Dependence structures between Chinese stock markets and the international financial market: Evidence from a wavelet-based quantile regression approach

In this study, we investigate the dependence structures between six Chinese stock markets and the international financial market including possible safe haven assets and global economic factors under different market conditions and investment horizons. The research is conducted by combining a quantile regression approach with a wavelet decomposition analysis. Although we find little or insignificant dependence under short investment horizons, we detect the strong asymmetric dependence of oil prices and the US dollar index on the six Chinese stock markets in the medium and long terms. Moreover, not only is crude oil not a safe haven, it may damage Chinese stock markets as it increases over the long term, even in bull markets. Meanwhile, appreciation of the US dollar (depreciation of RMB) damages (boosts) Chinese stock markets during bull (bear) market conditions under long investment horizons. Moreover, we find that VIX (volatility index)-related derivatives may serve as good risk management tools under any market condition, while gold is a safe haven asset only during crisis periods.     

Searching hedging instruments against diverse global risks and uncertainties

We evaluate the influence of five major risk and uncertainty factors on four asset classes. Our time-varying findings suggest that each asset hedges only a particular uncertainty factor, whereas gold does more than one factor, especially during COVID-19. Our frequency-based quantile regression (QR) results show that in the raw frequency, gold and Islamic stock can better hedge various uncertainty factors than Bitcoin and crude oil, depending on the market conditions. Additionally, using the frequency bands (e.g., short, medium, and long term) data, we further notice that, depending on the market circumstances and investment horizons, gold and Islamic stock returns are still better hedges for the various risks and uncertainties than Bitcoin and crude oil returns. Our findings have crucial risk and portfolio management implications for investors, portfolio managers, and policymakers.     

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.     

Time–frequency quantile dependence between Bitcoin and global equity markets

In this paper, we examine return dependence between Bitcoin and stock market returns using a novel quantile cross-spectral dependence approach. The results suggest a right-tail (high return) dependence between Bitcoin and the stock markets in the long term and that said dependence decreases significantly from yearly to monthly investment horizons. Furthermore, right-tail dependence between Bitcoin and the US stock market is the strongest compared with other stock markets. We also extract information on the time-varying and time–frequency structure of co-movements between Bitcoin and the stock markets using wavelet-coherence analysis, the results of which suggest that the co-movement between Bitcoin and the US stock market is positive, whereas, for other stock markets, it is negative at certain frequencies and time periods. Overall, the findings highlight additional risk-management capabilities of Bitcoin according to different stock markets.     

Predicting failure risk using financial ratios: Quantile hazard model approach

This study examines the role of financial ratios in predicting companies’ default risk using the quantile hazard model (QHM) approach and compares its results to the discrete hazard model (DHM). We adopt the LASSO method to select essential predictors among the variables mentioned in the literature. We show the preeminence of our proposed QHM through the fact that it presents a different degree of financial ratios’ effect over various quantile levels. While DHM only confirms the aftermaths of “stock return volatilities” and “total liabilities” and the positive effects of “stock price”, “stock excess return”, and “profitability” on businesses, under high quantile levels QHM is able to supplement “cash and short-term investment to total assets”, “market capitalization”, and “current liabilities ratio” into the list of factors that influence a default. More interestingly, “cash and short-term investment to total assets” and “market capitalization” switch signs in high quantile levels, showing their different influence on companies with different risk levels. We also discover evidence for the distinction of default probability among different industrial sectors. Lastly, our proposed QHM empirically demonstrates improved out-of-sample forecasting performance.     

近期股市价格走势与宏观经济协调性风险分析

股市价格走势与宏观经济运行的协调性是投资分析中很重要的研究问题。文章基于传统理论,对现阶段二者的协调性进行了分析,得出了其对投资分析具有较大风险的结论。通过深究原因,提出几个简单的解决方法。     

投资者信念异质性和适应性对股市价格的影响

考察了异质性信念、具有适应性的交易者相互作用下股市价格行为。通过数字模拟,分析实际市场上常用的价值分析和技术分析策略对价格动力学的作用,说明不同参数对价格行为的影响。     

Macroeconomic influences on the stock market

The paper studies the dynamic interactions among indicators of economic activity, such as industrial production, interest rate and exchange rate, the performance of the foreign stock market, oil prices, and stock returns to examine whether economic activity movements affect the performance of the stock market for Greece. The empirical evidence suggests that stock returns do not lead changes in real economic activity while the macroeconomic activity and foreign stock market changes explain only partially stock market movements. Oil price changes explain stock price movements and have a negative impact on macroeconomic activity.     

Financial information and macroeconomic forecasts

We study the forecasting power of financial variables for macroeconomic variables in 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices, and house prices have considerable predictive power for macroeconomic variables at the one- to four-quarter horizons. A forecasting model that includes financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85% of our sample countries at the four-quarter horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.     

Examining macroeconomic models through the lens of asset pricing

We develop new methods for representing the asset-pricing implications of stochastic general equilibrium models. We provide asset-pricing counterparts to impulse response functions and the resulting dynamic value decompositions (DVDs). These methods quantify the exposures of macroeconomic cash flows to shocks over alternative investment horizons and the corresponding prices or investors’ compensations. We extend the continuous-time methods developed in Hansen and Scheinkman (2012) and Borovi?ka et al. (2011) by constructing discrete-time, state-dependent, shock-exposure and shock-price elasticities as functions of the investment horizon. Our methods are applicable to economic models that are nonlinear, including models with stochastic volatility.     

