[1]AUDRINO F, SIGRIST F, BALLINARI D. The impact of sentiment and attention measures on stock market volatility[J/OL]. International Journal of Forecasting, 2020, 36(2): 334-357. DOI:10.1016/j.ijforecast.2019.05.010.
The impact of sentiment and attention measures on stock market volatility
情绪和注意力措施对股市波动的影响
Francesco Audrino, Fabio Sigrist, Daniele Ballinari
Abstract
We analyze the impact of sentiment and attention variables on the stock market volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. We apply a state-of-the-art sentiment classification technique in order to investigate the question of whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify the most relevant variables to be investors’ attention, as measured by the number of Google searches on financial keywords (e.g. “financial market” and “stock market”), and the daily volume of company-specific short messages posted on StockTwits. In addition, our study shows that attention and sentiment variables are able to improve volatility forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view.
我们通过使用结合了社交媒体、新闻文章、信息消费和搜索引擎数据的新颖且广泛的数据集来分析情绪和注意力变量对股市波动的影响。 我们应用最先进的情绪分类技术来调查情绪和注意力测量在控制广泛的经济和金融预测因素时是否包含对已实现波动性的额外预测能力的问题。 使用惩罚回归框架，我们通过金融关键词（例如“金融市场”和“股票市场”）的谷歌搜索次数以及特定公司的每日交易量来衡量，确定了最受投资者关注的相关变量。 StockTwits 上发布的短消息。 此外，我们的研究表明，注意力和情绪变量能够显着改善波动性预测，尽管从经济角度来看改善的幅度相对较小。
[2]BHOWMIK R, WANG S. Stock Market Volatility and Return Analysis: A Systematic Literature Review[J/OL]. Entropy, 2020, 22(5): 522. DOI:10.3390/e22050522.
Stock Market Volatility and Return Analysis: A Systematic Literature Review
股市波动与回报分析：系统文献综述
Roni Bhowmik
Shouyang Wang
Abstract
In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.
在商业研究方法领域，文献综述比以往任何时候都更加重要。 尽管传统的文献评论缺乏完整性和灵活性，人们对此类评论的质量和可信度提出了质疑。 这项研究提供了文献综述，使用系统数据库来检查和交叉引用滚雪球效应。 在本文中，还回顾了之前基于广义自回归条件异方差（GARCH）家族模型股票市场回报和波动性的研究。 股票市场在当今世界经济活动中发挥着举足轻重的作用，被称为一个国家或地区经济金融活动的“晴雨表”和“警报器”。 为了防范股市的不确定性和风险，有效衡量股指收益的波动性显得尤为重要。 然而，本次审查的主要目的是检查推荐用于执行市场回报和波动率分析的有效 GARCH 模型。 这项综述研究的第二个目的是对 12 年（2008-2019 年）期间 50 篇不同论文中的回报和波动性文献综述进行内容分析。 研究发现，过去10年里研究工作发生了重大变化，大多数研究人员都致力于发展股票市场。
Keywords:
stock returns; volatility; GARCH family model; complexity in market volatility forecasting
- Introduction
In the context of economic globalization, especially after the impact of the contemporary international financial crisis, the stock market has experienced unprecedented fluctuations. This volatility increases the uncertainty and risk of the stock market and is detrimental to the normal operation of the stock market. To reduce this uncertainty, it is particularly important to measure accurately the volatility of stock index returns. At the same time, due to the important position of the stock market in the global economy, the beneficial development of the stock market has become the focus. Therefore, the knowledge of theoretical and literature significance of volatility are needed to measure the volatility of stock index returns.
在经济全球化背景下，特别是当代国际金融危机冲击后，股市出现了前所未有的波动。 这种波动增加了股市的不确定性和风险，不利于股市的正常运行。 为了减少这种不确定性，准确衡量股指收益的波动性尤为重要。 同时，由于股票市场在全球经济中的重要地位，股票市场的良性发展成为人们关注的焦点。 因此，需要了解波动性的理论和文献意义来衡量股指收益的波动性。
Volatility is a hot issue in economic and financial research. Volatility is one of the most important characteristics of financial markets. It is directly related to market uncertainty and affects the investment behavior of enterprises and individuals. A study of the volatility of financial asset returns is also one of the core issues in modern financial research and this volatility is often described and measured by the variance of the rate of return. However, forecasting perfect market volatility is difficult work and despite the availability of various models and techniques, not all of them work equally for all stock markets. It is for this reason that researchers and financial analysts face such a complexity in market returns and volatilities forecasting.
