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.

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.

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