Tim Bollerslev
Biographic Data
| ID | 5849007 |
|---|---|
| NAME | Tim Bollerslev |
| GIVEN NAMES | Tim |
| FAMILY NAME | Bollerslev |
| SIGNATURE | BOLLERSLEV T |
| AFFILIATIONS | Duke University |
| ORCID | 0000-0003-2743-3087 |
| VERIFIED | Yes |
| TOTAL WORKS | 16 |
| TOTAL CITATIONS | 27 |
| WORKS AS AUTHOR | 16 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1986 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 1 |
Generalized Jump Regressions for Local Moments
We develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance type estimation of the parameters of interest under general loss functions that include both least-square and more robust quantile regressions as spec…
Roughing It Up
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…
The Message in Daily Exchange Rates
Formal testing procedures confirm the presence of a unit root in the autoregressive ploynomial of the univariate time series representation of daily exchange-rate data. the first differences of the logarithms of daily spot rates are approximately uncorrelated through time, and a generalized autoregressive conditional heteroscedasticity model with daily dummy variables and conditionally t-distributed errors is found to provide a good representatio…
High-Frequency Data, Frequency Domain Inference, and Volatility Forecasting
Although it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high-frequency data. The method avoids using a tight parametric model by instead simply fitting a long autoregression to log-squared, squared, or absolute high-freq…
Equity Trading Volume and Volatility
This article examines the behavior of equity trading volume and volatility for the individual firms composing the Standard & Poor's 100 composite index. Using multivariate spectral methods, we find that fractionally integrated processes best describe the long-run temporal dependencies in both series. Consistent with a stylized mixture-of-distributions hypothesis model in which the aggregate “news”-arrival process possesses long-memory characteris…
Answering the Skeptics
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
Periodic Autoregressive Conditional Heteroscedasticity
Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class of periodic autoregressive moving average (ARMA) model…
Dan Nelson Remembered
ARCH modeling in finance
Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally…
Modelling the Coherence in Short-Run Nominal Exchange Rates
A multivariate time series model with time varying conditional variances and covariances, but constant conditional correlations is proposed. In a multivariate regression framework, the model is readily interpreted as an extension of the Seemingly Unrelated Regression (SUR) model allowing for heteroskedasticity. Parameterizing each of the conditional variances as a univariate Generalized Autoregressive Conditional Heteroskedastic (GARCH) process, …
The Message in Daily Exchange Rates
Formal testing procedures confirm the presence of a unit root in the autoregressive polynomial of the univariate time series representation of daily exchange-rate data. The first differences of the logarithms of daily spot rates are approximately uncorrelated through time, and a generalized autoregressive conditional heteroscedasticity model with daily dummy variables and conditionally t-distributed errors is found to provide a good representatio…
A Capital Asset Pricing Model with Time-Varying Covariances
The capital asset pricing model provides a theoretical structure for the pricing of assets with uncertain returns. The premium to induc e risk-averse investors to bear risk is proportional to the nondivers ifiable risk, which is measured by the covariance of the asset return with the market portfolio return. In this paper, a multivariate, gen eralized-autoregressive, conditional, heteroscedastic process is esti mated for returns to bills, bonds, …
A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return
Generalized autoregressive conditional heteroskedasticity
A Capital Asset Pricing Model with Time-Varying Covariances
The capital asset pricing model provides a theoretical structure for the pricing of assets with uncertain returns. The premium to induc e risk-averse investors to bear risk is proportional to the nondivers ifiable risk, which is measured by the covariance of the asset return with the market portfolio return. In this paper, a multivariate, gen eralized-autoregressive, conditional, heteroscedastic process is esti mated for returns to bills, bonds, …
Generalized autoregressive conditional heteroskedasticity
A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return
A Capital Asset Pricing Model with Time-Varying Covariances
The capital asset pricing model provides a theoretical structure for the pricing of assets with uncertain returns. The premium to induc e risk-averse investors to bear risk is proportional to the nondivers ifiable risk, which is measured by the covariance of the asset return with the market portfolio return. In this paper, a multivariate, gen eralized-autoregressive, conditional, heteroscedastic process is esti mated for returns to bills, bonds, …
The Message in Daily Exchange Rates
Formal testing procedures confirm the presence of a unit root in the autoregressive polynomial of the univariate time series representation of daily exchange-rate data. The first differences of the logarithms of daily spot rates are approximately uncorrelated through time, and a generalized autoregressive conditional heteroscedasticity model with daily dummy variables and conditionally t-distributed errors is found to provide a good representatio…
Modelling the Coherence in Short-Run Nominal Exchange Rates
A multivariate time series model with time varying conditional variances and covariances, but constant conditional correlations is proposed. In a multivariate regression framework, the model is readily interpreted as an extension of the Seemingly Unrelated Regression (SUR) model allowing for heteroskedasticity. Parameterizing each of the conditional variances as a univariate Generalized Autoregressive Conditional Heteroskedastic (GARCH) process, …
ARCH modeling in finance
Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally…
Dan Nelson Remembered
Periodic Autoregressive Conditional Heteroscedasticity
Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class of periodic autoregressive moving average (ARMA) model…
Answering the Skeptics
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
Equity Trading Volume and Volatility
This article examines the behavior of equity trading volume and volatility for the individual firms composing the Standard & Poor's 100 composite index. Using multivariate spectral methods, we find that fractionally integrated processes best describe the long-run temporal dependencies in both series. Consistent with a stylized mixture-of-distributions hypothesis model in which the aggregate “news”-arrival process possesses long-memory characteris…
High-Frequency Data, Frequency Domain Inference, and Volatility Forecasting
Although it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high-frequency data. The method avoids using a tight parametric model by instead simply fitting a long autoregression to log-squared, squared, or absolute high-freq…
The Message in Daily Exchange Rates
Formal testing procedures confirm the presence of a unit root in the autoregressive ploynomial of the univariate time series representation of daily exchange-rate data. the first differences of the logarithms of daily spot rates are approximately uncorrelated through time, and a generalized autoregressive conditional heteroscedasticity model with daily dummy variables and conditionally t-distributed errors is found to provide a good representatio…
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…
Roughing It Up
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…
Generalized Jump Regressions for Local Moments
We develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance type estimation of the parameters of interest under general loss functions that include both least-square and more robust quantile regressions as spec…
Econometrics (16 works) · Economics (13 works) · Financial Risk and Volatility Modeling (13 works) · Market Dynamics and Volatility (12 works) · Mathematics (10 works) · Statistics (10 works) · Monetary Policy and Economic Impact (9 works) · Autoregressive model (8 works) · Heteroscedasticity (7 works) · Volatility (finance) (7 works)