波动性是经济和金融研究的热点问题。 波动性是金融市场最重要的特征之一。 它与市场的不确定性直接相关，影响企业和个人的投资行为。 对金融资产收益率波动性的研究也是现代金融研究的核心问题之一，这种波动性往往用收益率的方差来描述和衡量。 然而，预测完美的市场波动性是一项艰巨的工作，尽管有各种模型和技术可供使用，但并非所有模型和技术都同样适用于所有股票市场。 正是由于这个原因，研究人员和金融分析师在市场回报和波动性预测方面面临着如此复杂的问题。
The traditional econometric model often assumes that the variance is constant, that is, the variance is kept constant at different times. An accurate measurement of the rate of return’s fluctuation is directly related to the correctness of portfolio selection, the effectiveness of risk management, and the rationality of asset pricing. However, with the development of financial theory and the deepening of empirical research, it was found that this assumption is not reasonable. Additionally, the volatility of asset prices is one of the most puzzling phenomena in financial economics. It is a great challenge for investors to get a pure understanding of volatility.
传统的计量经济学模型往往假设方差是恒定的，即方差在不同时刻保持恒定。 收益率波动的准确计量直接关系到投资组合选择的正确性、风险管理的有效性以及资产定价的合理性。 然而，随着金融理论的发展和实证研究的深入，人们发现这种假设并不合理。 此外，资产价格的波动是金融经济学中最令人费解的现象之一。 对于投资者来说，获得对波动性的纯粹理解是一个巨大的挑战。
A literature reviews act as a significant part of all kinds of research work. Literature reviews serve as a foundation for knowledge progress, make guidelines for plan and practice, provide grounds of an effect, and, if well guided, have the capacity to create new ideas and directions for a particular area [1]. Similarly, they carry out as the basis for future research and theory work. This paper conducts a literature review of stock returns and volatility analysis based on generalized autoregressive conditional heteroskedastic (GARCH) family models. Volatility refers to the degree of dispersion of random variables.
文献综述是各类研究工作的重要组成部分。 文献综述可以作为知识进步的基础，为计划和实践提供指导，提供效果的基础，并且如果指导得当，有能力为特定领域创造新的想法和方向[1]。 同样，它们也是未来研究和理论工作的基础。 本文对基于广义自回归条件异方差（GARCH）族模型的股票收益和波动率分析进行了文献综述。 波动性是指随机变量的分散程度。
Financial market volatility is mainly reflected in the deviation of the expected future value of assets. The possibility, that is, volatility, represents the uncertainty of the future price of an asset. This uncertainty is usually characterized by variance or standard deviation. There are currently two main explanations in the academic world for the relationship between these two: The leverage effect and the volatility feedback hypothesis. Leverage often means that unfavorable news appears, stock price falls, leading to an increase in the leverage factor, and thus the degree of stock volatility increases. Conversely, the degree of volatility weakens; volatility feedback can be simply described as unpredictable stock volatility that will inevitably lead to higher risk in the future.
金融市场的波动主要体现在资产预期未来价值的偏差。 可能性，即波动性，代表了资产未来价格的不确定性。 这种不确定性通常用方差或标准差来表征。 目前学术界对于两者之间的关系主要有两种解释：杠杆效应和波动反馈假说。 杠杆往往意味着不利消息出现，股价下跌，导致杠杆系数增大，从而股票波动程度加大。 反之，波动程度减弱； 波动反馈可以简单地描述为不可预测的股票波动，这将不可避免地导致未来更高的风险。
There are many factors that affect price movements in the stock market. Firstly, there is the impact of monetary policy on the stock market, which is extremely substantial. If a loose monetary policy is implemented in a year, the probability of a stock market index rise will increase. On the other hand, if a relatively tight monetary policy is implemented in a year, the probability of a stock market index decline will increase. Secondly, there is the impact of interest rate liberalization on risk-free interest rates. Looking at the major global capital markets, the change in risk-free interest rates has a greater correlation with the current stock market. In general, when interest rates continue to rise, the risk-free interest rate will rise, and the cost of capital invested in the stock market will rise simultaneously. As a result, the economy is expected to gradually pick up during the release of the reform dividend, and the stock market is expected to achieve a higher return on investment.
