China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

Markov-Switching Time-Varying Copula Modeling of Dependence Structure between Oil and GCC Stock Markets

This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.

Volatility Risk Management of Chinese Stock Grading Market——An Empirical Study of GARCH-VaR Model

This paper mainly through the comparison of GARCH-VaR China stock market board,small board and gem in the United States correspond to the three stock index volatility,volatility between stock indexes obtained U.S.stock market volatility risk multi-level market differences.As a suggestion and reference for investors,it can also provide reference for the supervision department of stock market risk.Based on the empirical research,analyzes the advantages and disadvantages of traditional risk measurement methods,and combined with GARCH model with high degree of complexity and the practice effect analysis,trying to find the objective measure stock model analysis.In the specific study of the volatility of the stock market,through the comparison of China’s three major plates and the market classification mechanism of mature U.S.stock market,combined with the objective situation of the market,draw conclusions and change expectations.From the empirical results,the U.S.stock market has recovered after the financial crisis,and its performance on risk volatility is better than China’s three major plates.From the comparison of the stock market in the same country,the small and medium-sized plates tend to have greater risks,while the risks of the main board and the gem have the characteristics of low average value but frequent fluctuations.

Can the Baidu Index predict realized volatility in the Chinese stock market?

This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

Dynamic connectedness between stock markets in the presence of the COVID‑19 pandemic:does economic policy uncertainty matter?

This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.

ARIMA and Facebook Prophet Model in Google Stock Price Prediction

We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.

SIMPLEST DIFFERENTIAL EQUATION OF STOCK PRICE,ITS SOLUTION AND RELATION TO ASSUMPTION OF BLACK-SCHOLES MODEL

Two kinds of mathematical expressions of stock price, one of which based on certain description is the solution of the simplest differential equation (S.D.E.) obtained by method similar to that used in solid mechanics,the other based on uncertain description (i.e., the statistic theory)is the assumption of Black_Scholes's model (A.B_S.M.) in which the density function of stock price obeys logarithmic normal distribution, can be shown to be completely the same under certain equivalence relation of coefficients. The range of the solution of S.D.E. has been shown to be suited only for normal cases (no profit, or lost profit news, etc.) of stock market, so the same range is suited for A.B_ S.M. as well.

Pyramid scheme in stock market:A kind of financial market simulation

Artificial stock market simulation based on agent is an important means to study financial market.Based on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes.Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main fund.Our work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.

Effects of Financial Market Variables on Stock Prices:A Review of the Literature

Stock market plays a pivotal role in firms’expansion and turns economic growth.In the literature,because of the importance of stock markets to the real economy,the smooth and risk-free operation of the stock market has attracted significant attention.The finance literature contains a large number of studies that examine the stock price behaviour with some emphasis on the determinants of the relationship between the equity prices and the financial market activities.The present study reviews the previous works of the effect of financial market variables and stock price.Five selected financial market variables,market capitalization,earnings per share,price earnings multiples,dividend yield,and trading volume are reviewed in this study.In the past literature,there are the opinions of the positive significant relationship between market capitalization and stock price.To find the relationship between dividend yield and stock price,there are two broad schools of thoughts.Both of the relevance and irrelevance theory of Gordon and Modigliani have the strong evidence in the current literature that keeps on the dilemma and provides the scopes for future research.Price-earnings multiples are analyzed in the past literature by using different variables.Based on that,it is evidenced that price-earnings multiples have a negative significant effect on stock price.The reviewed studies state the cointegrating relationship between the stock price and the trading volume as the trading volume is a source of risk.

The Research of the Correlation between Stock Market and Macroeconomy--Based on Comparison of Chinese and American Stock Markets

Since 2008,the economic volume of the United States has gradually decreased from 30% of world GDP to 20%-25%,and China has risen from 7% to 15%.However,face at a fast-growing economy,China's stock market has been sluggish,contrast strongly to the US's thriving stock market.This paper studies the correlation between stock market and macroeconomy,based on the perspective of stock market and macroeconomy between China and the United States.This article takes China and the United States from 1999 to early 2017 as the time frame.Choosing the Shanghai Stock Exchange securities market,the S&P 500 index and the macroeconomy indicators and policies of China and the United States as research objects,using a comparative method to study the interactive relationship between the two major economies.In addition,this paper analyzes the parts of macroeconomy and microlisted companies in economic and financial theory,and innovatively applies the four different aspects of macroeconomy of total seven indicators to more fully represent the macroeconomy.This paper establishes the VAR model,impulsive response,and variance decomposition to explore the interaction between trends of the stock market and macroeconomic trends.The research results show that the stock market trend is positively related to the macroeconomic trend.China's stock market is greatly affected by capital,and the reason why the US stock market can develop better under the condition that the macroeconomic development is not as good as China's,because of the unique status of the US dollar.Finally,this paper combines descriptive analysis and empirical analysis results to propose policy recommendations for China's stock market and macroeconomic development.

