scholarly journals Dynamic Spillover Effects between the US Stock Volatility and China’s Stock Market Crash Risk: A TVP-VAR Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ping Zhang ◽  
Jieying Gao ◽  
Yanbin Zhang ◽  
Te-Wei Wang
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
Crash Risk ◽  
Short Term ◽  
Stock Volatility ◽  
Gradual Improvement ◽  
Stock Market Crash ◽  
Capital Outflow ◽  
Flight To Quality ◽  
The Us

Due to the increasing linkage of China and the US stock markets today, we constructed a TVP-VAR model to study the dynamic spillover effects between the US stock volatility and China’s stock market crash risk. We found dynamic spillover effects are constantly strengthening between US stock volatility and China’s stock market crash risk: when the US stock volatility increases, China’s stock market crash risk increases. In addition, the gradual improvement of financial market openness in China, the short-term capital outflow from China, and the depreciation of the RMB exchange rate will increase China’s stock market crash risk. And, the impacts of short-term capital outflow from China are more significant. Further, the increase in China’s stock market crash risk will lead to the decline of the US stock volatility, which may be due to the flight to quality.

scholarly journals Preventing crash in stock market: The role of economic policy uncertainty during COVID-19

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Peng-Fei Dai ◽  
Xiong Xiong ◽  
Zhifeng Liu ◽  
Toan Luu Duc Huynh ◽  
Jianjun Sun
Keyword(s):  
Economic Policy ◽  
Stock Market ◽  
Negative Correlation ◽  
Crash Risk ◽  
Policy Uncertainty ◽  
Stock Market Crash ◽  
Economic Policy Uncertainty ◽  
The Us ◽  
The Impact

AbstractThis paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the epidemic.

An empirical study on big data stock volatility forecasting algorithm based on multivariate hybrid criterion fuzzy model

2021 ◽  
pp. 002072092098399
Author(s):  
Zhang Xiao-Wen ◽  
Zeng Min
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Fuzzy Model ◽  
Reference Value ◽  
Developed Countries ◽  
Theoretical Research ◽  
Prediction Algorithm ◽  
Real Economy ◽  
Stock Volatility ◽  
The Us

The fluctuation of the stock market has always been a matter of great concern to investors. People always hope to judge the trend of the stock market through the trend of the K line, so as to obtain the price difference through trading, Therefore, it is a theoretical research concerned by the academic circles to carry out empirical research through big data stock volatility prediction algorithm, so as to establish a model to predict the trend of the stock market. After decades of development, China's stock market has gradually matured in continuous exploration. However, compared with the stock market in developed countries, there are still imperfections. For example, the market value of China's stock market does not improve well with economic growth. Year-on-year growth and the development of the real economy. By studying the historical data from 2002 to 2017, we use the Multivariate Mixed Criterion Fuzzy Model (MMCFM) to predict the price changes in the stock market, and obtain the market in China through error statistical analysis. (SSE) is more unstable than the US stock market. Therefore, Multivariate Mixing Criterion (MMC) can be used as a reference indicator to visually measure market maturity. In this paper, we establish a multivariate mixed criteria fuzzy model, and use big data to predict the stock volatility. The algorithm verifies the reliability and accuracy of the model, which has a good reference value for investors.

scholarly journals INTERRELATIONSHIPS AMONG INTERNATIONAL STOCK MARKET INDICES: EUROPE, ASIA AND THE AMERICAS

2005 ◽  
Vol 08 (05) ◽  
pp. 603-622 ◽  
Author(s):  
ADEL SHARKASI ◽  
HEATHER J. RUSKIN ◽  
MARTIN CRANE
Keyword(s):  
Hong Kong ◽  
Wavelet Transform ◽  
Stock Market ◽  
Stock Markets ◽  
Spillover Effects ◽  
Data Series ◽  
Testing Method ◽  
International Stock Markets ◽  
The Us ◽  
European Markets

In this paper, we investigate the price interdependence between seven international stock markets, namely Irish, UK, Portuguese, US, Brazilian, Japanese and Hong Kong, using a new testing method, based on the wavelet transform to reconstruct the data series, as suggested by Lee [11]. We find evidence of intra-European (Irish, UK and Portuguese) market co-movements with the US market also weakly influencing the Irish market. We also find co-movement between the US and Brazilian markets and similar intra-Asian co-movements (Japanese and Hong Kong). Finally, we conclude that the circle of impact is that of the European markets (Irish, UK and Portuguese) on both American markets (US and Brazilian), with these in turn impacting on the Asian markets (Japanese and Hong Kong) which in turn influence the European markets. In summary, we find evidence for intra-continental relationships and an increase in importance of international spillover effects since the mid 1990s, while the importance of historical transmissions has decreased since the beginning of this century.

