Bollerslev, Tim 1958
Overview
Works:  99 works in 323 publications in 2 languages and 1,268 library holdings 

Roles:  Author, Editor, Creator, Contributor, Honoree 
Classifications:  HB1, 330.0151955 
Publication Timeline
.
Most widely held works by
Tim Bollerslev
Volatility and time series econometrics : essays in honor of Robert F. Engle(
Book
)
19 editions published between 2009 and 2010 in English and held by 192 WorldCat member libraries worldwide
This volume celebrates and develops the work of Nobel Laureate Robert Engle. It includes original contributions from some of the world's leading econometricians that further Engle's work in time series economics
19 editions published between 2009 and 2010 in English and held by 192 WorldCat member libraries worldwide
This volume celebrates and develops the work of Nobel Laureate Robert Engle. It includes original contributions from some of the world's leading econometricians that further Engle's work in time series economics
Testing for market microstructure effects in intraday volatility : a reassessment of the Tokyo FX experiment by
Torben G Andersen(
Book
)
11 editions published in 1998 in English and held by 73 WorldCat member libraries worldwide
This paper develops mew robust inference procedures for analyzing the intraday return volatility patterns that constitute a focal point of much market microstructure theory. Our empirical analysis is motivated by the recent lifting of trading restrictions in the interbank foreign exchange (FX) market for Japanese banks during the Tokyo lunch period. Ito, Lyons, and Melvin (1998) (ILM) argue that this deregulation resulted in a highly significant shift in the volatility pattern across the entire Japanese trading day, indicating that private information is an important component of the price formation process in the FX market. In contrast, our robust analysis finds no evidence for any discernible change in the pattern outside of the Tokyo lunch period. Moreover, we document that the standard varianceratio methodology inference in this highfrequency data context
11 editions published in 1998 in English and held by 73 WorldCat member libraries worldwide
This paper develops mew robust inference procedures for analyzing the intraday return volatility patterns that constitute a focal point of much market microstructure theory. Our empirical analysis is motivated by the recent lifting of trading restrictions in the interbank foreign exchange (FX) market for Japanese banks during the Tokyo lunch period. Ito, Lyons, and Melvin (1998) (ILM) argue that this deregulation resulted in a highly significant shift in the volatility pattern across the entire Japanese trading day, indicating that private information is an important component of the price formation process in the FX market. In contrast, our robust analysis finds no evidence for any discernible change in the pattern outside of the Tokyo lunch period. Moreover, we document that the standard varianceratio methodology inference in this highfrequency data context
Answering the critics : yes, ARCH models do provide good volatility forecasts by
Torben G Andersen(
Book
)
13 editions published in 1997 in English and Danish and held by 66 WorldCat member libraries worldwide
Abstract: Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good forecasts of future volatility are critical for the implementation and evaluation of asset pricing theories. In response to this, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility at the daily and lower frequencies using ARCH and stochastic volatility type models. Most of these studies find highly significant insample parameter estimates and pronounced intertemporal volatility persistence. Meanwhile, when judged by standard forecast evaluation criteria, based on the squared or absolute returns over daily or longer forecast horizons, ARCH models provide seemingly poor volatility forecasts. The present paper demonstrates that ARCH models, contrary to the above contention, produce strikingly accurate interdaily forecasts for the latent volatility factor that is relevant for most financial applications
13 editions published in 1997 in English and Danish and held by 66 WorldCat member libraries worldwide
Abstract: Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good forecasts of future volatility are critical for the implementation and evaluation of asset pricing theories. In response to this, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility at the daily and lower frequencies using ARCH and stochastic volatility type models. Most of these studies find highly significant insample parameter estimates and pronounced intertemporal volatility persistence. Meanwhile, when judged by standard forecast evaluation criteria, based on the squared or absolute returns over daily or longer forecast horizons, ARCH models provide seemingly poor volatility forecasts. The present paper demonstrates that ARCH models, contrary to the above contention, produce strikingly accurate interdaily forecasts for the latent volatility factor that is relevant for most financial applications
