| Journal Article |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A copula–multifractal volatility hedging model for CSI 300 index futures |
0 |
0 |
0 |
11 |
1 |
1 |
1 |
96 |
| A nonparametric approach to test for predictability |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
34 |
| Abnormal temperature and the cross-section of stock returns in China |
1 |
1 |
2 |
3 |
2 |
3 |
9 |
12 |
| Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis |
0 |
4 |
6 |
84 |
1 |
5 |
8 |
303 |
| Analysis of efficiency for Shenzhen stock market: Evidence from the source of multifractality |
0 |
0 |
0 |
22 |
1 |
1 |
3 |
155 |
| Analysis of market efficiency for the Shanghai stock market over time |
0 |
0 |
0 |
25 |
1 |
2 |
4 |
115 |
| Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis |
0 |
0 |
1 |
23 |
0 |
0 |
6 |
88 |
| Analysis of the efficiency of the Shanghai stock market: A volatility perspective |
0 |
0 |
0 |
9 |
0 |
1 |
2 |
55 |
| Are crude oil spot and futures prices cointegrated? Not always! |
0 |
0 |
0 |
40 |
0 |
0 |
1 |
172 |
| Asymmetric spillover of geopolitical risk and oil price volatility: A global perspective |
0 |
0 |
1 |
5 |
2 |
5 |
7 |
16 |
| Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
49 |
| Can GARCH-class models capture long memory in WTI crude oil markets? |
0 |
1 |
1 |
58 |
0 |
1 |
3 |
213 |
| Can commodity prices forecast exchange rates? |
2 |
4 |
5 |
34 |
2 |
5 |
7 |
111 |
| Climate risk exposure and the cross-section of Chinese stock returns |
0 |
0 |
1 |
4 |
0 |
1 |
5 |
16 |
| Cloud cover and expected oil returns |
0 |
0 |
2 |
2 |
0 |
1 |
7 |
8 |
| Commodity price changes and the predictability of economic policy uncertainty |
0 |
0 |
0 |
49 |
0 |
0 |
2 |
193 |
| Cross-correlations between Chinese A-share and B-share markets |
0 |
1 |
2 |
24 |
0 |
1 |
3 |
94 |
| Cross-correlations between spot and futures markets of nonferrous metals |
0 |
0 |
0 |
12 |
0 |
0 |
1 |
46 |
| Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging |
1 |
1 |
2 |
25 |
1 |
1 |
8 |
132 |
| Crude oil futures and the short-term price predictability of petroleum products |
0 |
1 |
5 |
5 |
1 |
4 |
12 |
12 |
| Detrended fluctuation analysis on spot and futures markets of West Texas Intermediate crude oil |
0 |
0 |
0 |
12 |
0 |
0 |
2 |
88 |
| Disentangling the determinants of real oil prices |
0 |
0 |
0 |
26 |
1 |
3 |
5 |
110 |
| Dynamic portfolio allocation with time-varying jump risk |
0 |
1 |
1 |
24 |
0 |
1 |
2 |
76 |
| Economic-environmental equilibrium-based bi-level dispatch strategy towards integrated electricity and natural gas systems |
0 |
0 |
0 |
7 |
0 |
2 |
3 |
20 |
| Efficiency of Crude Oil Futures Markets: New Evidence from Multifractal Detrending Moving Average Analysis |
0 |
0 |
0 |
23 |
0 |
0 |
3 |
97 |
| Energy prices and exchange rates of the U.S. dollar: Further evidence from linear and nonlinear causality analysis |
0 |
0 |
2 |
97 |
1 |
2 |
8 |
306 |
| Exploiting the sentiments: A simple approach for improving cross hedging effectiveness |
0 |
0 |
2 |
2 |
0 |
0 |
5 |
5 |
| Extreme risk spillovers between crude oil prices and the U.S. exchange rate: Evidence from oil-exporting and oil-importing countries |
0 |
0 |
0 |
13 |
0 |
0 |
1 |
41 |
| Eye in outer space: satellite imageries of container ports can predict world stock returns |
0 |
2 |
8 |
25 |
4 |
13 |
38 |
83 |
| Forecasting Bitcoin volatility: A new insight from the threshold regression model |
0 |
0 |
2 |
17 |
1 |
2 |
6 |
35 |
| Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model |
2 |
3 |
3 |
34 |
2 |
3 |
8 |
194 |
| Forecasting US stock market volatility: How to use international volatility information |
0 |
0 |
1 |
6 |
1 |
1 |
2 |
26 |
| Forecasting aggregate market volatility: The role of good and bad uncertainties |
1 |
1 |
1 |
7 |
1 |
1 |
4 |
25 |
| Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index |
1 |
1 |
1 |
4 |
1 |
1 |
5 |
11 |