影响股票市场价格变动的因素有很多。 首先是货币政策对股市的影响非常大。 如果一年内实行宽松的货币政策，股市指数上涨的概率就会增加。 另一方面，如果一年内实施相对从紧的货币政策，股市指数下跌的概率就会增加。 其次，利率市场化对无风险利率的影响。 纵观全球主要资本市场，无风险利率的变化与当前股市有较大的相关性。 一般来说，当利率持续上升时，无风险利率就会上升，投资于股市的资金成本也会同步上升。 因此，经济有望在改革红利释放期间逐步回暖，股市有望获得较高的投资回报。
Volatility is the tendency for prices to change unexpectedly [2], however, all kinds of volatility is not bad. At the same time, financial market volatility has also a direct impact on macroeconomic and financial stability. Important economic risk factors are generally highly valued by governments around the world. Therefore, research on the volatility of financial markets has always been the focus of financial economists and financial practitioners. Nowadays, a large part of the literature has studied some characteristics of the stock market, such as the leverage effect of volatility, the short-term memory of volatility, and the GARCH effect, etc., but some researchers show that when adopting short-term memory by the GARCH model, there is usually a confusing phenomenon, as the sampling interval tends to zero. The characterization of the tail of the yield generally assumes an ideal situation, that is, obeys the normal distribution, but this perfect situation is usually not established.
波动性是价格发生意外变化的趋势[2]，但是，各种波动性都还不错。 同时，金融市场波动也直接影响宏观经济和金融稳定。 重要的经济风险因素普遍受到世界各国政府的高度重视。 因此，对金融市场波动性的研究一直是金融经济学家和金融从业者关注的焦点。 如今，很大一部分文献研究了股票市场的一些特征，例如波动性的杠杆效应、波动性的短期记忆以及GARCH效应等，但也有研究者表明，当采用短期 GARCH模型的术语记忆通常存在一个令人困惑的现象，即采样间隔趋于零。 收益率尾部的表征一般假设一种理想情况，即服从正态分布，但这种完美情况通常不成立。
Researchers have proposed different distributed models in order to better describe the thick tail of the daily rate of return. Engle [3] first proposed an autoregressive conditional heteroscedasticity model (ARCH model) to characterize some possible correlations of the conditional variance of the prediction error. Bollerslev [4] has been extended it to form a generalized autoregressive conditional heteroskedastic model (GARCH model). Later, the GARCH model rapidly expanded and a GARCH family model was created.
研究人员提出了不同的分布式模型，以便更好地描述日收益率的粗尾。 Engle[3]首先提出了自回归条件异方差模型（ARCH模型）来表征预测误差的条件方差的一些可能的相关性。 Bollerslev [4] 对其进行了扩展，形成了广义自回归条件异方差模型（GARCH 模型）。 后来GARCH模型迅速扩展，创建了GARCH家族模型。
When employing GARCH family models to analyze and forecast return volatility, selection of input variables for forecasting is crucial as the appropriate and essential condition will be given for the method to have a stationary solution and perfect matching [5]. It has been shown in several findings that the unchanged model can produce suggestively different results when it is consumed with different inputs. Thus, another key purpose of this literature review is to observe studies which use directional prediction accuracy model as a yardstick from a realistic point of understanding and has the core objective of the forecast of financial time series in stock market return. Researchers estimate little forecast error, namely measured as mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) which do not essentially interpret into capital gain [6,7]. Some others mention that the predictions are not required to be precise in terms of NMSE (normalized mean squared error) [8]. It means that finding the low rate of root mean squared error does not feed high returns, in another words, the relationship is not linear between two.
