| Journal Article |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| Abnormal temperature and the cross-section of stock returns in China |
0 |
0 |
2 |
3 |
1 |
3 |
10 |
15 |
| Are low-frequency data really uninformative? A forecasting combination perspective |
0 |
0 |
0 |
37 |
3 |
6 |
10 |
312 |
| Attention to climate events and carbon price volatility |
0 |
1 |
1 |
1 |
0 |
2 |
2 |
2 |
| Climate policy uncertainty and the stock return predictability of the oil industry |
0 |
2 |
3 |
18 |
3 |
8 |
14 |
54 |
| Climate risk exposure and the cross-section of Chinese stock returns |
0 |
1 |
2 |
6 |
1 |
5 |
9 |
24 |
| Default return spread: A powerful predictor of crude oil price returns |
0 |
1 |
5 |
6 |
0 |
1 |
6 |
10 |
| Detection of fraud statement based on word vector: Evidence from financial companies in China |
1 |
2 |
7 |
37 |
3 |
8 |
28 |
105 |
| Does US Economic Policy Uncertainty matter for European stock markets volatility? |
0 |
0 |
0 |
20 |
2 |
4 |
6 |
74 |
| Does default point vary with firm size? |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
18 |
| Dynamic asymmetric impact of equity market uncertainty on energy markets: A time-varying causality analysis |
0 |
0 |
2 |
3 |
1 |
3 |
5 |
15 |
| Economic constraints and stock return predictability: A new approach |
0 |
0 |
1 |
17 |
1 |
2 |
4 |
61 |
| Economic policy uncertainty and the Chinese stock market volatility: Novel evidence |
0 |
2 |
4 |
40 |
2 |
9 |
24 |
151 |
| Economic policy uncertainty and the Chinese stock market volatility: new evidence |
0 |
0 |
1 |
19 |
0 |
2 |
8 |
51 |
| Financial regulatory policy uncertainty: An informative predictor for financial industry stock returns |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
3 |
| Forecasting Bitcoin volatility: A new insight from the threshold regression model |
0 |
0 |
2 |
17 |
0 |
1 |
7 |
36 |
| Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities? |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
11 |
| Forecasting Chinese Stock Market Volatility With Volatilities in Bond Markets |
0 |
1 |
2 |
2 |
1 |
2 |
14 |
14 |
| Forecasting Realized Volatility: The Choice of Window Size |
1 |
2 |
4 |
4 |
3 |
10 |
15 |
15 |
| Forecasting US stock market volatility: How to use international volatility information |
0 |
0 |
1 |
6 |
0 |
0 |
2 |
26 |
| Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index |
0 |
0 |
1 |
4 |
1 |
1 |
6 |
12 |
| Forecasting carbon prices under diversified attention: A dynamic model averaging approach with common factors |
0 |
0 |
1 |
1 |
0 |
0 |
6 |
7 |
| Forecasting crude oil futures market returns: A principal component analysis combination approach |
0 |
1 |
5 |
8 |
1 |
5 |
15 |
33 |
| Forecasting crude oil market returns: Enhanced moving average technical indicators |
1 |
1 |
4 |
13 |
1 |
6 |
11 |
27 |
| Forecasting crude oil market volatility using variable selection and common factor |
0 |
0 |
1 |
12 |
1 |
5 |
9 |
31 |
| Forecasting crude oil market volatility: A comprehensive look at uncertainty variables |
0 |
0 |
3 |
6 |
0 |
1 |
6 |
10 |
| Forecasting crude oil market volatility: A newspaper-based predictor regarding petroleum market volatility |
0 |
1 |
1 |
4 |
1 |
2 |
3 |
20 |
| Forecasting crude oil price returns: Can nonlinearity help? |
0 |
0 |
0 |
1 |
1 |
2 |
4 |
7 |
| Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors? |
1 |
2 |
9 |
72 |
3 |
4 |
29 |
214 |
| Forecasting crude oil prices with global ocean temperatures |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
1 |
| Forecasting crude oil prices: A reduced-rank approach |
0 |
0 |
0 |
1 |
1 |
1 |
5 |
11 |
| Forecasting crude oil prices: A scaled PCA approach |
0 |
1 |
9 |
67 |
0 |
2 |
17 |
184 |
| Forecasting crude oil prices: do technical indicators need economic constraints? |