DMdollar volatility : intraday activity patterns, macroeconomic announcements, and longer run dependencies by
Torben G Andersen(
Book
)
12 editions published in 1996 in English and held by 66 WorldCat member libraries worldwide
Abstract: This paper characterizes the volatility in the DMdollar foreign exchange market using an annual sample of fiveminute returns. Our modeling approach explicitly captures the pronounced intraday activity patterns, the strong macroeconomic announcement effects, and the volatility persistence, or ARCH effects, familiar from lower frequency returns. The different features are separately quantified and shown, in conjunction, to account for a substantial fraction of the realized return variability, both at the intradaily and daily levels. Moreover, we demonstrate how the high frequency returns, when properly modeled, constitute an extremely valuable and vastly underutilized resource for better understanding the volatility dynamics at the daily or lower frequencies
12 editions published in 1996 in English and held by 66 WorldCat member libraries worldwide
Abstract: This paper characterizes the volatility in the DMdollar foreign exchange market using an annual sample of fiveminute returns. Our modeling approach explicitly captures the pronounced intraday activity patterns, the strong macroeconomic announcement effects, and the volatility persistence, or ARCH effects, familiar from lower frequency returns. The different features are separately quantified and shown, in conjunction, to account for a substantial fraction of the realized return variability, both at the intradaily and daily levels. Moreover, we demonstrate how the high frequency returns, when properly modeled, constitute an extremely valuable and vastly underutilized resource for better understanding the volatility dynamics at the daily or lower frequencies
Heterogeneous information arrivals and return volatility dynamics : uncovering the longrun in high frequency returns by
Torben G Andersen(
Book
)
11 editions published in 1996 in English and held by 65 WorldCat member libraries worldwide
Abstract: Recent empirical evidence suggests that the longrun dependence in financial market volatility is best characterized by a slowly meanreverting fractionally integrated process. At the same time, much shorterlived volatility dependencies are typically observed with highfrequency intradaily returns. This paper draws on the information arrival, or mixtureofdistributions hypothesis interpretation of the latent volatility process in rationalizing this behavior. By interpreting the overall volatility as the manifestation of numerous heterogeneous information arrivals, sudden bursts of volatility typically will have both shortrun and longrun components. Over intradaily frequencies, the shortrun decay stands out most clearly, while the impact of the highly persistent processes will be dominant over longer horizons. These ideas are confirmed by our empirical analysis of a oneyear time series of intradaily fiveminute Deutschemark  U.S. Dollar returns. Whereas traditional time series based measures for the temporal dependencies in the absolute returns give rise to very conflicting results across different intradaily sampling frequencies, the corresponding semiparametric estimates for the order of fractional integration remain remarkably stable. Similarly, the autocorrelogram for the lowpass filtered absolute returns, obtained by annihilating periods in excess of one day, exhibit a striking hyperbolic rate of decay
11 editions published in 1996 in English and held by 65 WorldCat member libraries worldwide
Abstract: Recent empirical evidence suggests that the longrun dependence in financial market volatility is best characterized by a slowly meanreverting fractionally integrated process. At the same time, much shorterlived volatility dependencies are typically observed with highfrequency intradaily returns. This paper draws on the information arrival, or mixtureofdistributions hypothesis interpretation of the latent volatility process in rationalizing this behavior. By interpreting the overall volatility as the manifestation of numerous heterogeneous information arrivals, sudden bursts of volatility typically will have both shortrun and longrun components. Over intradaily frequencies, the shortrun decay stands out most clearly, while the impact of the highly persistent processes will be dominant over longer horizons. These ideas are confirmed by our empirical analysis of a oneyear time series of intradaily fiveminute Deutschemark  U.S. Dollar returns. Whereas traditional time series based measures for the temporal dependencies in the absolute returns give rise to very conflicting results across different intradaily sampling frequencies, the corresponding semiparametric estimates for the order of fractional integration remain remarkably stable. Similarly, the autocorrelogram for the lowpass filtered absolute returns, obtained by annihilating periods in excess of one day, exhibit a striking hyperbolic rate of decay
Financial market efficiency tests by
Tim Bollerslev(
Book
)
11 editions published in 1992 in English and held by 46 WorldCat member libraries worldwide