当采用GARCH族模型分析和预测收益波动性时，预测输入变量的选择至关重要，因为这将给出该方法具有平稳解和完美匹配的适当且必要的条件[5]。 多项研究结果表明，当使用不同的输入时，未更改的模型可能会产生明显不同的结果。 因此，本文综述的另一个重要目的是从现实的理解角度观察以方向性预测精度模型为尺度、以股市收益中的金融时间序列预测为核心目标的研究。 研究人员估计预测误差很小，即以平均绝对偏差 (MAD)、均方根误差 (RMSE)、平均绝对误差 (MAE) 和均方误差 (MSE) 来衡量，这些误差本质上并不解释为资本收益 [6,7 ]。 其他一些人提到预测不需要在 NMSE（归一化均方误差）方面精确 [8]。 这意味着找到低均方根误差率并不能带来高回报，换句话说，两者之间的关系不是线性的。
In this manuscript, it is proposed to categorize the studies not only by their model selection standards but also for the inputs used for the return volatility as well as how precise it is spending them in terms of return directions. In this investigation, the authors repute studies which use percentage of success trades benchmark procedures for analyzing the researchers’ proposed models. From this theme, this study’s authentic approach is compared with earlier models in the literature review for input variables used for forecasting volatility and how precise they are in analyzing the direction of the related time series. There are other review studies on return and volatility analysis and GARCH-family based financial forecasting methods done by a number of researchers [9,10,11,12,13]. Consequently, the aim of this manuscript is to put forward the importance of sufficient and necessary conditions for model selection and contribute for the better understanding of academic researchers and financial practitioners.
在这份手稿中，建议不仅根据模型选择标准对研究进行分类，而且还根据用于回报波动性的输入以及在回报方向方面花费它们的精确程度对研究进行分类。 在这项调查中，作者赞扬了使用成功百分比交易基准程序来分析研究人员提出的模型的研究。 从这个主题出发，本研究的真实方法与文献综述中用于预测波动性的输入变量的早期模型以及它们在分析相关时间序列的方向时的精确度进行了比较。 还有许多研究人员对回报和波动性分析以及基于 GARCH 系列的财务预测方法进行的其他综述研究 [9,10,11,12,13]。 因此，本文的目的是提出模型选择充分必要条件的重要性，并有助于学术研究人员和金融从业者更好地理解。
Systematic reviews have most notable been expanded by medical science as a way to synthesize research recognition in a systematic, transparent, and reproducible process. Despite the opportunity of this technique, its exercise has not been overly widespread in business research, but it is expanding day by day. In this paper, the authors have used the systematic review process because the target of a systematic review is to determine all empirical indication that fits the pre-decided inclusion criteria or standard of response to a certain research question. Researchers proved that GARCH is the most suitable model to use when one has to analysis the volatility of the returns of stocks with big volumes of observations [3,4,6,9,13]. Researchers observe keenly all the selected literature to answer the following research question: What are the effective GARCH models to recommend for performing market volatility and return analysis?
医学科学最显着地扩展了系统评价，作为在系统、透明和可重复的过程中综合研究认可的一种方式。 尽管有这种技术的机会，但它的运用在商业研究中并没有过于广泛，但它正在日益扩大。 在本文中，作者使用了系统评价过程，因为系统评价的目标是确定符合预先确定的纳入标准或对某个研究问题的回答标准的所有经验指标。 研究人员证明，当需要通过大量观察来分析股票回报的波动性时，GARCH 是最合适的模型 [3,4,6,9,13]。 研究人员敏锐地观察了所有选定的文献，以回答以下研究问题：推荐哪些有效的 GARCH 模型来进行市场波动和回报分析？
The main contribution of this paper is found in the following four aspects: (1) The best GARCH models can be recommended for stock market returns and volatilities evaluation. (2) The manuscript considers recent papers, 2008 to 2019, which have not been covered in previous studies. (3) In this study, both qualitative and quantitative processes have been used to examine the literature involving stock returns and volatilities. (4) The manuscript provides a study based on journals that will help academics and researchers recognize important journals that they can denote for a literature review, recognize factors motivating analysis stock returns and volatilities, and can publish their worth study manuscripts.