1 |
1 |
1 |
6 |
2 |
3 |
4 |
16 |
| Forecasting crude oil returns with oil-related industry ESG indices |
0 |
1 |
1 |
1 |
0 |
4 |
5 |
5 |
| Forecasting global equity market volatilities |
0 |
0 |
0 |
20 |
0 |
0 |
2 |
57 |
| Forecasting international equity market volatility: A new approach |
0 |
2 |
2 |
7 |
0 |
2 |
3 |
18 |
| Forecasting oil futures price volatility: New evidence from realized range-based volatility |
0 |
0 |
0 |
17 |
1 |
4 |
7 |
83 |
| Forecasting oil price volatility: Forecast combination versus shrinkage method |
0 |
0 |
0 |
20 |
1 |
3 |
6 |
88 |
| Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach |
0 |
0 |
0 |
4 |
0 |
1 |
6 |
25 |
| Forecasting stock market realized volatility: the role of global terrorist attacks |
0 |
0 |
1 |
3 |
0 |
0 |
2 |
7 |
| Forecasting stock market returns with a lottery index: Evidence from China |
0 |
0 |
2 |
2 |
1 |
1 |
6 |
6 |
| Forecasting stock market volatility: The sum of the parts is more than the whole |
0 |
0 |
5 |
17 |
0 |
0 |
10 |
38 |
| Forecasting stock return volatility using a robust regression model |
0 |
0 |
4 |
25 |
3 |
4 |
12 |
76 |
| Forecasting stock returns with cycle-decomposed predictors |
0 |
0 |
2 |
14 |
2 |
4 |
7 |
58 |
| Forecasting stock returns with industry volatility concentration |
1 |
2 |
12 |
12 |
4 |
11 |
34 |
34 |
| Forecasting stock returns: Do less powerful predictors help? |
0 |
0 |
0 |
12 |
0 |
1 |
2 |
65 |
| Forecasting the Chinese Stock Market Volatility with G7 Stock Market Volatilities: A Scaled PCA Approach |
0 |
0 |
1 |
5 |
1 |
1 |
3 |
13 |
| Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching |
0 |
0 |
1 |
12 |
1 |
2 |
4 |
39 |
| Forecasting the Chinese stock market volatility: A regression approach with a t-distributed error |
1 |
1 |
1 |
6 |
2 |
2 |
5 |
16 |
| Forecasting the Chinese stock volatility across global stock markets |
0 |
0 |
0 |
8 |
1 |
2 |
4 |
37 |
| Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets |
0 |
0 |
0 |
10 |
0 |
0 |
3 |
177 |
| Forecasting the aggregate oil price volatility in a data-rich environment |
0 |
0 |
1 |
21 |
2 |
3 |
6 |
96 |
| Forecasting the aggregate stock market volatility in a data-rich world |
0 |
0 |
2 |
10 |
0 |
0 |
2 |
26 |
| Forecasting the equity premium using weighted regressions: Does the jump variation help? |
0 |
0 |
0 |
0 |
3 |
5 |
9 |
9 |
| Forecasting the oil futures price volatility: Large jumps and small jumps |
0 |
0 |
0 |
12 |
2 |
4 |
5 |
79 |
| Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model |
0 |
0 |
1 |
2 |
2 |
2 |
6 |
16 |
| Forecasting the prices of crude oil using the predictor, economic and combined constraints |
0 |
0 |
1 |
2 |
0 |
0 |
3 |
38 |
| Forecasting the prices of crude oil: An iterated combination approach |
0 |
0 |
0 |
24 |
5 |
7 |
10 |
136 |
| Forecasting the volatility of Chinese stock market: An international volatility index |
0 |
0 |
1 |
4 |
0 |
2 |
8 |
36 |
| Forecasting the volatility of the German stock market: New evidence |
0 |
1 |
2 |
10 |
0 |
4 |
10 |
36 |
| Geopolitical risk and oil volatility: A new insight |
3 |
5 |
7 |
48 |
5 |
12 |
29 |
182 |
| Geopolitical risk and stock market volatility: A global perspective |
6 |
20 |
72 |
125 |
22 |
56 |
222 |
372 |
| Geopolitical risk exposure and stock returns: Evidence from China |
0 |
3 |
15 |
20 |
4 |
11 |
44 |
53 |
| Geopolitical risk trends and crude oil price predictability |
0 |
0 |
3 |
31 |
1 |
5 |
15 |
87 |
| Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility |
0 |
0 |
1 |
6 |
0 |
0 |
2 |
15 |
| Good variance, bad variance, and stock return predictability |
0 |
0 |
4 |
17 |
1 |
2 |
11 |
45 |
| Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets |
0 |
0 |
0 |
20 |
0 |
0 |
2 |
129 |
| Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks |
0 |
0 |
2 |
14 |