This paper provides a selective survey of the voluminous literature on tests for market efficiency. The ideas discussed include standard autocorrelation tests, multiperiod regression tests and volatility tests. The formulation and estimation of models for timevarying volatility are also considered. Dependence in secondorder moments plays an important role in implementing and understanding tests for market efficiency. All of the reported test statistics and model estimates are calculated with monthly data on valueweighted NYSE stock prices and dividends. The distributions of the test statistics under various alternatives, including fads and bubbles, are illustrated through the use of Monte Carlo methods. In addition to the standard constant discount rate present value model, we postulate and simulate a new fundamental price relationship that accounts for the timevarying uncertainty in the monthly dividend growth rates. Allowing the discount rate to be a function of the timevarying uncertainty in the dividend process results in a simulated fundamental price series that is broadly consistent with most of the sample statistics of the actual data
11 editions published in 1992 in English and held by 46 WorldCat member libraries worldwide
This paper provides a selective survey of the voluminous literature on tests for market efficiency. The ideas discussed include standard autocorrelation tests, multiperiod regression tests and volatility tests. The formulation and estimation of models for timevarying volatility are also considered. Dependence in secondorder moments plays an important role in implementing and understanding tests for market efficiency. All of the reported test statistics and model estimates are calculated with monthly data on valueweighted NYSE stock prices and dividends. The distributions of the test statistics under various alternatives, including fads and bubbles, are illustrated through the use of Monte Carlo methods. In addition to the standard constant discount rate present value model, we postulate and simulate a new fundamental price relationship that accounts for the timevarying uncertainty in the monthly dividend growth rates. Allowing the discount rate to be a function of the timevarying uncertainty in the dividend process results in a simulated fundamental price series that is broadly consistent with most of the sample statistics of the actual data
Roughing it up : including jump components in the measurement, modeling and forecasting of return volatility by
Torben G Andersen(
Book
)
11 editions published in 2005 in English and held by 31 WorldCat member libraries worldwide
A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from highfrequency returns coupled with simple modeling procedures. Building on recent theoretical results in BarndorffNielsen and Shephard (2004a, 2005) for related bipower variation measures, the present paper provides a practical and robust framework for nonparametrically measuring the jump component in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from nonjump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the nonjump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process
11 editions published in 2005 in English and held by 31 WorldCat member libraries worldwide
A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from highfrequency returns coupled with simple modeling procedures. Building on recent theoretical results in BarndorffNielsen and Shephard (2004a, 2005) for related bipower variation measures, the present paper provides a practical and robust framework for nonparametrically measuring the jump component in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from nonjump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the nonjump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process
Micro effects of macroannouncements : realtime price discovery in foreign exchange by
Torben G Andersen(
)
18 editions published between 2002 and 2006 in English and held by 27 WorldCat member libraries worldwide
We characterize the response of U.S., German and British stock, bond and foreign exchange markets to realtime U.S. macroeconomic news. Our analysis is based on a unique data set of highfrequency futures returns for each of the markets. We find that news surprises produce conditional mean jumps; hence highfrequency stock, bond and exchange rate dynamics are linked to fundamentals. The details of the linkages are particularly intriguing as regards equity markets. We show that equity markets react differently to the same news depending on the state of the U.S. economy, with bad news having a positive impact during expansions and the traditionallyexpected negative impact during recessions. We rationalize this by temporal variation in the competing "cash flow" and "discount rate" effects for equity valuation. This finding also helps explain the apparent timevarying correlation between stock and bond returns, and the relatively small equity market news announcement effect when averaged across expansions and recessions. Hence, while our results confirm previous unconditional rankings suggesting that bond markets almost uniformly react most strongly to macroeconomic news, followed by foreign exchange and then equity markets, importantly when conditioning on the state of the economy the foreign exchange and equity markets appear equally responsive. Lastly, relying on the pronounced heteroskedasticity in the new highfrequency data, we also document important contemporaneous linkages across all markets and countries overandabove the direct news announcement effects