Time-frequency effect of crude oil and exchange rates on stock markets in BRICS countries: Evidence from wavelet quantile regression analysis

This study examines the effects of oil prices and exchange rates on stock market returns in BRICS countries (Brazil, Russia, China, India and South Africa) from a time–frequency perspective over the period 2009–2020. We use wavelet decomposition series to develop a threshold rolling window quantile regression to detect time–frequency effects at various scales. The empirical results are as follows. First, our findings confirm that the effects of both crude oil prices and exchange rates on BRICS stock returns are asymmetric. Positive shocks of crude oil have a greater impact on a bull market, whereas negative shocks have a greater impact on a bear market. Second, there is a short-term enhancement effect of crude oil and exchange rate on BRICS stock markets. In addition, volatility in the macro financial environment also exacerbates the impacts of oil prices and exchange rates on the stock market, and these fluctuations are heterogeneous. Overall, these findings provide useful insights for international investors and policy makers.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Firms’ fundamentals,macroeconomic variables and quarterly stock prices in the US

This paper modeled the effects of firms’ fundamentals such as total assets and long-term debt and of macroeconomic variables such as unemployment and interest rates on quarterly stock prices of over 3000 US firms in the period 2000–07. The merged CRSP/Compustat database was augmented by macroeconomic variables and comprehensive dynamic models were estimated by maximum likelihood taking into account heterogeneity across firms. Likelihood ratio statistics were developed for sequentially testing hypotheses regarding the adequacy of macroeconomic variables in the models. The main findings were that the estimated coefficients of lagged stock prices in simple dynamic random effects models were in the interval 0.90–0.95. Second, comprehensive dynamic models for stock prices showed that the firms’ earnings per share, total assets, long-term debt, dividends per share, and unemployment and interest rates were significant predictors; there were significant interactions between firms’ long-term debt and interest rates. Finally, implications of the results for corporate policies are discussed.     

Detecting speculative bubbles in an IT-intensive stock market

Using a battery of unit root test procedures and cointegration analysis with alternative null hypotheses we find some evidence of speculative bubbles in the Finnish stock market for monthly data on industry portfolio stock prices and returns from the 1990s. When analyzing the time series behavior of stock market prices and returns against the development of certain macroeconomic fundamentals, the bubbles seem to be present especially in the information technology (IT) prices and only during the latter half of the decade. (JEL C22, G12)     

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.     

我国证券投资基金重仓持股变动对股票价格波动性影响的实证研究

文章采用了2006年第一季度到2008年第四季度国内证券投资基金公布的季度数据,运用动态面板数据模型对我国证券投资基金重仓持股变动与股票价格波动性之间的关系进行了实证研究。结果表明,在牛市中证券投资基金重仓持股比例的变化与股价波动性成正向关系,加剧了股价的波动;在熊市中证券投资基金持股比例变化与股票价格波动性成反向关系,起到了稳定证券市场的作用。     

Stablecoins as diversifiers,hedges and safe havens: A quantile coherency approach

This study investigates the diversifier, hedge and safe haven properties of stablecoins against various financial assets including cryptocurrencies such as Bitcoin, Ether, XRP and stock market indices. Using quantile coherency we show that stablecoins included in the study act as weak hedges in normal conditions and weak safe havens when considering moments of market turmoil and there is little evidence to support the existence of any contagion effects between the cryptocurrency and stablecoin markets. Aforementioned results are not significantly influenced by the choice of investment horizon. We further evaluate the implications of those results for the question of whether stablecoins are in fact stable.     

House prices and consumer welfare

We develop a new approach to measuring changes in consumer welfare due to changes in the price of owner-occupied housing. In our approach, an agent's welfare adjustment is defined as the transfer required to keep expected discounted utility constant given a change in current house prices. We demonstrate that, up to a first-order approximation, there is no aggregate change in welfare due to price increases in the existing housing stock. This follows from a simple market clearing condition where capital gains experienced by sellers are exactly offset by welfare losses to buyers. We show that this result holds (approximately) even in a model that accounts for changes in consumption and investment plans prompted by current house price changes. There can, however, be changes in welfare due to additions to the stock of housing, or to changes in the price of renovating and upgrading the existing stock of housing. For the United States, we estimate the welfare cost of house price appreciation to be an average of $127 per household per year over the 1984–1998 period.     

Anticipating Long‐Term Stock Market Volatility

We investigate the relationship between long‐term US stock market risks and the macroeconomic environment using a two‐component GARCH‐MIDAS model. Our results show that macroeconomic variables are important determinants of the secular component of stock market volatility. Among the various macro variables in our dataset the term spread, housing starts, corporate profits and the unemployment rate have the highest predictive ability for long‐term stock market volatility. While the term spread and housing starts are leading variables with respect to stock market volatility, for industrial production and the unemployment rate expectations data from the Survey of Professional Forecasters regarding the future development are most informative. Copyright © 2014 John Wiley & Sons, Ltd.     

Stock prices and domestic and international macroeconomic activity: a cointegration approach

This paper supports the existence of a significant, long-run relationship between stock prices and domestic and international economic activity in six European economies. Johansen Cointegration tests demonstrate that stock price levels are significantly related to industrial production, business surveys of manufacturing orders, short- and long-term interest rates as well as foreign stock prices, short-term interest rates and production. Variance decomposition methods support the strong explanatory power of macroeconomic variables in contributing to the forecast variance of stock prices. Hence, stock prices are determined by macroeconomic activity.