECTION AND VOLATILITY: DIVIDEND CHANGE ANNOUNCEMENTS

We examine whether investor reactions are sensitive to the recent direction or volatility of underlying market movements. We find that dividend change announcements elicit a greater change in stock price when the nature of the news (good or bad) goes against the grain of the recent market direction during volatile times. For example, announcements to lower dividends elicit a significantly greater decrease in stock price when market returns have been up and more volatile. Similarly, announcements to raise dividends tends to elicit a greater increase in stock price when market returns have been normal or down and more volatile, although this latter tendency lacks statistical significance. We suggest an explanation for these results that combines the implications of a dynamic rational expectations equilibrium model with behavioral considerations that link the responsiveness of investors to market direction and volatility.     

Price and Volatility Spillovers in the Case of Stock Markets Located in Different Time Zones

This paper investigates the interdependence of price volatility across the U.S. stock market and two emerging markets: Poland and Hungary. Using daily data for countries located in different time zones, we point out the problems caused by the presence of nonsynchronous trading effects. To address this problem we use open-to-close logarithmic returns of major stock market indexes. The asymmetric impact of good and bad news is described by a multivariate exponential general autoregressive conditional heteroskedastic model. We investigate the sample from May 2004 to December 2011. The evidence is that the U.S. prices spill over to other markets. Our results show no pronounced volatility spillovers among the three examined markets. Moreover, we observe the presence of negative asymmetry in the case of all markets.     

The distribution of earnings news over time and seasonalities in aggregate stock returns

Over the past 55 years returns on stock market indexes have on average been higher during the first half-month of calendar quarters 2 through 4 than at other times. Coincidentally, aggregate corporate earnings news arriving at the market during these half-month periods tends to be good, whereas earnings reports arriving later are more likely to convey bad news. In addition firms tend to publish bad-news earnings reports on Mondays, coincident with negative Monday effects in stock returns. The coincidence of earnings news arrival and market seasonalities leads to conjectures about informational reasons for observed seasonalities.     

“好”的不确定性、“坏”的不确定性与股票市场定价——基于中国股市高频数据分析

不确定性并不是都是“坏”的,“好”的不确定性也同样存在。本文采用Barndorff-Nielsen et al.(2010)提出的已实现半方差作为股票市场“好”的不确定性和“坏”的不确定性的代理指标,并在此基础上构建了相对符号变差(RSV),分析RSV对中国股市定价的影响。基于2007-2017年中国A股5分钟高频数据的实证研究发现:(1)与理论解释相一致,RSV与股票收益之间呈现负相关关系。无论是基于单变量分组、双变量分组还是公司层面的截面回归,这种影响在经济上和统计上都显著。(2)RSV是独立于已实现偏度的一个重要定价因子,且RSV对股票的定价能力强于已实现偏度的定价能力。(3)RSV对中国股市的影响是状态依存的,相对于经济景气程度高的状态,在经济景气程度低的状态下RSV定价影响更大。(4)基于RSV构建的投资组合的表现明显优于市场超额收益率组合、SMB组合和HML组合的表现。     

Asymmetric effects of federal funds target rate changes on S&P100 stock returns,volatilities and correlations

We study the effects of FOMC announcements of federal funds target rate decisions on individual stock returns, volatilities and correlations at the intraday level. For all three characteristics we find that the stock market responds differently to positive and negative target rate surprises. First, the average response to positive surprises (that is, bad news for stocks) is larger. Second, in case of bad news the mere occurrence of a surprise matters most, whereas for good news its magnitude is more important. These new insights are possible due to the use of high-frequency intraday data.     

News or Noise? Using Twitter to Identify and Understand Company‐specific News Flow

This study presents a methodology for identifying a broad range of real‐world news events based on microblogging messages. Applying computational linguistics to a unique dataset of more than 400,000 S&P 500 stock‐related Twitter messages, we distinguish between good and bad news and demonstrate that the returns prior to good news events are more pronounced than for bad news events. We show that the stock market impact of news events differs substantially across different categories.     

How does news sentiment affect the states of Japanese stock return volatility?