18 editions published between 2002 and 2006 in English and held by 27 WorldCat member libraries worldwide
We characterize the response of U.S., German and British stock, bond and foreign exchange markets to realtime U.S. macroeconomic news. Our analysis is based on a unique data set of highfrequency futures returns for each of the markets. We find that news surprises produce conditional mean jumps; hence highfrequency stock, bond and exchange rate dynamics are linked to fundamentals. The details of the linkages are particularly intriguing as regards equity markets. We show that equity markets react differently to the same news depending on the state of the U.S. economy, with bad news having a positive impact during expansions and the traditionallyexpected negative impact during recessions. We rationalize this by temporal variation in the competing "cash flow" and "discount rate" effects for equity valuation. This finding also helps explain the apparent timevarying correlation between stock and bond returns, and the relatively small equity market news announcement effect when averaged across expansions and recessions. Hence, while our results confirm previous unconditional rankings suggesting that bond markets almost uniformly react most strongly to macroeconomic news, followed by foreign exchange and then equity markets, importantly when conditioning on the state of the economy the foreign exchange and equity markets appear equally responsive. Lastly, relying on the pronounced heteroskedasticity in the new highfrequency data, we also document important contemporaneous linkages across all markets and countries overandabove the direct news announcement effects
Parametric and nonparametric volatility measurement by
Torben G Andersen(
Book
)
8 editions published in 2002 in English and held by 22 WorldCat member libraries worldwide
Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade. This chapter provides a unified continuoustime, frictionless, noarbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures. We define three different volatility concepts: (i) the notional volatility corresponding to the expost samplepath return variability over a fixed time interval, (ii) the exante expected volatility over a fixed time interval, and (iii) the instantaneous volatility corresponding to the strength of the volatility process at a point in time. The parametric procedures rely on explicit functional form assumptions regarding the expected and/or instantaneous volatility. In the discretetime ARCH class of models, the expectations are formulated in terms of directly observable variables, while the discrete and continuoustime stochastic volatility models involve latent state variable(s). The nonparametric procedures are generally free from such functional form assumptions and hence afford estimates of notional volatility that are flexible yet consistent (as the sampling frequency of the underlying returns increases). The nonparametric procedures include ARCH filters and smoothers designed to measure the volatility over infinitesimally short horizons, as well as the recentlypopularized realized volatility measures for (nontrivial) fixedlength time intervals
8 editions published in 2002 in English and held by 22 WorldCat member libraries worldwide
Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade. This chapter provides a unified continuoustime, frictionless, noarbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures. We define three different volatility concepts: (i) the notional volatility corresponding to the expost samplepath return variability over a fixed time interval, (ii) the exante expected volatility over a fixed time interval, and (iii) the instantaneous volatility corresponding to the strength of the volatility process at a point in time. The parametric procedures rely on explicit functional form assumptions regarding the expected and/or instantaneous volatility. In the discretetime ARCH class of models, the expectations are formulated in terms of directly observable variables, while the discrete and continuoustime stochastic volatility models involve latent state variable(s). The nonparametric procedures are generally free from such functional form assumptions and hence afford estimates of notional volatility that are flexible yet consistent (as the sampling frequency of the underlying returns increases). The nonparametric procedures include ARCH filters and smoothers designed to measure the volatility over infinitesimally short horizons, as well as the recentlypopularized realized volatility measures for (nontrivial) fixedlength time intervals
Noarbitrage semiMartingale restrictions for continuoustime volatility models subject to leverage effects, jumps and i.i.d.