本文的主要贡献体现在以下四个方面：（1）可以推荐最好的GARCH模型用于股票市场收益和波动性评估。 (2) 本文考虑了 2008 年至 2019 年最近的论文，这些论文在之前的研究中尚未涵盖。 (3) 在本研究中，使用定性和定量方法来检验涉及股票收益和波动性的文献。 (4) 手稿提供了基于期刊的研究，这将帮助学者和研究人员认识到他们可以表示进行文献综述的重要期刊，认识到激励分析股票收益和波动性的因素，并可以发表他们值得研究的手稿。
Realized volatility forecast: structural breaks, long memory, asymmetry, and day-of-the-week effect
已实现的波动率预测：结构性断裂、长记忆、不对称性和星期效应
杨科; 陈浪南
Ke Yang, Langnan Chen
Abstract
We investigate the properties of the realized volatility in Chinese stock markets by employing the high-frequency data of Shanghai Stock Exchange Composite Index and four individual stocks from Shanghai Stock Exchange and Shenzhen Stock Exchange, and find that the volatility exhibits the properties of long-term memory, structural breaks, asymmetry, and day-of-the-week effect. In addition, the structural breaks only partially explain the long memory. To capture these properties simultaneously, we derive an adaptive asymmetry heterogeneous autoregressive model with day-of-the-week effect and fractionally integrated generalized autoregressive conditional heteroskedasticity errors (HAR-D-FIGARCH) and use it to conduct a forecast of realized volatility. Compared with other heterogeneous autoregressive realized volatility models, the proposed model improves the in-sample fit significantly. The proposed model is the best model for the day-ahead realized volatility forecasts among the six models based on various loss functions by utilizing the superior predictive ability test.
我们利用上证综指以及沪深交易所四只个股的高频数据考察了中国股票市场的实际波动率的性质，发现波动率呈现出长期波动的性质。 记忆、结构断裂、不对称和星期效应。 此外，结构断裂只能部分解释长记忆。 为了同时捕获这些属性，我们推导了具有星期效应和分数积分广义自回归条件异方差误差（HAR-D-FIGARCH）的自适应不对称异质自回归模型，并用它来预测已实现的波动率。 与其他异构自回归实现波动率模型相比，该模型显着改善了样本内拟合。 通过利用优越的预测能力测试，所提出的模型是基于各种损失函数的六种模型中日前实现波动率预测的最佳模型。
Realized Volatility Forecast of Stock Index Under Structural Breaks
结构性突破下股指波动率预测实现
Ke Yang, Langnan Chen, Fengping Tian
We investigate the realized volatility forecast of stock indices under the structural breaks. We utilize a pure multiple mean break model to identify the possibility of structural breaks in the daily realized volatility series by employing the intraday high-frequency data of the Shanghai Stock Exchange Composite Index and the five sectoral stock indices in Chinese stock markets for the period 4 January 2000 to 30 December 2011. We then conduct both in-sample tests and out-of-sample forecasts to examine the effects of structural breaks on the performance of ARFIMAX-FIGARCH models for the realized volatility forecast by utilizing a variety of estimation window sizes designed to accommodate potential structural breaks. The results of the in-sample tests show that there are multiple breaks in all realized volatility series. The results of the out-of-sample point forecasts indicate that the combination forecasts with time-varying weights across individual forecast models estimated with different estimation windows perform well. In particular, nonlinear combination forecasts with the weights chosen based on a non-parametric kernel regression and linear combination forecasts with the weights chosen based on the non-negative restricted least squares and Schwarz information criterion appear to be the most accurate methods in point forecasting for realized volatility under structural breaks. We also conduct an interval forecast of the realized volatility for the combination approaches, and find that the interval forecast for nonlinear combination approaches with the weights chosen according to a non-parametric kernel regression performs best among the competing models. Copyright © 2014 John Wiley & Sons, Ltd.
我们研究了结构性突破下股指的已实现波动率预测。 我们利用纯多重均值突破模型，利用上证综指和中国股市五个板块股指第 4 阶段的日内高频数据，来识别日实现波动率序列出现结构性突破的可能性。 2000 年 1 月至 2011 年 12 月 30 日。然后，我们进行样本内测试和样本外预测，以检查结构性断裂对 ARFIMAX-FIGARCH 模型性能的影响，以利用各种估计窗口大小进行实际波动率预测 旨在适应潜在的结构断裂。 样本内检验的结果表明，所有已实现的波动率序列均存在多次突破。 样本外点预测的结果表明，使用不同估计窗口估计的各个预测模型的时变权重组合预测表现良好。 特别是，基于非参数核回归选择权重的非线性组合预测和基于非负限制最小二乘和 Schwarz 信息准则选择权重的线性组合预测似乎是点预测中最准确的方法。 结构性断裂下的实际波动。 我们还对组合方法的已实现波动率进行了区间预测，并发现根据非参数核回归选择权重的非线性组合方法的区间预测在竞争模型中表现最佳。 版权所有 © 2014 约翰·威利父子有限公司
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