| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Distributional Approach to Realized Volatility |
0 |
0 |
0 |
7 |
0 |
2 |
4 |
59 |
| A Theoretical Comparison Between Integrated and Realized Volatilies |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
165 |
| A Theoretical Comparison Between Integrated and Realized Volatilies |
0 |
0 |
0 |
64 |
0 |
1 |
3 |
287 |
| A Theoretical Comparison Between Integrated and Realized Volatilities |
0 |
0 |
1 |
153 |
0 |
2 |
4 |
527 |
| ARMA REPRESENTATION OF INTEGRATED AND REALIZED VARIANCES |
0 |
0 |
0 |
19 |
0 |
2 |
2 |
202 |
| ARMA Representation of Integrated and Realized Variances |
0 |
1 |
1 |
134 |
0 |
2 |
2 |
692 |
| ARMA Representation of Integrated and Realized Variances |
0 |
0 |
0 |
54 |
0 |
1 |
2 |
194 |
| ARMA Representation of Two-Factor Models |
0 |
0 |
0 |
297 |
1 |
2 |
2 |
1,062 |
| Aggregations and Marginalization of GARCH and Stochastic Volatility Models |
0 |
0 |
0 |
207 |
0 |
1 |
3 |
604 |
| Aggregations and Marginalization of Garch and Stochastic Volatility Models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
449 |
| An Eigenfunction Approach for Volatility Modeling |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
275 |
| An Eigenfunction Approach for Volatility Modeling |
0 |
0 |
0 |
311 |
0 |
0 |
0 |
1,372 |
| An Eigenfunction Approach for Volatility Modeling |
0 |
0 |
0 |
260 |
0 |
0 |
2 |
768 |
| Analytic Evaluation of Volatility Forecasts |
0 |
0 |
0 |
815 |
0 |
1 |
3 |
1,881 |
| Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns |
0 |
0 |
0 |
53 |
0 |
0 |
4 |
111 |
| Bootstrapping pre-averaged realized volatility under market microstructure noise |
0 |
1 |
2 |
58 |
0 |
1 |
7 |
165 |
| Bootstrapping realized multivariate volatility measures |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
62 |
| CORRECTING THE ERRORS: A NOTE ON VOLATILITY FORECAST EVALUATION BASED ON HIGH-FREQUENCY DATA AND REALIZED VOLATILITIES |
0 |
0 |
1 |
119 |
0 |
1 |
3 |
445 |
| Correcting the Errors: A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities |
0 |
0 |
0 |
421 |
0 |
1 |
1 |
951 |
| Correcting the Errors: A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities |
0 |
0 |
0 |
171 |
0 |
0 |
1 |
491 |
| Expected Value Models: A New Approach |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2,351 |
| GARCH and Irregularly Spaced Data |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
26 |
| Generalized Disappointment Aversion, Long Run Volatility Risk and Asset Prices |
0 |
0 |
0 |
27 |
0 |
1 |
2 |
107 |
| Generalized Disappointment Aversion, Long Run Volatility Risk and Asset Prices |
0 |
0 |
0 |
88 |
0 |
1 |
1 |
253 |
| Quadratic M-Estimators for ARCH-Type Processes |
0 |
0 |
0 |
209 |
0 |
1 |
4 |
948 |
| Quadratic M-Estimators for ARCH-Type Processes |
0 |
0 |
0 |
38 |
0 |
0 |
1 |
255 |
| TESTING NORMALITY: A GMM APPROACH |
0 |
0 |
0 |
64 |
0 |
1 |
2 |
338 |
| Temporal Aggregation of Volatility Models |
0 |
0 |
0 |
428 |
0 |
0 |
3 |
1,339 |
| Temporal Aggregation of Volatility Models |
0 |
0 |
0 |
133 |
0 |
0 |
0 |
290 |
| Testing Distributional Assumptions: A GMM Approach |
0 |
0 |
0 |
114 |
0 |
1 |
4 |
426 |
| Testing Distributional Assumptions: A GMM Approach |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
295 |
| Testing Normality: A GMM Approach |
0 |
0 |
0 |
183 |
0 |
1 |
1 |
699 |
| Testing Normality: A GMM Approach |
0 |
0 |
0 |
309 |
0 |
1 |
2 |
1,587 |
| The Economic Value of Realized Volatility: Using High-Frequency Returns for Option Valuation |
0 |
0 |
0 |
97 |
0 |
1 |
4 |
395 |
| Volatility Forecasting when the Noise Variance Is Time-Varying |
0 |
0 |
0 |
13 |
0 |
0 |
1 |
58 |
| Total Working Papers |
0 |
2 |
5 |
4,855 |
1 |
29 |
75 |
20,129 |
LogEc is hosted by the
Örebro University School of Business.