| Forecasting carbon prices under diversified attention: A dynamic model averaging approach with common factors |
0 |
0 |
1 |
1 |
0 |
3 |
6 |
7 |
| Forecasting commodity prices out-of-sample: Can technical indicators help? |
1 |
2 |
9 |
65 |
1 |
6 |
17 |
211 |
| Forecasting crude oil futures market returns: A principal component analysis combination approach |
0 |
2 |
4 |
7 |
0 |
4 |
14 |
28 |
| Forecasting crude oil market returns: Enhanced moving average technical indicators |
0 |
2 |
3 |
12 |
0 |
4 |
5 |
21 |
| Forecasting crude oil market volatility using variable selection and common factor |
0 |
0 |
1 |
12 |
1 |
2 |
5 |
26 |
| Forecasting crude oil market volatility: A Markov switching multifractal volatility approach |
1 |
1 |
4 |
49 |
1 |
2 |
9 |
222 |
| Forecasting crude oil market volatility: A comprehensive look at uncertainty variables |
0 |
1 |
6 |
6 |
0 |
2 |
9 |
9 |
| Forecasting crude oil market volatility: A newspaper-based predictor regarding petroleum market volatility |
0 |
0 |
0 |
3 |
0 |
1 |
2 |
18 |
| Forecasting crude oil market volatility: Further evidence using GARCH-class models |
0 |
0 |
6 |
198 |
1 |
1 |
9 |
568 |
| Forecasting crude oil price returns: Can nonlinearity help? |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
5 |
| Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors? |
0 |
2 |
11 |
70 |
2 |
15 |
32 |
209 |
| Forecasting crude oil prices: A reduced-rank approach |
0 |
0 |
0 |
1 |
1 |
1 |
6 |
10 |
| Forecasting crude oil prices: A scaled PCA approach |
0 |
2 |
11 |
66 |
0 |
3 |
24 |
182 |
| Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models? |
0 |
1 |
6 |
178 |
1 |
4 |
18 |
576 |
| Forecasting excess stock returns with crude oil market data |
0 |
0 |
0 |
33 |
0 |
0 |
2 |
145 |
| Forecasting oil futures returns with news |
0 |
0 |
1 |
1 |
0 |
2 |
10 |
13 |
| Forecasting realized volatility in a changing world: A dynamic model averaging approach |
1 |
2 |
6 |
61 |
2 |
5 |
16 |
215 |
| Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach |
0 |
0 |
0 |
4 |
0 |
0 |
7 |
24 |
| Forecasting stock market realized volatility: the role of global terrorist attacks |
0 |
0 |
1 |
3 |
0 |
0 |
2 |
7 |
| Forecasting stock market volatility: The sum of the parts is more than the whole |
0 |
0 |
10 |
17 |
0 |
1 |
19 |
38 |
| Forecasting stock returns: A predictor-constrained approach |
0 |
0 |
0 |
10 |
0 |
1 |
4 |
53 |
| Forecasting stock returns: A time-dependent weighted least squares approach |
1 |
4 |
13 |
49 |
2 |
7 |
22 |
142 |
| Forecasting the Chinese stock market volatility: A regression approach with a t-distributed error |
0 |
0 |
1 |
5 |
1 |
2 |
5 |
14 |
| Forecasting the equity premium using weighted regressions: Does the jump variation help? |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
4 |
| Forecasting the real prices of crude oil under economic and statistical constraints |
0 |
1 |
1 |
22 |
0 |
2 |
4 |
151 |
| Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models |
0 |
0 |
2 |
28 |
0 |
1 |
5 |
114 |
| Forecasting the real prices of crude oil using robust regression models with regularization constraints |
0 |
0 |
1 |
20 |
0 |
1 |
7 |
85 |
| Forecasting the real prices of crude oil: A robust weighted least squares approach |
0 |
0 |
0 |
7 |
0 |
0 |
3 |
20 |
| Forecasting the real prices of crude oil: What is the role of parameter instability? |
0 |
0 |
1 |
13 |
0 |
0 |
3 |
24 |
| Forecasting the stock risk premium: A new statistical constraint |
0 |
0 |
1 |
2 |
0 |
0 |
3 |
6 |
| Forecasting the volatility of crude oil basis: Univariate models versus multivariate models |
0 |
0 |
4 |
4 |
0 |
0 |
7 |
7 |
| Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint |
0 |
0 |
3 |
5 |
0 |
1 |
6 |
9 |
| Futures Hedging in CSI 300 Markets: A Comparison Between Minimum-Variance and Maximum-Utility Frameworks |
0 |
0 |
1 |
10 |
0 |
1 |
3 |
24 |