Chapter II. The World Economy

1987 ◽  
Vol 122 ◽  
pp. 24-40
Author(s):  
Simon Wren-Lewis ◽  
Fiona Eastwood
Keyword(s):  
Stock Market ◽  
Current Account ◽  
World Economy ◽  
Current Account Deficit ◽  
Stock Market Crash ◽  
The Us ◽  
The World ◽  
Global Stock

To a large extent the seeds of the global stock market crash in October lie in the Louvre accord established at the beginning of this year. As we argue below, this agreement was fatally flawed in attempting to fix the dollar at too high a level. This error was initially both masked and aggravated by the extensive use of official intervention as the means of supporting the dollar. By the end of the summer it is estimated that up to two thirds of the US current account deficit was ‘covered’ by official intervention.

scholarly journals Spillover Effect of US Monetary Policy to ASEAN Stock Market

2019 ◽  
Vol 15 (2) ◽  
pp. 232-242
Author(s):  
Aniek Hindrayani ◽  
Fadikia K Putri ◽  
Inda F Puspitasari
Keyword(s):  
Monetary Policy ◽  
Stock Market ◽  
Markov Switching ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Bear Market ◽  
Bull Market ◽  
The Us ◽  
Recession Period ◽  
Us Monetary Policy

Abstract: This study analyzes the spillover effects of the US monetary policy on the ASEAN stock market with Markov switching model and investigates differences in empirical results of each country from ASEAN member. The results of this study have important implications for asset price allocation, specifically in the case of a transition between US and other small countries. The results showed that the ASEAN stock market is more affected by the US interest rates during bull-market than bear-markets. This can be seen from the increasing of stock market volatility during expansion comparing with recession period. Therefore, the stock markets of ASEAN countries will not be easily affected by the dollar rate during financial crisis or the recession period. Keywords: stock market, monetary policy, spillover effect, Markov-switching modelEfek Spillover pada Perubahan Kebijakan Moneter Amerika Terhadap Stock Market di ASEANAbstrak: Penelitian ini menganalisis efek spillover akibat adanya perubahan kebijakan moneter Amerika terhadap stock market di ASEAN dengan model Markov switching dan menginvestigasi terkait ada atau tidaknya perbedaan pada hasil empiris di setiap negara anggota ASEAN. Hasil penelitian ini memberikan implikasi penting bagi mekanisme transisi harga aset, khususnya dari Amerika terhadap negara dengan skala perekonomian kecil. Hasil penelitian menunjukkan bahwa stock market ASEAN lebih mudah terpengaruh oleh tingkat suku bunga Amerika pada saat kondisi bull-market dibandingkan saat bear-market. Hal ini dapat dilihat dari tingginya volatilitas stock market pada saat ekspansi dibandingkan saat periode resesi, sehingga stock market negara-negara ASEAN tidak akan mudah terpengaruh oleh dollar pada saat perekonomian mengalami krisis atau saat periode resesi. Kata kunci: stock market, kebijakan moneter, spillover effect, model Markov-switching

scholarly journals Application of the Value Averaging Investment Method on the US Stock Market

2015 ◽  
Vol 63 (6) ◽  
pp. 2151-2160
Author(s):  
Martin Širůček ◽  
Ivana Škatuĺárová
Keyword(s):  
Stock Market ◽  
Real Data ◽  
Investment Horizon ◽  
Medium Term ◽  
Short Term ◽  
Specific Investment ◽  
The Us ◽  
Expansion Period ◽  
Annual Risk

The paper focuses on empirical testing and the use of the regular investment, particularly on the value averaging investment method on real data from the US stock market in the years 1990–2013. The 23-year period was chosen because of a consistently interesting situation in the market and so this regular investment method could be tested to see how it works in a bull (expansion) period and a bear (recession) period. The analysis is focused on results obtained by using this investment method from the viewpoint of return and risk on selected investment horizons (short-term 1 year, medium-term 5 years and long-term 10 years). The selected aim is reached by using the ratio between profit and risk. The revenue-risk profile is the ratio of the average annual profit rate measured for each investment by the internal rate of return and average annual risk expressed by selective standard deviation. The obtained results show that regular investment is suitable for a long investment horizon or the longer the investment horizon, the better the revenue-risk ratio (Sharpe ratio). According to the results obtained, specific investment recommendations are presented in the conclusion, e.g. if this investment method is suitable for a long investment period, if it is better to use value averaging for a growing, sinking or sluggish market, etc.

scholarly journals Conditional Time-Varying General Dynamic Factor Models and Its Application to the Measurement of Volatility Spillovers across Russian Assets

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2484
Author(s):  
Vladimir Balash ◽  
Alexey Faizliev ◽  
Sergei Sidorov ◽  
Elena Chistopolskaya
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
Political Crisis ◽  
Dynamic Factor ◽  
Short Term ◽  
Volatility Spillovers ◽  
Conditional Time ◽  
Russian Stock Market ◽  
The Impact

This study analyzes the spillover effects of volatility in the Russian stock market. The paper applies the Diebold–Yilmaz connectedness methodology to characterize volatility spillovers between Russian assets. The spectral representation of the forecast variance decomposition proposed by Baruník and Křehlik is used to describe the connectivity in short-term (up to 5 days), medium-term (6–20 days) and long-term (more than 20 days) time frequencies. Additionally, two new augmented models are developed and applied to evaluate conditional spillover effects in different sectors of the Russian economy for the period from January 2012 to June 2021. It is shown that spillover effects increase significantly during political and economic crises and decrease during periods of relative stability. The rising of the overall level of spillovers in the Russian stock market coincides in time with the political crisis of 2014, the intensification of anti-Russian sanctions in 2018 and the fall in oil prices and the start of the pandemic in 2020. With the consideration of the augmented models it can be argued that a significant part of the long-term spillover effects on the Russian stock market may be caused by the influence of external economic and political factors. However, volatility spillovers generated by internal Russian idiosyncratic shocks are short-term. Thus, the proposed approach provides new information on the impact of external factors on volatility spillovers in the Russian stock market.

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