noise : theory and testable distributional implications by
Torben G Andersen(
Book
)
7 editions published in 2007 in English and held by 21 WorldCat member libraries worldwide
"We develop a sequential procedure to test the adequacy of jumpdiffusion models for return distributions. We rely on intraday data and nonparametric volatility measures, along with a new jump detection technique and appropriate conditional moment tests, for assessing the import of jumps and leverage effects. A novel robusttojumps approach is utilized to alleviate microstructure frictions for realized volatility estimation. Size and power of the procedure are explored through Monte Carlo methods. Our empirical findings support the jumpdiffusive representation for S & P500 futures returns but reveal it is critical to account for leverage effects and jumps to maintain the underlying semimartingale assumption"National Bureau of Economic Research web site
7 editions published in 2007 in English and held by 21 WorldCat member libraries worldwide
"We develop a sequential procedure to test the adequacy of jumpdiffusion models for return distributions. We rely on intraday data and nonparametric volatility measures, along with a new jump detection technique and appropriate conditional moment tests, for assessing the import of jumps and leverage effects. A novel robusttojumps approach is utilized to alleviate microstructure frictions for realized volatility estimation. Size and power of the procedure are explored through Monte Carlo methods. Our empirical findings support the jumpdiffusive representation for S & P500 futures returns but reveal it is critical to account for leverage effects and jumps to maintain the underlying semimartingale assumption"National Bureau of Economic Research web site
High frequency data, frequency domain inference and volatility forecasting by
Jonathan H Wright(
Book
)
6 editions published in 1999 in English and held by 16 WorldCat member libraries worldwide
While 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 logsquared, squared or absolute high frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of logsquared, squared or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an outofsample forecasting exercise
6 editions published in 1999 in English and held by 16 WorldCat member libraries worldwide
While 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 logsquared, squared or absolute high frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of logsquared, squared or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an outofsample forecasting exercise
A framework for exploring the macroeconomic determinants of systematic risk(
)
3 editions published in 2005 in English and held by 16 WorldCat member libraries worldwide
3 editions published in 2005 in English and held by 16 WorldCat member libraries worldwide
Some like it smooth, and some like it rough untangling continuous and jump components in measuring, modeling, and forecasting
asset return volatility(
)
2 editions published in 2003 in English and held by 16 WorldCat member libraries worldwide
A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from highfrequency returns coupled with relatively simple reducedform time series modeling procedures. Building on recent theoretical results from BarndorffNielsen and Shephard (2003c,d) for related bipower variation measures involving the sum of highfrequency absolute returns, the present paper provides a practical framework for nonparametrically measuring the jump component in realized volatility measurements. Exploiting these ideas for a decade of highfrequency fiveminute returns for the DM/$ exchange rate, the S&P500 market index, and the 30year U.S. Treasury bond yield, we find the jump component of the price process to be distinctly less persistent than the continuous sample path component. Explicitly including the jump measure as an additional explanatory variable in an easytoimplement reduced form model for realized volatility results in highly significant jump coefficient estimates at the daily, weekly and quarterly forecast horizons. As such, our results hold promise for improved financial asset allocation, risk management, and derivatives pricing, by separate modeling, forecasting and pricing of the continuous and jump components of total return variability. Klassifikation: C1, G1
2 editions published in 2003 in English and held by 16 WorldCat member libraries worldwide
A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from highfrequency returns coupled with relatively simple reducedform time series modeling procedures. Building on recent theoretical results from BarndorffNielsen and Shephard (2003c,d) for related bipower variation measures involving the sum of highfrequency absolute returns, the present paper provides a practical framework for nonparametrically measuring the jump component in realized volatility measurements. Exploiting these ideas for a decade of highfrequency fiveminute returns for the DM/$ exchange rate, the S&P500 market index, and the 30year U.S. Treasury bond yield, we find the jump component of the price process to be distinctly less persistent than the continuous sample path component. Explicitly including the jump measure as an additional explanatory variable in an easytoimplement reduced form model for realized volatility results in highly significant jump coefficient estimates at the daily, weekly and quarterly forecast horizons. As such, our results hold promise for improved financial asset allocation, risk management, and derivatives pricing, by separate modeling, forecasting and pricing of the continuous and jump components of total return variability. Klassifikation: C1, G1
Realized beta persistence and predictability(
)
3 editions published between 2004 and 2005 in English and held by 15 WorldCat member libraries worldwide
