Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model

Abstract

A recent study by Rapach, Strauss, and Zhou (Journal of Finance, 2013, 68(4), 1633–1662) shows that US stock returns can provide predictive content for international stock returns. We extend their work from a volatility perspective. We propose a model, namely a heterogeneous volatility spillover–generalized autoregressive conditional heteroskedasticity model, to investigate volatility spillover. The model specification is parsimonious and can be used to analyze the time variation property of the spillover effect. Our in‐sample evidence shows the existence of strong volatility spillover from the US to five major stock markets and indicates that the spillover was stronger during business cycle recessions in the USA. Out‐of‐sample results show that accounting for spillover information from the USA can significantly improve the forecasting accuracy of international stock price volatility.

The effects of volatility and changes in conditional correlations in the stock markets of Russia and developed countries

“…Various interdependencies are revealed. Abdelkefi (2015) demonstrates the existence of unilateral and bilateral relations between the US stock market and other developed markets; Panda and Nanda (2018) establish that emerging markets are less related to developed market in terms of profitability; Kutlar and Torun (2014) show that while the markets of developed countries show a strong spread of volatility, in developed countries there is a weak spread of volatility to developing countries; Seth and Singhania (2019) show that selective border markets are intertwined with developed markets; Guesmi et al (2014) show that most European stock markets are closely related to the US market; Ahmed et al (2018) use correlation analysis to show a significant positive correlation between developed markets but a relatively insignificant correlation between developing and developed markets; Wang et al(2018) highlight the presence of a strong VOLATILITY AND CORRELATIONS BETWEEN STOCK MARKETS spread of volatility from the USA to five major stock markets; Serletis and Azad (2018) reveal statistically significant secondary effects of volatility from emerging economies on the United States; Hung (2019) demonstrates that the correlation between Central European markets is especially significant; and Mitra et al (2015) find that the transfer of volatility between stock markets is predictable because they follow a certain pattern, and therefore they were modelled using appropriate theoretical distributions. The above articles establish that the process of the spread of volatility affects the flow of financial assets between countries and has led to significant changes in terms of stock market returns, the volume of transactions, and market value.…”

“…The interactions of the US market are of particular interest because, on the one hand, previous studies have shown that the USA is the main driver of Asian and European markets (Al-Zeaud & Alshbiel, 2012) and is also responsible for the transfer of volatility; and on the other hand there is evidence of much less interdependence between the US market and developing countries, including the Russian market (Panda & Nanda, 2018;Wang et al, 2018). Changes in economic policies in recent years have led to changes in the flow and value of commodities and finances, and thus in investors' decisions.…”

Study on Globalization of Shipping Stocks Pricing Based on a DC-MSV Model

“…The study measures the linkage level of stock markets at home and abroad from the initial static correlation and risk spillover effects [1], turning to dynamic correlation to evaluate the evolution of linkage relationship [2], then analyzes the impact of major investment open policy on the sudden change of linkage structure [3], especially the evolution of the linkage between Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect [4], to assess the effectiveness of the policies. But the evolution of the linkage is also affected by multiple factors, for example, global economic crisis [5], A-share stock disaster [6], changes of interest rates and exchange rates [7] etc. These factors affect the global stock market's rise and fall through profit, interest rate, risk appetite and so on.…”

“…In addition, in order to better fit the linkage relationship, the study shifts from linear to nonlinear [3]; risk spillover effect research shifts from symmetric spillover to asymmetric spillover [8]. In the research object, the main focus is on the linkage between international stock markets [5], the linkage between domestic and overseas stock markets [1], especially the linkage between A-shares and Hong Kong stocks. [7] [9] [10]; the research target shifts from the general index to the industry [9].…”

Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic

“…Volatility forecasts play an important role in risk management, asset pricing, and portfolios, and methods of improving the accuracy of volatility prediction represent an important and difficult issue (see, e.g., Ji and Guo, 2015 ; Rossi and Fantazzini, 2015 ; Wang et al, 2016 ; Degiannakis and Filis, 2017 ; Ma et al, 2017 ; Wang et al, 2018a ; Ma et al, 2019 ; Zhang et al, 2019b ; Bai et al, 2020 ; Liang et al, 2020a ; Liang et al, 2020b ; Wei et al, 2020 ; Zhang et al, 2020 ; Liang et al, 2021 ). In addition, numerous studies have examined the factors that affect oil prices (see, e.g., Elder et al, 2013 ; Zhang and Cao, 2013 ; Sévi, 2014 ; Wang et al, 2016 ; Zhang, 2017 ; Jing et al, 2018 ; Wang et al, 2018b ; Zhang et al, 2019a ).…”