News sentiment in the cryptocurrency market: An empirical comparison with Forex

We use high frequency intra-day data to investigate the influence of unscheduled currency and Bitcoin news on the returns, volume and volatility of the cryptocurrency Bitcoin and traditional currencies over the period from January 2012 to November 2018. Results show that Bitcoin behaves differently to traditional currencies. Traditional currencies typically experience a decrease in returns after negative news arrivals and an increase in returns following positive news whereas Bitcoin reacts positively to both positive and negative news. This suggests investor enthusiasm for Bitcoin irrespective of the sentiment of the news. This phenomenon is exacerbated during bubble periods. Conversely, cryptocurrency cyber-attack news and fraud news dampen this effect, decreasing Bitcoin returns and volatility. Our results contribute to the discussion on the nature of Bitcoin as a currency or an asset. They further inform practitioners about the characteristics of cryptocurrencies as a financial asset and inform regulators about the influence of news on Bitcoin volatility, particularly during bubble periods.     

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.     

The impact of salient political and economic news on the trading activity

This paper investigates the impact of salient political and economic news on the intraday trading activity, namely, the stock return volatility, the stock price volatility, the number of shares traded, and the trading frequency. Using transactions data on 33 constituent stocks of the Hang Seng Index in the Stock Exchange of Hong Kong (SEHK), we find that political news has a distinct impact on market activity when compared with economic news. We argue that the observed phenomenon is related to the precision of signals associated with these two types of news and investors' perceptual biases.     

Macro news and stock returns in the Euro area: A VAR-GARCH-in-mean analysis

This paper analyses the effects of newspaper coverage of macro news on stock returns in eight countries belonging to the euro area (Belgium, France, Germany, Greece, Ireland, Italy, Portugal and Spain) using daily data for the period 1994–2013. The econometric analysis is based on the estimation of a VAR-GARCH-in-mean model. The results can be summarised as follows. Positive (negative) news have significant positive (negative) effects on stock returns in all cases. Their volatility has a significant impact on both stock returns and volatility; specifically, an increase in news volatility is always associated with a decrease in stock returns. Markets are particularly responsive to negative news, and the reaction is bigger in the PIIGS countries, and during the recent crisis period.     

Public information releases, private information arrival and volatility in the foreign exchange market

This paper estimates the impact of market activity and news on the volatility of returns in the exchange market for Japanese Yen and US dollars. We examine the effects of news on volatility before, during and after news arrival, using three categories of news. Market activity is proxied by quote arrival, separated into a predictable seasonal component and an unexpected component. Results indicate that both components of market activity, as well as news releases, affect volatility levels. We conclude that both private information and news effects are important determinants of exchange rate volatility. Our finding that unexpected quote arrival positively impacts foreign exchange rate volatility is consistent with the interpretation that unexpected quote arrival serves as a measure of informed trading. Corroborating this interpretation is regression analysis, which indicates that spreads increase in the surprise component of the quote arrival rate, but not in the expected component. The estimated impact of a unit increase in unexpected quote arrival and the range of values observed for this variable imply an important volatility conditioning role for informed trading.     

Forecasting Financial Market Volatility Using a Dynamic Topic Model

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological evolution of the topic in the set of articles using a dynamic topic model. After calculating a topic score, we develop time series models that incorporate the score to estimate and forecast realized volatility. The results of our empirical analysis suggest that the proposed models can contribute to improving forecasting accuracy.     

Public information arrival and volatility persistence in financial markets

This paper explores the relationship between daily market volatility and the arrival of public information in four different financial markets. Public information is measured as the daily number of economic news headlines, divided in six categories of news. Statistical analysis of the news data suggests the presence of particular seasonality effects, as well as a strong degree of autocorrelation. Over the period 1994–1998, significant effects of specific news categories on the volatility of US stocks, treasury bills, bonds and dollar were detected. However, the effects – in size and duration – vary by news category and by financial market. It is demonstrated that most of the volatility persistence, as observed by GARCH models, tends to disappear when news is included in the conditional variance equation.     