1 |
1 |
6 |
52 |
| Hedging pressure momentum and the predictability of oil futures returns |
0 |
0 |
4 |
14 |
0 |
3 |
9 |
31 |
| How macro-variables drive crude oil volatility? Perspective from the STL-based iterated combination method |
0 |
0 |
0 |
1 |
2 |
2 |
3 |
6 |
| Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value |
0 |
0 |
1 |
9 |
1 |
2 |
4 |
28 |
| Industry volatility concentration and the predictability of aggregate stock market volatility |
0 |
1 |
5 |
7 |
4 |
9 |
19 |
22 |
| Industry volatility spillover and aggregate stock returns |
1 |
3 |
3 |
4 |
1 |
4 |
11 |
13 |
| Information connectedness of international crude oil futures: Evidence from SC, WTI, and Brent |
0 |
0 |
0 |
3 |
1 |
2 |
4 |
26 |
| Information transmission between gold and financial assets: Mean, volatility, or risk spillovers? |
0 |
0 |
0 |
6 |
1 |
1 |
1 |
27 |
| Interest rate level and stock return predictability |
0 |
0 |
1 |
5 |
1 |
1 |
3 |
19 |
| Intraday momentum and stock return predictability: Evidence from China |
0 |
0 |
13 |
91 |
6 |
12 |
36 |
341 |
| Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism |
0 |
0 |
1 |
7 |
0 |
0 |
2 |
33 |
| Is implied volatility more informative for forecasting realized volatility: An international perspective |
0 |
1 |
1 |
12 |
1 |
2 |
11 |
52 |
| Market Skewness and Stock Return Predictability: New Evidence from China |
0 |
0 |
2 |
6 |
1 |
1 |
9 |
15 |
| Model specification for volatility forecasting benchmark |
0 |
0 |
1 |
1 |
1 |
1 |
10 |
10 |
| Modelling and forecasting crude oil price volatility with climate policy uncertainty |
0 |
0 |
0 |
0 |
3 |
6 |
9 |
10 |
| New evidence of extreme risk transmission between financial stress and international crude oil markets |
0 |
2 |
2 |
4 |
0 |
2 |
3 |
12 |
| Not all geopolitical shocks are alike: Identifying price dynamics in the crude oil market under tensions |
0 |
0 |
1 |
4 |
0 |
0 |
10 |
25 |
| Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help? |
0 |
0 |
1 |
4 |
1 |
3 |
10 |
20 |
| Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches |
0 |
0 |
0 |
5 |
0 |
0 |
2 |
29 |
| Out‐of‐sample volatility prediction: A new mixed‐frequency approach |
0 |
0 |
1 |
9 |
1 |
1 |
4 |
35 |
| Out‐of‐sample volatility prediction: Rolling window, expanding window, or both? |
2 |
3 |
10 |
17 |
5 |
9 |
23 |
36 |
| Policy uncertainty, investor sentiment, and good and bad volatilities in the stock market: Evidence from China |
1 |
3 |
5 |
11 |
6 |
11 |
30 |
40 |
| Predicting cryptocurrency returns for real-world investments: A daily updated and accessible predictor |
1 |
4 |
5 |
9 |
3 |
8 |
12 |
16 |
| Predicting stock realized variance based on an asymmetric robust regression approach |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
7 |
| Realized skewness and the short-term predictability for aggregate stock market volatility |
0 |
1 |
7 |
34 |
0 |
2 |
16 |
73 |
| Systematic risk and deposit insurance pricing |
0 |
0 |
0 |
14 |
1 |
2 |
3 |
54 |
| The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns |
0 |
0 |
0 |
1 |
0 |
3 |
6 |
11 |
| The predictability of iron ore futures prices: A product‐material lead–lag effect |
0 |
0 |
6 |
16 |
1 |
2 |
10 |
28 |
| The pricing of loan insurance based on the Gram-Charlier option model |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
12 |
| To jump or not to jump: momentum of jumps in crude oil price volatility prediction |
0 |
0 |
0 |
4 |
0 |
0 |
2 |
20 |
| Volatility forecasting: long memory, regime switching and heteroscedasticity |
0 |
0 |
1 |
10 |
0 |
2 |
5 |
32 |
| Which predictor is more predictive for Bitcoin volatility? And why? |
0 |
0 |
0 |
8 |
0 |
0 |
3 |
22 |
| Total Journal Articles |
21 |
72 |
288 |
1,319 |
140 |
348 |
1,071 |
4,924 |
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