上海A股市场股价波动相互影响能力实证研究

股票市场中股票价格的波动是相互影响的,但不同的股票其价格波动时对其他股票价格行为的影响能力是不同的。本文通过对我国上海A股市场的实证分析表明,有少数股票其价格波动时对其他股票价格行为有很强的影响能力,而大多数股票的这种影响能力很小。此外,单个股票价格波动时引起其他股票价格涨、跌的能力一般都有显著差异。     

影响股票价格的两因素模型

我国股票价格的波动较发达国家成熟的金融市场来讲有很大的非理性成分,因而应从整体宏观实体经济发展和货币供应量两个因素来研究股票价格的波动.     

货币政策的资产价格传递机制分析

（一）股票价格波动对投资的影响。Tobin的q理论（Tobin，1978）为股票价格波动对经济的影响提供了一个重要的传递机制。托宾的q值是指企业的股票市场价格与企业的资本重置之比。假设资本的边际产量MPK固定不变，折旧率为d，利息率为r，P为企业的股票价格。     

正反馈交易与股票价格关系研究综述

在阐述正反馈交易与股票价格关系的提出的基础上,就国内外学者对正反馈交易与股票价格关系的研究成果进行综述,表明正反馈交易能够很好地描述出股票价格波动过程,同时指出我国股市一直存在着比较严重的正反馈交易行为.     

股价波动源模型下欧式下出局期权的鞅定价

机游走模型(Random Walk)和对数正态分布模型是两种最常见的描述股票价格的模型,但是这两种都存在着一定的缺陷,距离实际上的波动还具有较大的差距。波动源模型更全面的考虑了大量散户交易者对股票价格的影响,以及其他的一些因素,能够更加接近实际的描述出股票的价格变动以及波动现象.在股票价格波动源模型下,利用Martingale Pricing方法推导出欧式下出局期权(买权)的定价公式.作为特例,同时得到了传统的对数正态分布模型下欧式下出局期权的定价公式。     

房地产调控政策对地产上市公司股价影响的实证研究

本文分别运用非参数推断统计与事件研究法分析了我国2010年至2013年间国务院出台的房地产调控政策对我国房地产上市公司股票价格波动产生的影响。研究发现,房地产宏观调控政策对房地产上市公司股票价格波动有一定程度的短期影响,政策效应能在短期内从实体经济传递到证券市场。     

股市波动与经济周期的关系——基于股利折现模型的一个推导

本文基于股利折现模型(DDM),通过比较静态分析方法,推导并分析了股市波动与经济周期之间的相关性。文章指出:在整个经济周期中,预期收入和利率的变化的相互作用引起了股票价格水平的波动,股票价格的变化总是与预期收入变化呈正相关关系、与利率变化呈负相关关系,股价波动先于经济波动,股票市场引导经济周期。     

电力行业上市公司股票价格影响因素实证分析

影响上市公司股票价格波动的因素很多,其中财务业绩无疑是最重要的。利用统计和计量方法对上市公司财务状况进行分析,进而找出与股票价格的关系,我们发现股票价格与反映公司盈利能力的每股收益变量呈显著正相关,与流通股本规模呈显著负相关。     

我国房地产业股价与会计信息的相关性研究

会计信息的决策有用性越来越为人们所关注,它在资本市场上最明显的反映就是股票价格会随相关会计信息的披露而波动,本文运用数学模型说明会计信息对房地产行业股票价格的影响。     

价格涨跌幅机制对市场波动及换手率的影响

文章调查了价格限削机制对股票价格波动以及市场流动性的影响.首先刻画了实行价格涨跌幅限制后股票价格的变化特征,研究价格限制机制对股票价格波动的影响;随后在考虑延续冲击效应因素的基础上,通过对引入价格限制机制后投资者总成本的考察,研究了该机制对市场流动性的影响.文章结论认为,股票价格涨跌幅限制机制的引入,将增加股票市场的波动性,并且会导致投资者心理所能承受的潜在收益、损失量减小,使投资者更加频繁地买卖股票,从而增加市场换手率,进而提高整个市场的流动性.     

New insights into the role of global factors in BRICS stock markets: A quantile cointegration approach

Because of the acceleration in marketization and globalization, stock markets in the BRICS (Brazil, Russia, India, China, and South Africa) countries are affected by various global factors, for example, oil prices, gold prices, global stock market volatility, global economic policy uncertainty, financial stress, and investor sentiment. This paper offers new insights into the short- and long-run linkages between global factors and BRICS stock markets by applying the quantile autoregressive distributed lags (QARDL) approach. This novel methodology enables us to test short- and long-run linkages accounting for distributional asymmetry. That is, the nonlinear dynamic relationship between the global factors and BRICS stock prices depends on market conditions. Our empirical results show that the effects of gold prices and global stock market volatility on BRICS stock prices are more significant in the long run than in the short run. A decrease in global stock market volatility is associated with higher stock prices, while gold prices demonstrate upward co-movement in dynamic correlations with stock markets. Irrational factors, such as economic policy uncertainty, financial stress, and investor sentiment, play a critical role in the short term, and negative interdependence is dominant. Finally, the rolling-window estimation technique is used to examine time-varying patterns between major global factors and BRICS stock markets.     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