Volatility changes in European currency exchange rates due to EMS announcements

This study examines the effects of announcements concerning European Monetary Union on the exchange rate volatilities of several European currencies. It is expected that when good news is portrayed in regard to a single currency this will be considered bad news, thus eliciting a negative reaction, for the German mark. Conversely, good news for a single currency should also be good news for weaker currencies, such as the Portuguese escudo, the Italian lira, the Greek drachma, and the Spanish peseta. In terms of volatility, a reaction to good news should be a reduction in volatility, as bad news should cause an increase in volatility. In total there are 22 announcements examined from January 1986 through September 1997. The German mark is observed to experience greater increases in volatility than decreases, as does the Italian lira. Portugal and Greece appear to react more strongly to positive news in that the decreases in volatility are on average greater than the increases.     

Stochastic correlation across international stock markets

This paper examines the correlation across a number of international stock market indices. As correlation is not observable, we assume it to be a latent variable whose dynamics must be estimated using data on observables. To do so, we use filtering methods to extract stochastic correlation from returns data. We find evidence that the estimated correlation structure is dynamically changing over time. We also investigate the link between stochastic correlation and volatility. In general, stochastic correlation tends to increase in response to higher volatility but the effect is by no means consistent. These results have important implications for portfolio theory as well as risk management.     

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.     

Intraday and interday volatility in the Japanese stock market

This paper characterizes the volatility in the Japanese stock market based on a 4-year sample of 5-min Nikkei 225 returns from 1994 through 1997. The intradaily volatility exhibits a doubly U-shaped pattern associated with the opening and closing of the separate morning and afternoon trading sessions on the Tokyo Stock Exchange. This feature is consistent with market microstructure theories that emphasize the role of private and asymmetric information in the price formation process. Meanwhile, readily identifiable Japanese macroeconomic news announcements explain little of the day-to-day variation in the volatility, confirming previous findings for US equity markets. Furthermore, by appropriately filtering out the strong intradaily periodic pattern, the high-frequency returns reveal the existence of important long-memory interdaily volatility dependencies. This supports recent results stressing the importance of exploiting high-frequency intraday asset prices in the study of long-run volatility properties of asset returns.     

Macro news and bond yield spreads in the euro area

This paper analyses the effects of newspaper coverage of macro news on the spread between the yield on the 10-year German Bund and on sovereign bonds in eight countries belonging to the euro area (Belgium, France, Greece, Ireland, Italy, the Netherlands, Portugal and Spain) using daily data for the period 1999–2014. The econometric analysis is based on the estimation of a VAR-GARCH model. The results can be summarized as follows. Negative news have significant positive effects on yield spreads in all GIIPS (Greece, Ireland, Italy, Portugal and Spain) countries but Italy before September 2008; markets respond more to negative news, and their reaction has increased during the recent financial crisis. News volatility has a significant impact on yield spread volatility, the effects being more pronounced in the case of negative news and bigger in the most recent crisis period, especially in the GIIPS countries. Further, the conditional correlations between yield spreads and negative news increase in absolute value during the financial crisis (especially in the GIIPS countries), indicating a higher sensitivity of the former to the latter.     

Macroeconomic fundamentals, price discovery, and volatility dynamics in emerging bond markets

This study characterizes volatility dynamics in external emerging bond markets and examines how prices and volatility respond to macroeconomic news. As in mature bond markets, surprises about macroeconomic conditions in emerging markets are found to affect both conditional returns and volatility of external bonds, with the effects on volatility being more pronounced and longer lasting than those on prices. Yet the process of information absorption tends to be more drawn-out than in mature bond markets. Global and regional macroeconomic news is at least as important as local news for both price and volatility dynamics.     

