This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting daily lower frequency volatility return distributions. Most procedures modeling financial asset volatilities, correlations, distributions rely on restrictive complicated parametric multivariate ARCH or stochastic models, which often perform poorly at frequencies. Use realized constructed from returns, in contrast, permits use traditional time series...

A voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic models. While most of these studies have documented highly significant in-sample parameter estimates pronounced intertemporal persistence, traditional ex-post forecast evaluation criteria suggest that models provide seemingly poor forecasts. Contrary to this contention, we show produce strikingly accurate interdaily forecasts latent factor would be interest...

Using high-frequency data on deutschemark and yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility correlation that cover an entire decade. Our estimates, termed realized volatilities correlations, are not only model-free, but also approximately free measurement error under general conditions, which discuss in detail. Hence, for practical purposes, may treat correlations as observed rather than latent. We do so, characterize their joint...

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 and corresponding nonparametric tests for jumps. Our empirical analyses exchange rates, equity index returns, bond yields suggest that the jump component is both highly distinctly less persistent than continuous component, separating rough moves smooth results significant...

This paper provides a detailed characterization of the volatility in deutsche mark–dollar foreign exchange market using an annual sample five‐minute returns. The approach captures intraday activity patterns, macroeconomic announcements, and persistence (ARCH) known from daily different features are separately quantified shown to account for substantial fraction return variability, both at level. implications results interpretation fundamental “driving forces” behind process is also discussed.

ABSTRACT The paper develops an empirical return volatility‐trading volume model from a microstructure framework in which informational asymmetries and liquidity needs motivate trade response to information arrivals. resulting system modifies the so‐called “Mixture of Distribution Hypothesis” (MDH). dynamic features are governed by flow, modeled as stochastic volatility process, generalize standard ARCH specifications. Specification tests support modified MDH representation show that it...

ABSTRACT This paper extends the class of stochastic volatility diffusions for asset returns to encompass Poisson jumps time‐varying intensity. We find that any reasonably descriptive continuous‐time model equity‐index must allow discrete as well with a pronounced negative relationship between return and innovations. also dominant empirical characteristics process appear be priced by option market. Our analysis indicates general correspondence evidence extracted from daily stylized features...

ABSTRACT 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 dynamics typically observed with high frequency intradaily returns. The present article demonstrates, interpreting as mixture of numerous heterogeneous short‐run information arrivals, process may exhibit long‐run dependence. As such, long‐memory characteristics...

We examine alternative generalized method of moments procedures for estimation a stochastic autoregressive volatility model by Monte Carlo methods. document the existence tradeoff between number moments, or information, included in and quality, precision, objective function used estimation. Furthermore, an approximation to optimal weighting matrix is explore impact estimation, specification testing, inference procedures. The results provide guidelines that help achieve desirable small-sample...

Using a new dataset consisting of six years real-time exchange rate quotations, macroeconomic expectations, and realizations (announcements), we characterize the conditional means U.S. dollar spot rates versus German Mark, British Pound, Japanese Yen, Swiss Franc, Euro. In particular, find that announcement surprises (that is, divergences between expectations realizations, or "news") produce mean jumps; hence high-frequency dynamics are linked to fundamentals. The details linkage intriguing...

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized benchmarks.The procedures, which exploit recent non-parametric asymptotic distributional results in Barndorff-Nielsen and Shephard (2002a) along with new explicitly allowing leverage effects, are both easy-to-implement highly accurate empirically realistic situations.On properly accounting measurement errors forecast evaluations reported...

This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting daily lower frequency volatility return distributions. Most procedures modeling financial asset volatilities, correlations, distributions rely on restrictive complicated parametric multivariate ARCH or stochastic models, which often perform poorly at frequencies. Use realized constructed from returns, in contrast, permits use traditional time series...