The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH-MIDAS approach

2017 ◽  
Vol 38 (3) ◽  
pp. 413-422 ◽  
Author(s):  
Libing Fang ◽  
Baizhu Chen ◽  
Honghai Yu ◽  
Yichuo Qian
Keyword(s):  
Economic Policy ◽  
Futures Market ◽  
Market Volatility ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Gold Futures

scholarly journals Macroeconomic Uncertainty and Crude Oil Futures Volatility–Evidence from China Crude Oil Futures Market

2021 ◽  
Vol 9 ◽  
Author(s):  
Adan Yi ◽  
Menglong Yang ◽  
Yongshan Li
Keyword(s):  
Economic Policy ◽  
Crude Oil ◽  
Futures Market ◽  
Market Volatility ◽  
Policy Uncertainty ◽  
Oil Futures ◽  
Uncertainty Factors ◽  
Economic Policy Uncertainty ◽  
Macroeconomic Uncertainty ◽  
Crude Oil Futures

This paper investigates whether the macroeconomic uncertainty factors can explain and forecast China’s INE crude oil futures market volatility. We use the GARCH-MIDAS model to investigate the explaining and predicting power of the macroeconomic uncertainties. We considered various geopolitical risk (GPR) indices, economic policy uncertainty (EPU) indices, and infectious disease pandemic (IDEMV) indices in our model. The empirical results suggest that the geopolitical risk, the geopolitical act risk, the global economic policy uncertainty, the economic policy uncertainty from the United Kingdom, and the economic policy uncertainty from Japan comprehensively integrate the information contained in the rest factors, and have superior predictive powers for INE crude oil future volatility. These findings highlight the importance of the impact of macroeconomic uncertainty factors has on the crude oil futures market, and indicate that the macroeconomic uncertainties need to be considered when explaining and forecasting crude oil futures market volatility.

scholarly journals COVID-19, Oil Price, Bitcoin, and US Economic Policy Uncertainty: Evidence from ARDL Model

2021 ◽  
Vol 13 (11) ◽  
pp. 88
Author(s):  
Hanan Naser
Keyword(s):  
Economic Policy ◽  
Financial Market ◽  
Global Economy ◽  
Positive Impact ◽  
Oil Price ◽  
Market Volatility ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
The Impact

The pandemic of coronavirus (COVID-19) creates fear and uncertainty causing extraordinary disruption to financial markets and global economy. Witnessing the fastest selloff in the American stock market in history with a plunge of more than 28% in S&P 500 has increased the volatility of global financial market to exceed the level observed during the financial crisis of 2008. On the other hand, Bitcoin value has shown considerable stability in the last couple of months peaking at $10,367.53 in the mid of February 2020. In this context, the aim of this paper is to investigate the impact of COVID-19 numbers on Bitcoin price taking into consideration number of controlling variables including WTI-oil price, S&P 500 index, financial market volatility, gold prices, and economic policy uncertainty of the US. To do so, ARDL estimation has been applied using daily data from December 31, 2019 till May 20, 2020. Key findings reveal that the daily reported cases of new infections have a marginal positive impact on Bitcoin price in the long term. However, the indirect impact associated with the fear of COVID-19 pandemic via financial market stress cannot be neglected. Bitcoin can also serve as a hedging tool against the economic policy uncertainty in the long term. In the short run, while the returns of economic policy uncertainty have no impact on Bitcoin price, the growth in the new cases of COVID-19 infection and returns of financial market volatility have more positive significant impact on Bitcoin returns.

scholarly journals Economic Policy Uncertainty and Chinese Stock Market Volatility: A CARR-MIDAS Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xinyu Wu ◽  
Tianyu Liu ◽  
Haibin Xie
Keyword(s):  
Economic Policy ◽  
Stock Market ◽  
Market Volatility ◽  
Policy Uncertainty ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Model Framework ◽  
Economic Policy Uncertainty ◽  
Volatility Model ◽  
The Impact

Intraday range (the difference between intraday high and low prices) is often used to measure volatility, which has proven to be a more efficient volatility estimator than the return-based one. Meanwhile, a growing body of studies has found that economic policy uncertainty (EPU) has important impact on stock market volatility. In this paper, building on the range-based volatility model, namely, the conditional autoregressive range (CARR) model, we introduce the CARR-mixed-data sampling (CARR-MIDAS) model framework by considering intraday information to investigate the impact of EPU on the volatility of Chinese stock market and to explore the predictive ability of EPU for Chinese stock market. The empirical results show that both the China EPU (CEPU) and global EPU (GEPU) have a significantly negative effect on the long-run volatility of Chinese stock market. Furthermore, we find that taking into account the CEPU and GEPU leads to substantial improvement in the ability to forecast the volatility of Chinese stock market. We also find that the CEPU provides superior volatility forecasts compared to the GEPU. Our findings are robust to different forecasting windows.

scholarly journals A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

2020 ◽  
Vol 13 (2) ◽  
pp. 33 ◽  
Author(s):  
Theodore Panagiotidis ◽  
Thanasis Stengos ◽  
Orestis Vravosinos
Keyword(s):  
Economic Policy ◽  
Strong Evidence ◽  
Principal Component ◽  
Market Volatility ◽  
Policy Uncertainty ◽  
Stock Market Volatility ◽  
Sparse Regression ◽  
Economic Policy Uncertainty ◽  
Regression Approach

We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017–2018 period.

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