| 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 |
1 |
5 |
60 |
| A Theoretical Comparison Between Integrated and Realized Volatilies |
0 |
0 |
0 |
0 |
1 |
2 |
6 |
168 |
| A Theoretical Comparison Between Integrated and Realized Volatilies |
0 |
0 |
0 |
64 |
0 |
1 |
2 |
288 |
| A Theoretical Comparison Between Integrated and Realized Volatilities |
0 |
0 |
1 |
153 |
1 |
2 |
6 |
530 |
| ARMA REPRESENTATION OF INTEGRATED AND REALIZED VARIANCES |
0 |
0 |
0 |
19 |
0 |
3 |
5 |
205 |
| ARMA Representation of Integrated and Realized Variances |
0 |
0 |
0 |
54 |
0 |
1 |
3 |
195 |
| ARMA Representation of Integrated and Realized Variances |
0 |
0 |
1 |
134 |
3 |
4 |
6 |
696 |
| ARMA Representation of Two-Factor Models |
0 |
0 |
0 |
297 |
0 |
1 |
4 |
1,064 |
| Aggregations and Marginalization of GARCH and Stochastic Volatility Models |
0 |
2 |
2 |
209 |
2 |
5 |
7 |
609 |
| Aggregations and Marginalization of Garch and Stochastic Volatility Models |
0 |
0 |
0 |
0 |
1 |
4 |
6 |
454 |
| An Eigenfunction Approach for Volatility Modeling |
0 |
0 |
0 |
260 |
0 |
1 |
2 |
770 |
| An Eigenfunction Approach for Volatility Modeling |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
276 |
| An Eigenfunction Approach for Volatility Modeling |
0 |
0 |
0 |
311 |
1 |
2 |
4 |
1,376 |
| Analytic Evaluation of Volatility Forecasts |
0 |
0 |
0 |
815 |
0 |
1 |
2 |
1,882 |
| Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns |
0 |
0 |
0 |
53 |
0 |
0 |
4 |
113 |
| Bootstrapping pre-averaged realized volatility under market microstructure noise |
0 |
0 |
1 |
58 |
0 |
0 |
2 |
165 |
| Bootstrapping realized multivariate volatility measures |
0 |
0 |
0 |
6 |
0 |
1 |
1 |
63 |
| CORRECTING THE ERRORS: A NOTE ON VOLATILITY FORECAST EVALUATION BASED ON HIGH-FREQUENCY DATA AND REALIZED VOLATILITIES |
0 |
0 |
0 |
119 |
1 |
1 |
3 |
447 |
| Correcting the Errors: A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities |
0 |
0 |
0 |
171 |
1 |
1 |
2 |
492 |
| Correcting the Errors: A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities |
0 |
0 |
0 |
421 |
2 |
3 |
5 |
955 |
| Expected Value Models: A New Approach |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
2,352 |
| GARCH and Irregularly Spaced Data |
0 |
0 |
0 |
1 |
0 |
1 |
2 |
27 |
| Generalized Disappointment Aversion, Long Run Volatility Risk and Asset Prices |
0 |
0 |
0 |
88 |
3 |
3 |
5 |
257 |
| Generalized Disappointment Aversion, Long Run Volatility Risk and Asset Prices |
0 |
0 |
0 |
27 |
1 |
2 |
4 |
109 |
| Quadratic M-Estimators for ARCH-Type Processes |
0 |
0 |
0 |
209 |
0 |
0 |
4 |
949 |
| Quadratic M-Estimators for ARCH-Type Processes |
0 |
0 |
0 |
38 |
0 |
0 |
0 |
255 |
| TESTING NORMALITY: A GMM APPROACH |
0 |
0 |
0 |
64 |
4 |
6 |
7 |
344 |
| Temporal Aggregation of Volatility Models |
0 |
0 |
0 |
133 |
1 |
1 |
2 |
292 |
| Temporal Aggregation of Volatility Models |
0 |
0 |
0 |
428 |
0 |
1 |
2 |
1,341 |
| Testing Distributional Assumptions: A GMM Approach |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
297 |
| Testing Distributional Assumptions: A GMM Approach |
0 |
0 |
0 |
114 |
1 |
3 |
5 |
430 |
| Testing Normality: A GMM Approach |
0 |
0 |
0 |
183 |
2 |
2 |
4 |
702 |
| Testing Normality: A GMM Approach |
0 |
0 |
0 |
309 |
0 |
1 |
2 |
1,588 |
| The Economic Value of Realized Volatility: Using High-Frequency Returns for Option Valuation |
0 |
2 |
4 |
101 |
0 |
8 |
16 |
408 |
| Volatility Forecasting when the Noise Variance Is Time-Varying |
0 |
0 |
0 |
13 |
1 |
1 |
2 |
59 |
| Total Working Papers |
0 |
4 |
9 |
4,861 |
27 |
64 |
135 |
20,218 |
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