| Futures hedging in crude oil markets: A comparison between minimum-variance and minimum-risk frameworks |
0 |
0 |
1 |
7 |
0 |
0 |
5 |
46 |
| Geopolitical risk trends and crude oil price predictability |
1 |
1 |
7 |
31 |
2 |
3 |
16 |
80 |
| Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility |
0 |
0 |
2 |
6 |
0 |
0 |
5 |
14 |
| Good oil volatility, bad oil volatility, and stock return predictability |
0 |
0 |
1 |
6 |
0 |
1 |
9 |
27 |
| Good volatility, bad volatility, and time series return predictability |
0 |
0 |
3 |
10 |
0 |
0 |
4 |
18 |
| Hedging crude oil using refined product: A regime switching asymmetric DCC approach |
0 |
0 |
0 |
21 |
0 |
1 |
3 |
104 |
| Hedging pressure momentum and the predictability of oil futures returns |
1 |
2 |
6 |
14 |
1 |
3 |
10 |
28 |
| Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy? |
0 |
0 |
3 |
81 |
0 |
0 |
3 |
213 |
| Heterogeneous beliefs and aggregate market volatility revisited: New evidence from China |
0 |
1 |
1 |
6 |
0 |
1 |
2 |
19 |
| How does corporate investment react to oil prices changes? Evidence from China |
2 |
2 |
4 |
22 |
3 |
3 |
11 |
64 |
| Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model |
0 |
2 |
4 |
30 |
1 |
3 |
12 |
64 |
| Industry equi-correlation: A powerful predictor of stock returns |
0 |
0 |
0 |
24 |
0 |
0 |
4 |
65 |
| Information connectedness of international crude oil futures: Evidence from SC, WTI, and Brent |
0 |
0 |
0 |
3 |
0 |
1 |
2 |
24 |
| Information transmission between gold and financial assets: Mean, volatility, or risk spillovers? |
0 |
0 |
1 |
6 |
0 |
0 |
1 |
26 |
| Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism |
0 |
0 |
0 |
6 |
0 |
0 |
4 |
32 |
| Investor attention and oil market volatility: Does economic policy uncertainty matter? |
0 |
1 |
1 |
12 |
0 |
1 |
2 |
48 |
| Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis |
0 |
0 |
0 |
59 |
0 |
1 |
4 |
207 |
| It's not that important: The negligible effect of oil market uncertainty |
0 |
0 |
1 |
6 |
0 |
0 |
1 |
47 |
| Limited attention of individual investors and stock performance: Evidence from the ChiNext market |
0 |
0 |
1 |
19 |
0 |
0 |
2 |
73 |
| Long memory in energy futures markets: Further evidence |
0 |
0 |
0 |
15 |
0 |
0 |
1 |
95 |
| Macroeconomic fundamentals, jump dynamics and expected volatility |
0 |
0 |
0 |
5 |
0 |
0 |
3 |
23 |
| Macroeconomic uncertainty and expected shortfall (and value at risk): a new dynamic semiparametric model |
0 |
0 |
1 |
8 |
0 |
0 |
4 |
24 |
| Macroeconomic uncertainty, speculation, and energy futures returns: Evidence from a quantile regression |
0 |
3 |
3 |
14 |
0 |
3 |
8 |
28 |
| Managerial ability and idiosyncratic volatility |
0 |
0 |
1 |
5 |
0 |
0 |
1 |
24 |
| Modeling and forecasting stock return volatility using the HARGARCH model with VIX information |
0 |
1 |
6 |
6 |
0 |
2 |
14 |
14 |
| Modelling and forecasting crude oil price volatility with climate policy uncertainty |
0 |
0 |
0 |
0 |
2 |
3 |
4 |
4 |
| Momentum of return predictability |
0 |
1 |
2 |
54 |
0 |
2 |
3 |
216 |
| Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis |
0 |
0 |
0 |
11 |
4 |
4 |
7 |
87 |
| Multifractal characterization of energy stocks in China: A multifractal detrended fluctuation analysis |
0 |
0 |
0 |
10 |
0 |
0 |
2 |
45 |
| Multifractal detrended cross-correlations between crude oil market and Chinese ten sector stock markets |
0 |
0 |
0 |
5 |
1 |
1 |
3 |
37 |
| Multifractal detrending moving average analysis on the US Dollar exchange rates |
0 |
0 |
0 |
17 |
0 |
1 |
1 |
91 |
| Not all geopolitical shocks are alike: Identifying price dynamics in the crude oil market under tensions |
0 |
0 |
1 |
4 |
0 |
2 |
9 |
23 |
| Oil and the short-term predictability of stock return volatility |
0 |
0 |
2 |
30 |
0 |
1 |
4 |
128 |
| Oil implied volatility and expected stock returns along the worldwide supply chain |
0 |
0 |
0 |
4 |
0 |
0 |
5 |
17 |
| Oil information uncertainty and aggregate market returns: A natural experiment based on satellite data |