This paper examines the impact of public news sentiment on the volatility states of firm-level returns on the Japanese Stock market. We firstly adopt a novel Markov Regime Switching Long Memory GARCH (MRS-LMGARCH), which is employed to estimate the latent volatility states of intraday stock return. By using the RavenPack Dow Jones News Analytics database, we fit discrete choice models to investigate the impact of news sentiment on changes of volatility states of the constituent stocks in the TOPIX Core 30 Index. Our findings suggest that news occurrence and sentiment, especially those of macro-economic news, are a key factor that significantly drives the volatility state of Japanese stock returns. This provides essential information for traders of the Japanese stock market to optimize their trading strategies and risk management plans to combat volatility.     

中国股票市场波动性研究：模型选择及实证

以日收盘数据计算出的市场日收益率作为研究的基础数据,利用准极大似然估计方法QML估计三种ARCH类模型(GARCH、T-GARCH和E-GARCH)对中国股市波动性与稳定性的运行效果进行了实证研究的结果,得出EGARCH(1,1)模型是最优的拟合模型。运用最优模型实证发现中国股市不仅波动性很大,而且波动是不对称的。     

The time‐varying performance of UK analyst recommendation revisions: Do market conditions matter?

This study examines the time‐varying performance of investment strategies following analyst recommendation revisions in the UK stock market, with specific emphasis on the impact of changing market conditions. We find a negative relationship between the recommendation performance and market conditions as measured in terms of past market return and market volatility. In particular, the upgrade (downgrade) portfolio generates significantly positive (negative) net abnormal returns in bad market conditions (e.g., the dot‐com bubble burst in 2000 and the credit crisis in 2007), but not in other periods of time. Moreover, our non‐temporal threshold regression analysis shows that the reported negative relationship disappears when market conditions become better, i.e., when the past market return (market volatility) is higher (lower) than a certain level, indicating the importance of taking non‐linearity into account in the long sample period as examined in this study. Our time‐series bootstrap simulations further confirm that the superior recommendation performance in bad market conditions is not due to random chance; analysts have certain skills in making valuable up/downward revisions in bad markets.     

Option prices and stock market momentum: evidence from China

Option prices tend to be correlated to past stock market returns due to market imperfections. We unprecedentedly examine this issue on the SSE 50 ETF option in the Chinese derivatives market. To measure the price pressure in the options market, we construct an implied volatility spread based on pairs of the SSE 50 ETF option with identical expiration dates and strike prices. By regressing the implied volatility spread on past stock returns, we find that past stock returns exert a strong influence on the pricing of index options. Specifically, we find that SSE 50 ETF calls are significantly overvalued relative to SSE 50 ETF puts after stock price increases and the reverse is also true after the stock price decreases. Moreover, we validate the momentum effects in the underlying stock market to be responsible for the price pressure. These findings are both economically and statistically significant and have important implications.     

基于不同分布假设下波动模型估计效果比较——以上海股票市场为例

本文基于不同分布假设,即正态分布、Student-t分布以及EGB2分布,使用2005年1月4日至2011年6月30日上证综指日收益率数据对GARCH模型和GJR GARCH模型估计效果进行实证比较。实证结果显示：（1）基于非对称EGB2分布的GJR GARCH模型更适合中国证券市场;（2）中国股票市场存在波动不对称性,且好消息引发的波动大于坏消息引发的波动,这可能与中国股票市场特有的市场结构和交易制度有关;（3）波动的不对称特性可能部分来自于对分布偏度特性考虑的欠缺,验证了合理的分布假设在波动行为分析过程中的重要性。     

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.     

Bad beta good beta,state‐space news decomposition and the cross‐section of stock returns

This study employs an innovative market‐based approach, where return on equity (ROE) is employed as a proxy for cash‐flow news and a state‐space model is used for market news decomposition. We document that the bad beta good beta (BBGB) model of Campbell and Vuolteenaho (2004) explains about 30 per cent of the cross‐sectional variations in US stock returns. We also find that the BBGB model adequately explains the size effect leading to its superior performance in this area. Our method controls for the news decomposition method and market news proxies’ bias. We contribute to the literature by providing an alternative easy‐to‐implement and consistent market‐based method for news decomposition.