FINANCE; INTERNATIONAL BUSINESS & MARKETS
Nathan S. and Mary P. Sharp Professor of Finance
Professor Andersen has published widely in asset pricing, empirical finance, and empirical market microstructure. His work has centered on the modeling of volatility fluctuations in financial returns with applications to asset and derivatives pricing, portfolio selection, and the term structure of interest rates. His current work explores the use of large data sets of very high-frequency data for volatility forecasting, portfolio choice and risk management. He has received grants from the National Science Foundation, the Sloan Foundation, and the Institute for Quantitative Research in Finance (the Q-Group). He served as the editor-in-chief for the Journal of Business and Economic Statistics in 2004-2006, and he has served on the editorial board of various leading journals, including the Journal of Finance, Review of Financial Studies, Econometric Theory, and Management Science.
Professor Andersen has served as a consultant to the Stafford Trading Group, the Federal Reserve Board of Governors, various regional Federal Reserve Banks, foreign Central Banks, universities, and other organizations. He received his PhD in Economics from Yale University.
Econometrics
Equity Markets (Stock Market) (Includes: Asset Pricing, Investments and Portfolio Choice)
Financial Engineering
International Finance (Exchange Rates, Current Account)
Investments and Portfolio Choice (Includes: Asset Pricing, Equity Markets/Stock Market)
Microeconomics
- Recent Media Coverage
Economist Intelligence Unit: Executive Briefing: The VIX, CIV, and MFIV: Measuring up the accuracy of option-based predictors of volatility - 10/3/2008
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- Recent Kellogg News
Global perspective - 7/14/2009
Kellogg School finance scholar Torben Andersen elected to Econometric Society - 12/15/2008
Northwestern University’s Kellogg School of Management Celebrates Centennial - 10/10/2008
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We develop a sequential procedure to test the adequacy of jump-diffusion 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 robust-to-jumps 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 jump-diffusive representation for S&P500 futures returns but reveal it is critical to account for leverage effects and jumps to maintain the underlying semi-martingale assumption.
Using a unique high-frequency futures dataset, we characterize the response of U.S., German and British stock, bond and foreign exchange markets to real-time U.S. macroeconomic news. We find that news produces conditional mean jumps; hence high-frequency stock, bond and exchange rate dynamics are linked to fundamentals. Equity markets, moreover, react differently to news depending on the stage of the business cycle, which explains the low correlation between stock and bond returns when averaged over the cycle. Hence our results qualify earlier work suggesting that bond markets react most strongly to macroeconomic news; in particular, when conditioning on the state of the economy, the equity and foreign exchange markets appear equally responsive. Finally, we also document important contemporaneous links across all markets and countries, even after controlling for the effects of macroeconomic news.
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 persistent than the continuous component, and that separating the rough jump moves from the smooth continuous moves results in significant out-of-sample volatility forecast improvements. Moreover, many of the significant jumps are associated with specific macroeconomic news announcements.
In this paper we selectively survey, unify and extend that literature on asset return "realized" volatility and correlation dynamics. Rather than focusing exclusively on characterization of the properties of realized volatility, we progress by examining economically interesting functions of realized volatility, namely, realized betas for equity portfolios, relating them both to their underlying realized variance and covariance parts and to underlying macroeconomic fundamentals.
Using a new data set consisting of six years of real-time exchange-rate quotations, macroeconomic expectations, and macroeconomic realizations, we characterize the conditional means of U.S. dollar spot exchange rates. In particular, we find that announcement surprises produce conditional mean jumps; hence high-frequency exchange-rate dynamics are linked to fundamentals. The details of the linkage are intriguing and include announcement timing and sign effects. The sign effect refers to the fact that the market reacts to news in an asymmetric fashion: bad news has greater impact than good news, which we relate to recent theoretical work on information processing and price discovery.
Variance-ratio tests are routinely employed to assess the variation in return volatility over time and across markets. However, such tests are not statistically robust and can be seriously misleading within a high-frequency context. We develop improved inference procedures using a Fourier Flexible Form regression framework. The practical significance is illustrated through tests for changes in the FX intraday volatility pattern following the removal of trading restrictions in Tokyo. Contrary to earlier evidence, we find no discernible changes outside of the Tokyo lunch period. We ascribe the difference to the fragile finite-sample inference of conventional variance-ratio procedures and a single outlier.