Monetary policy and stock prices in small open economies: Empirical evidence for the new EU member states

This study presents evidence on the effect of domestic and Euro Area monetary policy on stock prices in four new EU member states of Central Europe and the main determinants of stock price volatility, estimating structural vector autoregressive models identified with short-run restrictions. We find that stock prices in the considered new EU member states are more sensitive to changes in the Euro Area interest rate than to the domestic one. Moreover, the bulk of stock price volatility in these countries is due to shocks related to exchange rate and Euro Area monetary policy. Overall, we find that local stock markets are more sensitive to external shocks than to domestic ones.     

Determining hedges and safe havens for stocks using interval analysis

We examine whether hedging and safe haven assets exist against stocks when market high and low prices evaluate asset prices. Using interval-based estimations, this paper finds that 10-year government bonds, the U.S. dollar, and gold served as weak hedging and/or safe haven assets for the stock market losses over the 2002–2019 period. We also provide evidence of the USD’s and gold’s hedging ability against the stock market volatility and of volatility transmission between assets, and highlight the importance of considering volatility.     

Economic policy uncertainty,oil price volatility and stock market returns: Evidence from a nonlinear model

This paper investigates the nonlinear relationship between economic policy uncertainty, oil price volatility and stock market returns for 25 countries by applying the panel smooth transition regression model. We find that oil price volatility has a negative effect on stock returns, and this effect increases with economic policy uncertainty. Furthermore, there is pronounced heterogeneity in responses. First, oil-exporting countries whose economies depend more on oil prices respond more strongly to oil price volatility than oil-importing countries. Second, stock returns of developing countries are more susceptible to oil price volatility than that of developed countries. Third, crisis plays a crucial role in the relation between oil price volatility and stock returns.     

我国沪、深股市的波动性研究——基于GARCH族模型

金融市场的波动性不仅是投资者关注的焦点之一,而且也是被研究的热点之一。中国股市还非常年轻,股票市场的价格常常表现出大幅波动的特征。本研究以上证综合指数和深圳成分指数为研究对象,分别运用GARCH模型、TARCH模型和EGARCH模型同时拟合,并对比分析了中国股市日收益率波动的动态特征;结果显示,EGACH模型能更有效拟合股市的波动性。     

Monetary Policy and Asset Prices: A Markov‐Switching DSGE Approach

This paper estimates a Markov‐switching dynamic stochastic general equilibrium model by incorporating stock prices in monetary policy rules in order to identify the Federal Reserve's stance toward them. Based on the data from 1984:Q1 to 2009:Q2, I find that historical evidence of the policy reaction toward stock prices is weak except for the stock market bubble of the 1990s. A counterfactual exercise shows that the rapid growth in stock prices during that period would have been significantly higher if monetary policy had been independent of the stock market. However, unconditional macroeconomic volatility increases with the degree of policy responsiveness toward stock prices. Copyright © 2017 John Wiley & Sons, Ltd.     

The asymmetric effect of crude oil prices on stock prices in major international financial markets

The aim of this paper is to explore the potential asymmetric impacts of positive and negative shocks in crude oil prices on stock prices in six major international financial markets which include China, Hong Kong, America, Japan, Britain, and Germany. We test for these asymmetric effects on 8 major international financial markets indices over the 2007M01–2020M03 periods. Our independent measures include the prices of Brent crude oil futures and West Texas Intermediate (WTI) futures. We use the nonlinear ARDL (NARDL) model proposed by Shin et al. (2014), which can capture both short- and long-run nonlinearities through positive and negative partial sum decompositions of the explanatory variables. This research finds that positive and negative fluctuations of oil price have asymmetric effects on stock price index in four financial markets, but the performance of the asymmetry is different. Specifically, the impacts of volatility in oil prices on two indices of Chinese stock prices are different, and the asymmetric effects of oil price volatility on stock price indices in China and other financial markets are significantly different.     

Day trading and stock price volatility

When an investor buys and sells the same stock on the same day, he is said to have made a day trade. Using the trading records of Finnish traders, this paper examines whether day trading is related to volatility of stock prices. I find a strong positive time-series relation between the number of day trades by individual investors and intraday volatility among heavily day traded stocks. This effect is robust after controlling for a previously documented volume–volatility relation. The result suggests that the joint hypothesis of price pressure and volatility induced day trading dominates the liquidity effects of day trading.     

Stock market volatility,the news,and monetary policy

The dynamic relationship linking the volatility of equity prices with “the news” and the expected path for monetary policy is investigated. Previous results that link the impact of the news about real activity to changes in current and future interest rates are employed in developing a positive link between changes in volatility and the news. Empirically, our results uncover a positive and statistically significant response of the CBOE volatility index, VIX, to unanticipated changes in employment, but not to inflation. Hence, agents' expectations for the policy response to news have an important influence on the expected volatility of stock prices. (JEL E44, E52)