0 |
1 |
3 |
3 |
1 |
4 |
10 |
10 |
| Oil price increases and the predictability of equity premium |
0 |
1 |
5 |
30 |
1 |
2 |
11 |
117 |
| Oil price shocks and Chinese economy revisited: New evidence from SVAR model with sign restrictions |
2 |
2 |
5 |
45 |
3 |
7 |
14 |
146 |
| Oil price shocks and U.S. dollar exchange rates |
0 |
1 |
2 |
92 |
1 |
5 |
10 |
233 |
| Oil price shocks and agricultural commodity prices |
0 |
0 |
3 |
177 |
0 |
1 |
9 |
521 |
| Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries |
1 |
7 |
16 |
436 |
6 |
20 |
63 |
1,212 |
| Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model |
0 |
4 |
5 |
96 |
1 |
7 |
15 |
298 |
| Oil volatility risk and stock market volatility predictability: Evidence from G7 countries |
0 |
1 |
1 |
21 |
0 |
1 |
2 |
109 |
| Out‐of‐sample volatility prediction: Rolling window, expanding window, or both? |
2 |
3 |
8 |
13 |
3 |
8 |
17 |
25 |
| Portfolios with return and volatility prediction for the energy stock market |
0 |
1 |
2 |
10 |
0 |
1 |
5 |
23 |
| Predictability of crude oil prices: An investor perspective |
0 |
0 |
0 |
7 |
1 |
3 |
8 |
88 |
| Realized bipower variation, jump components, and option valuation |
0 |
0 |
0 |
9 |
0 |
0 |
1 |
31 |
| Realized skewness and the short-term predictability for aggregate stock market volatility |
0 |
2 |
11 |
33 |
1 |
6 |
19 |
69 |
| Revisiting the multifractality in stock returns and its modeling implications |
0 |
0 |
0 |
8 |
0 |
0 |
2 |
36 |
| Risk spillovers between oil and stock markets: A VAR for VaR analysis |
0 |
0 |
0 |
27 |
0 |
0 |
7 |
123 |
| Shrinking return forecasts |
0 |
0 |
1 |
3 |
0 |
1 |
4 |
9 |
| Solving the Forecast Combination Puzzle Using Double Shrinkages |
1 |
1 |
1 |
1 |
1 |
2 |
5 |
5 |
| The asymmetric effects of oil price changes on China’s exports: New evidence from a nonlinear autoregressive distributed lag model |
1 |
1 |
1 |
8 |
1 |
2 |
5 |
26 |
| The dynamic spillover between carbon and energy markets: New evidence |
0 |
0 |
5 |
31 |
1 |
3 |
15 |
103 |
| The effects of oil shocks on export duration of China |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
38 |
| The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns |
0 |
0 |
0 |
1 |
1 |
2 |
5 |
7 |
| The predictability of iron ore futures prices: A product‐material lead–lag effect |
0 |
2 |
8 |
16 |
0 |
3 |
15 |
26 |
| The predictive effect of risk aversion on oil returns under different market conditions |
0 |
0 |
0 |
0 |
1 |
1 |
4 |
4 |
| The relationships between petroleum and stock returns: An asymmetric dynamic equi-correlation approach |
0 |
0 |
1 |
5 |
0 |
0 |
4 |
60 |
| Time‐Varying Parameter Realized Volatility Models |
0 |
0 |
0 |
15 |
0 |
1 |
2 |
118 |
| To jump or not to jump: momentum of jumps in crude oil price volatility prediction |
0 |
0 |
0 |
4 |
0 |
0 |
3 |
19 |
| Uncertainty and the predictability of stock returns |
0 |
0 |
2 |
7 |
0 |
0 |
6 |
17 |
| Understanding the multifractality in portfolio excess returns |
0 |
0 |
2 |
9 |
1 |
2 |
6 |
33 |
| Volatility linkages between stock and commodity markets revisited: Industry perspective and portfolio implications |
0 |
0 |
2 |
10 |
0 |
0 |
6 |
24 |
| Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model |
0 |
0 |
1 |
12 |
0 |
0 |
6 |
46 |
| Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective |
0 |
1 |
1 |
30 |
1 |
3 |
7 |
142 |
| What can we learn from the history of gasoline crack spreads?: Long memory, structural breaks and modeling implications |
1 |
1 |
1 |
23 |
2 |
2 |
7 |
136 |
| What can we learn from the return predictability over the business cycle? |
1 |
1 |
1 |
9 |
2 |
2 |
5 |
24 |
| What the investors need to know about forecasting oil futures return volatility |
0 |
0 |
0 |
11 |
0 |
0 |
1 |
86 |
| Total Journal Articles |
25 |
85 |
297 |
3,471 |
80 |
259 |
941 |
12,243 |
LogEc is hosted by the
Örebro University School of Business.