Torben G Andersen
Biographic Data
| ID | 8878658 |
|---|---|
| NAME | Torben G Andersen |
| GIVEN NAMES | Torben G |
| FAMILY NAME | Andersen |
| SIGNATURE | ANDERSEN T G |
| AFFILIATIONS | Northwestern University |
| ORCID | 0009-0004-0397-000X |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1994 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
The Pricing of Tail Risk and the Equity Premium: Evidence From International Option Markets
We explore the pricing of tail risk as manifest in index options across international equity markets. The risk premium associated with negative tail events displays persistent shifts, unrelated to volatility. This tail risk premium is a potent predictor of future returns for all the indices, while the option-implied volatility only forecasts the future return variation. Hence, compensation for negative jump risk is the primary driver of the equit…
Editors' Report 2006
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility
A growing literature documents important gains in asset return volatility forecasting via use of realized variation measures constructed from high-frequency returns. We progress by using newly developed bipower variation measures and corresponding nonparametric tests for jumps. Our empirical analyses of exchange rates, equity index returns, and bond yields suggest that the volatility jump component is both highly important and distinctly less per…
Modeling and Forecasting Realized Volatility
This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intrad…
Some Reflections on Analysis of High-Frequency Data
Finance is arguably the most empirically oriented of all the social sciences. This is in part due to the deliberate practical orientation and the ready availability of high-quality financial market data. In recent years, the ever lower costs of data recording and storage have driven the phenomenon to the ultimate limit for some markets: We may have access to time-stamped observations on all quotes and transactions, denoted ultra-high-frequency da…
Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts
Torben G. Andersen, Tim Bollerslev, Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts, International Economic Review, Vol. 39, No. 4, Symposium on Forecasting and Empirical Methods in Macroeconomics and Finance (Nov., 1998), pp. 885-905
GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods. We document the existence of a tradeoff between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore, an approximation to the optimal weighting matrix is used to explore the impact of the weighting ma…
[Bayesian Analysis of Stochastic Volatility Models]: Comment
No prominent works on this page.
[Bayesian Analysis of Stochastic Volatility Models]: Comment
GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods. We document the existence of a tradeoff between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore, an approximation to the optimal weighting matrix is used to explore the impact of the weighting ma…
Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts
Torben G. Andersen, Tim Bollerslev, Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts, International Economic Review, Vol. 39, No. 4, Symposium on Forecasting and Empirical Methods in Macroeconomics and Finance (Nov., 1998), pp. 885-905
Some Reflections on Analysis of High-Frequency Data
Finance is arguably the most empirically oriented of all the social sciences. This is in part due to the deliberate practical orientation and the ready availability of high-quality financial market data. In recent years, the ever lower costs of data recording and storage have driven the phenomenon to the ultimate limit for some markets: We may have access to time-stamped observations on all quotes and transactions, denoted ultra-high-frequency da…
Modeling and Forecasting Realized Volatility
This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intrad…
Editors' Report 2006
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility
A growing literature documents important gains in asset return volatility forecasting via use of realized variation measures constructed from high-frequency returns. We progress by using newly developed bipower variation measures and corresponding nonparametric tests for jumps. Our empirical analyses of exchange rates, equity index returns, and bond yields suggest that the volatility jump component is both highly important and distinctly less per…
The Pricing of Tail Risk and the Equity Premium: Evidence From International Option Markets
We explore the pricing of tail risk as manifest in index options across international equity markets. The risk premium associated with negative tail events displays persistent shifts, unrelated to volatility. This tail risk premium is a potent predictor of future returns for all the indices, while the option-implied volatility only forecasts the future return variation. Hence, compensation for negative jump risk is the primary driver of the equit…
Econometrics (8 works) · Economics (7 works) · Financial Risk and Volatility Modeling (5 works) · Stochastic processes and financial applications (4 works) · Stochastic volatility (4 works) · Complex Systems and Time Series Analysis (3 works) · Computer Science (3 works) · Financial economics (3 works) · Mathematics (3 works) · Statistics (3 works)