Stock Market Turnover and China’s Real Estate Market Price: An Empirical Study Based on VAR

Our analysis used the monthly data of the average sales price of commodity houses and stock turnover in the Shenzhen Stock Exchange from January 2016 to December 2020. We selected this data to establish a Vector Autoregression(VAR) model using the Granger causality test to investigate the correlation between the stock market and the real estate market. We found that there is a significant positive correlation between the stock market and the real estate market. We also found that the real estate market price is the one-way Granger cause for the stock market turnover, and that changes in the real estate market price have a significant role in forecasting changes in stock market turnover. Therefore, the linkage between the two markets should be considered in macro regulations, and the impact on one of the markets should be considered when regulating the other.

Exploring Apple’s Stock Price Volatility Using Five GARCH Models

The financial market is the core of national economic development,and stocks play an important role in the financial market.Analyzing stock prices has become the focus of investors,analysts,and people in related fields.This paper evaluates the volatility of Apple Inc.(AAPL)returns using five generalized autoregressive conditional heteroskedasticity(GARCH)models:sGARCH with constant mean,GARCH with sstd,GJR-GARCH,AR(1)GJR-GARCH,and GJR-GARCH in mean.The distribution of AAPL’s closing price and earnings data was analyzed,and skewed student t-distribution(sstd)and normal distribution(norm)were used to further compare the data distribution of the five models and capture the shape,skewness,and loglikelihood in Model 4-AR(1)GJR-GARCH.Through further analysis,the results showed that Model 4,AR(1)GJR-GARCH,is the optimal model to describe the volatility of the return series of AAPL.The analysis of the research process is both,a process of exploration and reflection.By analyzing the stock price of AAPL,we reflect on the shortcomings of previous analysis methods,clarify the purpose of the experiment,and identify the optimal analysis model.

Application of Hidden Markov Models in Stock Forecasting

In this paper,we tested our methodology on the stocks of four representative companies:Apple,Comcast Corporation(CMCST),Google,and Qualcomm.We compared their performance to several stocks using the hidden Markov model(HMM)and forecasts using mean absolute percentage error(MAPE).For simplicity,we considered four main features in these stocks:open,close,high,and low prices.When using the HMM for forecasting,the HMM has the best prediction for the daily low stock price and daily high stock price of Apple and CMCST,respectively.By calculating the MAPE for the four data sets of Google,the close price has the largest prediction error,while the open price has the smallest prediction error.The HMM has the largest prediction error and the smallest prediction error for Qualcomm’s daily low stock price and daily high stock price,respectively.

Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning

Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven,highly interconnected,and sector-integrated energy system.Simulation models allow testing market designs before implementation,which offers advantages for market robustness and efficiency.This work presents a novel approach to simulate the electricity market by using multi-agent deep reinforcement learning for representing revenue-maximizing market participants.The learning capability makes the agents highly adaptive,thereby facilitating a rigorous performance evaluation of market mechanisms under challenging yet practical conditions.Through distinct test cases that vary the number and size of learning agents in an energy-only market,we demonstrate the ability of the proposed method to diagnose market manipulation and reflect market liquidity.Our method is highly scalable,as demonstrated by a case study of the German wholesale energy market with 145 learning agents.This makes the model well-suited for analyzing large and complex electricity markets.The capability of the presented simulation approach facilitates market design analysis,thereby contributing to the establishment future-proof electricity markets to support the energy transition.

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

中国新能源股票市场收益及其波动相关性

本文利用2017年9月至2022年9月中国中证新能源指数、中证航空主题指数、中证医药健康100策略指数、中证农业主题指数和中证有色金属指数的交易日收盘价数据,采用3种多元GARCH模型(CCC-GARCH、DCC-GARCH和ADCC-GARCH)探索中国新能源公司与航空公司、医药健康公司、农业公司以及有色金属公司之间的动态股票收益与波动相关性。条件均值方程的估计结果显示,航空公司、医药健康公司与农业公司滞后1期的股票收益显著影响新能源公司的当期股票收益。CCC-GARCH、DCC-GARCH和ADCC-GARCH模型估计的条件相关系数表明,样本期内中国新能源公司的股票回报与有色金属公司的股票回报的条件相关性最大,其次依次为航空公司、农业公司和医药健康公司。通过滚动向前一步的动态条件相关性检验也表明了估计结果的稳定性。因此,投资者可以考虑将有色金属公司的股市表现作为预测新能源公司股票回报的先行指标,以降低投资风险。

资本市场开放与管理层语调操纵

资本市场开放如何促进上市公司高质量发展是当前金融改革面临的重大课题,而频发的管理层语调操纵现象则凸显了公司治理存在的“痛点”和“堵点”。本文以2010—2020年的沪市A股上市公司为样本,基于PSM-DID模型考察了“沪港通”互联互通机制对管理层语调操纵的影响。研究发现,“沪港通”机制的实施能有效抑制管理层语调操纵行为,其治理功效主要是通过缓解公司融资约束和提高公司透明度的途径得以发挥,且上述治理作用在机构投资者治理水平较高、非国有企业、高管过度自信程度较低以及领导权力分离的样本中更为显著。进一步研究表明,行业竞争强度弱化了资本市场开放对管理层语调操纵的抑制效应,而分析师跟踪会强化其治理效果。经济后果检验发现,“沪港通”机制通过约束管理层语调操纵降低了企业破产风险,并提升了企业财务绩效水平。本研究从管理层语调操纵视角证实了资本市场开放具有治理溢出效应,也为促进上市公司提高文本信息披露质量提供了经验证据。