Using high-frequency data on deutschemark and yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation that cover an entire decade. Our estimates, termed realized volatilities and correlations, are not only model-free, but also approximately free of measurement error under general conditions, which we discuss in detail. Hence, for practical purposes, we may treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and persistent dynamics in volatilities and correlations, evidence of long-memory dynamics in volatilities and correlations, and remarkably precise scaling laws under temporal aggregation.
This paper characterizes the volatility in the Japanese stock market based on a 4-year sample of 5-min Nikkei 225 returns from 1994 through 1997. The intradaily volatility exhibits a doubly U-shaped pattern associated with the opening and closing of the separate morning and afternoon trading sessions on the Tokyo Stock Exchange. This feature is consistent with market microstructure theories that emphasize the role of private and asymmetric information in the price formation process. Meanwhile, readily identifiable Japanese macroeconomic news announcements explain little of the day-to-day variation in the volatility, confirming previous findings for US equity markets. Furthermore, by appropriately filtering out the strong intradaily periodic pattern, the high-frequency returns reveal the existence of important long-memory interdaily volatility dependencies. This supports recent results stressing the importance of exploiting high-frequency intraday asset prices in the study of long-run volatility properties of asset returns.
The accessibility of high-performance computing power has always influenced theoretical and applied econometrics. Gouriéroux and Monfort begin their recent offering, Simulation-Based Econometric Methods, with a stylized three-stage classification of the history of statistical econometrics. In the first stage, lasting through the 1960's, models and estimation methods were designed to produce closed-form expressions for the estimators. This spurred thorough investigation of the standard linear model, linear simultaneous equations with the associated instrumental variable techniques, and maximum likelihood estimation within the exponential family. During the 1970's and 1980's the development of powerful numerical optimization routines led to the exploration of procedures without closed-form solutions for the estimators. During this period the general theory of nonlinear statistical inference was developed, and nonlinear micro models such as limited dependent variable models and nonlinear time series models, e.g., ARCH, were explored. The associated estimation principles included maximum likelihood (beyond the exponential family), pseudo-maximum likelihood, nonlinear least squares, and generalized method of moments. Finally, the third stage considers problems without a tractable analytic criterion function. Such problems almost invariably arise from the need to evaluate high-dimensional integrals. The idea is to circumvent the associated numerical problems by a simulation-based approach. The main requirement is therefore that the model may be simulated given the parameters and the exogenous variables. The approach delivers simulated counterparts to standard estimation procedures and has inspired the development of entirely new procedures based on the principle of indirect inference.
We perform an extensive Monte Carlo study of efficient method of moments (EMM) estimation of a stochastic volatility model. EMM uses the expectation under the structural model of the score from an auxiliary model as moment conditions. We examine the sensitivity to the choice of auxiliary model using ARCH, GARCH, and EGARCH models for the score as well as nonparametric extensions. EMM efficiency approaches that of maximum likelihood for larger sample sizes. Inference is sensitive to the choice of auxiliary model in small samples, but robust in larger samples. Specification tests and 't-tests' show little size distortion.
This paper explores the return volatility predictability inherent in high-frequency speculative returns. Our analysis focuses on a refinement of the more traditional volatility measures, the integrated volatility, which links the notion of volatility more directly to the return variance over the relevant horizon. In our empirical analysis of the foreign exchange market the integrated volatility is conveniently approximated by a cumulative sum of the squared intraday returns. Forecast horizons ranging from short intraday to 1-month intervals are investigated. We document that standard volatility models generally provide good forecasts of this economically relevant volatility measure. Moreover, the use of high-frequency returns significantly improves the longer run interdaily volatility forecasts, both in theory and practice. The results are thus directly relevant for general research methodology as well as industry applications.
This paper provides a selective summary of recent work that has documented the usefulness of high-frequency, intraday return series in exploring issues related to the more commonly studied daily or lower-frequency returns. We show that careful modeling of intraday data helps resolve puzzles and shed light on controversies in the extant volatility literature that are difficult to address with daily data. Among other things, we provide evidence on the interaction between market microstructure features in the data and the prevalence of strong volatility persistence, the source of significant day-of-the-week effect in daily returns, the apparent poor forecast performance of daily volatility models, and the origin of long-memory characteristics in daily return volatility series.
A voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic volatility models. While most of these studies have documented highly significant in-sample parameter estimates and pronounced intertemporal volatility persistence, traditional ex-post forecast evaluation criteria suggest that the models provide seemingly poor volatility forecasts. Contrary to this contention, we show that volatility models produce strikingly accurate interdaily forecasts for the latent volatility factor that would be of interest in most financial applications. New methods for improved ex-post interdaily volatility measurements based on high-frequency intradaily data are also discussed.
This paper provides a detailed characterization of the volatility in the deutsche mark–dollar foreign exchange market using an annual sample of five-minute returns. The approach captures the intraday activity patterns, the macroeconomic announcements, and the volatility persistence (ARCH) known from daily returns. The different features are separately quantified and shown to account for a substantial fraction of return variability, both at the intraday and daily level. The implications of the results for the interpretation of the fundamental “driving forces” behind the volatility process is also discussed.
We obtain consistent parameter estimates of continuous-time stochastic volatility diffusions for the U.S. risk-free short-term interest rate, sampled weekly over 1954-1995, using the Efficient Method of Moments procedure of Gallant and Tauchen. The preferred model displays mean reversion and incorporates 'level effects' and stochastic volatility in the diffusion function. Extensive diagnostics indicate that the Cox-Ingersoll-Ross model with an added stochastic volatility factor provides a good characterization of the short rate process. Further, they suggest that recently proposed GARCH models fail to approximate the discrete-time short rate dynamics, while 'Level-EGARCH' models perform reasonably well.
Recent empirical evidence suggests that the interdaily volatility clustering for most speculative returns are best characterized by a slowly mean-reverting fractionally integrated process. Meanwhile, much shorter lived volatility dynamics are typically observed with high frequency intradaily returns. The present article demonstrates, that by interpreting the volatility as a mixture of numerous heterogeneous short-run information arrivals, the observed volatility process may exhibit long-run dependence. As such, the long-memory characteristics constitute an intrinsic feature of the return generating process, rather than the manifestation of occasional structural shifts. These ideas are confirmed by our analysis of a one-year time series of five-minute Deutschemark-U.S. Dollar exchange rates.
The pervasive intraday periodicity in the return volatility in foreign exchange and equity markets is shown to have a strong impact on the dynamic properties of high frequency returns. Only by taking account of this strong intraday periodicity is it possible to uncover the complex intraday volatility dynamics that exists both within and across different financial markets. The explicit periodic modeling procedure developed here provides such a framework and thus sets the stage for a formal integration of standard volatility models with market microstructure variables to allow for a more comprehensive empirical investigation of the fundamental determinants behind the volatility clustering phenomenon.
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 trade-off 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 matrix for estimation, specification testing, and inference procedures. The results provide guidelines that help achieve desirable small-sample properties in settings characterized by strong conditional heteroscedasticity and correlation among the moments.
The paper develops an empirical return volatility-trading volume model from a microstructure framework in which informational asymmetries and liquidity needs motivate trade in response to information arrivals. The resulting system modifies the so-called "Mixture of Distribution Hypothesis" (MDH). The dynamic features are governed by the information flow, modeled as a stochastic volatility process, and generalize standard ARCH specifications. Specification tests support the modified MDH representation and show that it vastly outperforms the standard MDH. The findings suggest that the model may be useful for analysis of the economic factors behind the observed volatility clustering in returns.
Comment on paper by Eric Jacquier, Nicholas Polson and Peter Rossi in the same issue.
This course counts toward the following majors: Finance, International Business
Management of an international business or one exposed to global competition requires knowledge of international financial instruments, markets, and institutions. This course examines these issues from theoretical and applied perspectives. Topics include the nature of foreign exchange risk, the determination of spot and forward exchange rates and interest rates, the returns to foreign investments in external currency and in bond and stock markets, the management of foreign exchange risk with forward markets and foreign currency option markets, and the dynamics of the balance of payments with a focus on understanding international capital flows, country debt, and exchange rate fluctuations.
This course counts toward the following majors: Finance
This course focuses on modern developments in the modeling and pricing of financial derivative securities. Both theoretical and practical estimation issues will be addressed, with the aim of providing students with the necessary background to pursue further academic or practitioner-oriented research in this area. Topics include the pricing of derivatives on equity, foreign exchange, volatility, interest rates, credit risk, and mortgages. A variety of modeling approaches are considered including closed form models, Monte Carlo simulation, and models that admit jumps, stochastic volatility, and non-normal distributions.
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