A large literature over several decades reveals both extensive concern with the question of timevarying betas and an emerging consensus that betas are in fact timevarying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, visàvis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recentlypopularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionallyintegrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management. Klassifikation: C1, G1
3 editions published between 2004 and 2005 in English and held by 15 WorldCat member libraries worldwide
A large literature over several decades reveals both extensive concern with the question of timevarying betas and an emerging consensus that betas are in fact timevarying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, visàvis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recentlypopularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionallyintegrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management. Klassifikation: C1, G1
Estimating stochastic volatility diffusion using conditional moments of integrated volatility by
Tim Bollerslev(
Book
)
7 editions published in 2001 in English and held by 14 WorldCat member libraries worldwide
7 editions published in 2001 in English and held by 14 WorldCat member libraries worldwide
Dynamic estimation of volatility risk premia and investor risk aversion from optionimplied and realized volatilities by
Tim Bollerslev(
Book
)
6 editions published in 2004 in English and held by 10 WorldCat member libraries worldwide
"This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized modelfree realized and optionimplied volatility measures. A smallscale Monte Carlo experiment suggests that the procedure works well in practice. Implementing the procedure with actual S & P 500 optionimplied volatilities and highfrequency fiveminutebased realized volatilities results in significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of underlying macrofinance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns"Federal Reserve Board web site
6 editions published in 2004 in English and held by 10 WorldCat member libraries worldwide
"This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized modelfree realized and optionimplied volatility measures. A smallscale Monte Carlo experiment suggests that the procedure works well in practice. Implementing the procedure with actual S & P 500 optionimplied volatilities and highfrequency fiveminutebased realized volatilities results in significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of underlying macrofinance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns"Federal Reserve Board web site
Volatility puzzles : a unified framework for gauging returnvolatility regressions by
Tim Bollerslev(
Book
)
6 editions published in 2003 in English and held by 9 WorldCat member libraries worldwide
"This paper provides a simple unified framework for assessing the empirical linkages between returns and realized and implied volatilities. First, we show that whereas the volatility feedback effect as measured by the sign of the correlation between contemporaneous return and realized volatility depends importantly on the underlying structural model parameters, the correlation between return and implied volatility is unambiguously positive for all reasonable parameter configurations. Second, the lagged returnvolatility asymmetry, or the leverage effect, is always stronger for implied than realized volatility. Third, implied volatilities generally provide downward biased forecasts of subsequent realized volatilities. Our results help explain previous findings reported in the extant empirical literature, and is further corroborated by new estimation results for a sample of monthly returns and implied and realized volatilities for the aggregate S & P market index"Federal Reserve Board web site
6 editions published in 2003 in English and held by 9 WorldCat member libraries worldwide
"This paper provides a simple unified framework for assessing the empirical linkages between returns and realized and implied volatilities. First, we show that whereas the volatility feedback effect as measured by the sign of the correlation between contemporaneous return and realized volatility depends importantly on the underlying structural model parameters, the correlation between return and implied volatility is unambiguously positive for all reasonable parameter configurations. Second, the lagged returnvolatility asymmetry, or the leverage effect, is always stronger for implied than realized volatility. Third, implied volatilities generally provide downward biased forecasts of subsequent realized volatilities. Our results help explain previous findings reported in the extant empirical literature, and is further corroborated by new estimation results for a sample of monthly returns and implied and realized volatilities for the aggregate S & P market index"Federal Reserve Board web site
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Related Identities
 Andersen, Torben G. (Torben Gustav) Author Contributor
 National Bureau of Economic Research
 Engle, R. F. (Robert F.) Honoree Dedicatee
 Watson, Mark W. Editor Creator
 Russell, Jeffrey R. Editor
 Diebold, Francis X. Contributor
 Das, Ashish
 Hodrick, Robert J.
 Board of Governors of the Federal Reserve System (U.S.)
 Dobrev, Dobrislav
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Assets (Accounting)PricesEconometric models Autoregression (Statistics) BondsEconometric models Business cyclesEconometric models DividendsMathematical models Dollar, American Econometrics Economic forecasting Economics FinanceEconometric models Foreign exchange market Foreign exchange marketEconometric models Foreign exchange rates Foreign exchange ratesEconometric models Foreign exchange ratesMathematical models Germany Great Britain Japan Management Mark, German Rate of returnEconometric models SecuritiesPrices Stock exchangesEconometric models Stock price forecastingStatistical methods StocksEconometric models StocksPrices StocksPricesMathematical models Timeseries analysis United States