Browsing by Author "Diebold, FX"
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Item Open Access A Framework for Exploring the Macroeconomic Determinants of Systematic Risk(2005) Andersen, TG; Bollerslev, T; Diebold, FX; Wu, J; Brandt, MThe increasing availability of high-frequency asset return data has had a fundamental impact on empirical financial economics, focusing attention on asset return volatility and correlation dynamics, with key applications in portfolio and risk management. So-called "realized" volatilities and correlations have featured prominently in the recent literature, and numerous studies have provided direct characterizations of the unconditional and conditional distributions of realized volatilities and correlations across different assets, asset classes, countries, and sample periods. For overviews see Andersen et al. (2005a, b). In this paper we selectively survey, unify and extend that literature. 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.Item Restricted "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange"(2003) Andersen, TG; Bollerslev, T; Diebold, FX; Vega, CUsing a new dataset consisting of six years of real-time exchange rate quotations, macroeconomic expectations, and macroeconomic realizations (announcements), we characterize the conditional means of U.S. dollar spot exchange rates versus German Mark, British Pound, Japanese Yen, Swiss Franc, and the Euro. In particular, we find that announcement surprises (that is, divergences between expectations and realizations, or “news”) 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.Item Restricted "Modeling and Forecasting Realized Volatility"(2003) Andersen, TG; Bollerslev, T; Diebold, FX; Labys, PWe provide a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on potentially restrictive and complicated parametric multivariate ARCH or stochastic volatility models. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time-series methods for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark / Dollar and Yen / Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably compared to a variety of popular daily ARCH and more complicated high-frequency models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, produces well-calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.