Intraday Seasonalities and Macroeconomic News Announcements

Using a data set consisting of more than five years of 5‐minute intraday stock index returns for major European stock indices and US macroeconomic surprises, conditional means and volatility behaviour in European markets were investigated. The findings suggest that the opening of the US stock market significantly raises the level of volatility in Europe, all markets responding in an identical fashion. Furthermore, US macroeconomic surprises exert an immediate and major impact on both the European stock markets’ intraday returns and volatilities. Thus, high frequency data appear to be critical for the identification of news impacting the markets.     

Central bank communication and exchange rate volatility: a GARCH analysis

We examine the effects of the Czech National Bank communication, macroeconomic news and interest rate differential on exchange rate volatility using generalized autoregressive conditional heteroscedasticity model. Our results suggest that central bank communication has a calming effect on exchange rate volatility. The timing of central bank communication seems to matter, too, as financial markets respond more to the communication before the policy meetings than after them. Next, macroeconomic news releases are found to reduce exchange rate volatility, while interest rate differential seems to increase it.     

The impact of news on the volatility of ESG firms

This study provides one of the first empirical investigations of asymmetric volatility for environmental, social, and governance (ESG) investing. Using the Morgan Stanley Capital International (MSCI) indices as proxies for ESG test assets, this study investigates volatility risk for the highest ESG-rated firms through an empirical analysis in assessing how good news and bad news impact the risk of ESG firms. The analysis provides empirical evidence in support of the hypothesis that the impact of news on the volatility of ESG firms is larger for bad news, compared to good news. Employing an EGARCH framework, the analysis also finds that, in response to bad news, the observed volatility increases for small size ESG firms is lower compared to large and mid-cap ESG firms. The findings provide evidence of a slow response by small size firms to news in an ESG context. In modeling the conditional volatility of the ESG test assets, the analysis also provides evidence of higher persistence in the conditional volatility dynamics for small size ESG firms.     

Hunting the quicksilver: Using textual news and causality analysis to predict market volatility

This paper proposes that the dynamics of bond volatility may be understood by studying textual news sentiments. In this new approach, a modified framework is used to understand the atypical characteristics of bond market news. The paper proceeds in two steps. First, a word list of sentiment terms is generated using three sentiment word lists to determine negative and positive news sentiment scores. Second, four measures of volatility are estimated and combined with a nonlinear technique adapted from information theory to understand the correlation and direction of causality between sentiment scores and measures of volatility. This paper shows that sentiments extracted from textual news published in the newspapers can explain bond returns volatility or the quicksilver. The empirical results support that news sentiment is highly correlated with the measures of volatility and that information flows unidirectionally from news to volatility. This study, perhaps the earliest work in text mining to examine the run of causality between news signals and bond return volatility, adapts a nonlinear technique from information theory to describe the nonlinear behavior of Indian debt markets and understand the volatility dynamics of the benchmark bond.     

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 return volatility of cryptocurrencies during the COVID-19 pandemic: Assessing the news effect

In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.     

Realized volatility spillovers between US spot and futures during ECB news: Evidence from the European sovereign debt crisis

We study the simultaneity impact of the European Central Bank news on the daily realized volatility transmission mechanism (spillovers) among various US spot and futures markets. To this end, we apply a bias-corrected vector autoregressive model via Wild bootstrap simulation. We use minute-by-minute intraday data to construct daily realized volatility. We consider 429 news form the ECB as important events employing two major classifications, namely, a country classification with the highest total number of days related ECB news and a type of ECB news classification. We find that investors in futures markets react more vigorously and mainly for the ECB news that is associated with the group of EMU member states applied structural reforms. Yet, more importantly, we show that the US stock markets response heterogeneously to the ECB news, as we find key disagreements in the reactions both across the US markets and the types of ECB news studied. Such evidence is consistent with the explanation of the differential interpretation of information among market participants. From a practical point of view, we suggest that investors in the US spot market can effectively use two or more futures contracts to minimize their exposure to volatility risk associated with that news.