Government Bonds and Stock Market : Volatility Spillover Effect

2021 ◽  
Vol 8 (1-2) ◽  
pp. 61
Author(s):  
Muhammadriyaj Faniband ◽  
Taranum Faniband
Keyword(s):  
Stock Market ◽  
Spillover Effect ◽  
Market Volatility ◽  
Volatility Spillover ◽  
Stock Market Volatility ◽  
Government Bonds ◽  
Volatility Spillover Effect

Spillover effects between energies, gold, and stock: the United States versus China

2020 ◽  
Vol 31 (8) ◽  
pp. 1416-1447 ◽  
Author(s):  
Xie He ◽  
Tetsuya Takiguchi ◽  
Tadahiro Nakajima ◽  
Shigeyuki Hamori
Keyword(s):  
United States ◽  
Stock Market ◽  
Crude Oil ◽  
Spillover Effect ◽  
The United States ◽  
Commodity Markets ◽  
Volatility Spillover ◽  
Chinese Stock Market ◽  
The Us ◽  
Volatility Spillover Effect

This study investigates the time–frequency dynamics of return and volatility spillovers between the stock market and three commodity markets: natural gas, crude oil, and gold via a comparative analysis between the United States and China is conducted with the help of new empirical methods. Our findings are as follows. First, in terms of time, return spillovers between crude oil and the stock market are strongest in two of the three commodity markets. Crude oil emits a net negative return spillover to the US stock market, and a net positive return spillover to the Chinese stock market. By contrast, the strongest volatility spillover effect is transmitted to the stock markets of both countries through gold. However, gold has a net positive volatility spillover effect on the US stock market and a net negative effect on the Chinese stock market. In the frequency domain, most of the return spillover is produced in the short term, and most of the volatility spillover occurs in the long term. In addition, the moving-window method reveals the dynamic nature of the spillover effect. Some extreme events can have a dramatic effect on the spillover index. Conversely, the spillover effect differs significantly between the two countries and is characterized by time variation and frequency dependence.

scholarly journals Dynamic Cross-Market Volatility Spillover Based on MSV Model: Evidence from Bitcoin, Gold, Crude Oil, and Stock Markets

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Zhang ◽  
Qi-zhi He
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Granger Causality ◽  
Asset Allocation ◽  
Stock Markets ◽  
Spillover Effect ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Dynamic Correlation ◽  
Model Evidence

This paper examines the spillover effect between bitcoin, gold, crude oil, and major stock markets by using the MSV model with dynamic correlation and Granger causality. The empirical results of the DC-GC-MSV model are logically correct and convergent. The DIC test result has proved that the DC-GC-MSV model is better and more accurate. Bitcoin has no significant Granger causality spillover effect than other assets. As a safe haven product for stock assets, gold price has one-way spillover effect from stock market volatility. Moreover, crude oil has the highest correlation with the stock market. In the recent COVID-19 epidemic and the sluggish economic environment, investors need to consider a balanced asset allocation among low-correlation assets, medium-correlation assets, and high-correlation assets to reduce risks.

scholarly journals The Volatility Spillover Effect Between the International Crude Oil Futures Price and China’s Stock Market - Multivariate BEKK-GARCH Model Based on Wavelet Multiresolution

2019 ◽  
Vol 10 (4) ◽  
pp. 84
Author(s):  
Maoguo Wu ◽  
Zhehao Zhu
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Spillover Effect ◽  
Garch Model ◽  
Futures Market ◽  
Volatility Spillover ◽  
Oil Futures ◽  
Model Based ◽  
Crude Oil Futures ◽  
Volatility Spillover Effect

This study aims to analyze the volatility spillover effect between the international crude oil futures market and China’s stock market. Using West Texas Intermediate (WTI) and the Shanghai Composite Index (SSEC) to represent the international crude oil futures market and China’s stock market respectively, this study selects data of WTI and the SSEC from August 10, 2007 to August 10, 2017. It processes these data via wavelet multiresolution to decompose them into different levels and then builds the data model based on the BEKK-GARCH model. By testing the parameters through the Wald test, it further explores whether the volatility spillover effect exists between WTI and the SSEC. Empirical evidence finds that the volatility spillover effect between WTI and the SSEC is significant in the short run, while, however, such a volatility spillover effect does not exist in the medium and long term.

Research on volatility spillover effect between foreign exchange and stock market based on computer simulation multi resolution analysis

2021 ◽  
pp. 1-11
Author(s):  
Ping Zhang ◽  
Shiwei Nan Wang
Keyword(s):  
Computer Simulation ◽  
Exchange Rate ◽  
Stock Market ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Spillover Effect ◽  
Volatility Spillover ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Volatility Spillover Effect

In order to analyze the volatility spillover effect between foreign exchange and stock market, this paper adopts the wavelet multi-resolution analysis method of computer simulation. Firstly, aiming at the problem of high and low frequency oscillation and exchange rate de-noising, we adopts the generalized autoregressive conditional heteroskedasticity (GARCH) model to carry out the oscillation correction and exponential modification of the exchange rate denoising signal based on wavelet multi-resolution, and carries out the corresponding decomposition and fitting combined with the wavelet multi-resolution of the state transition GARCH. Then, through the computer simulation of the modified wavelet multi-resolution analysis, this paper studies the volatility spillover effect between the foreign exchange market and the stock market from different scales, so as to explore the simultaneous research from the time domain and frequency domain. The empirical results show that the low-frequency signals of RMB exchange rate volatility (RMB-ERV) and stock price volatility (SPV) have co-integration relationship. It is unique in that the volatility spillover effect in different trading cycles is inconsistent: in the short term, it is mainly manifested in the volatility spillover from the stock market (VS-SM) to the foreign exchange market (VS-FEM); and with the extension of the trading cycle, it shows both sides of effects on the VS.

Estimating Emerging Stock Market Volatility Using Garch Family Models

2011 ◽  
Vol 3 (9) ◽  
pp. 331-333 ◽  
Author(s):  
Ramona Birău ◽  
◽  
Jatin Trivedi
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Family Models
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