This 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 autoregressive conditional heteroskedasticity 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.

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.

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 the authors' analysis of a one-year time series of five-minute Deutschemark-U.S. dollar exchange rates.

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.

ABSTRACT 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.

ABSTRACT We propose using model‐free yield quadratic variation measures computed from intraday data as a tool for specification testing and selection of dynamic term structure models. We find that the yield curve fails to span realized yield volatility in the U.S. Treasury market, as the systematic volatility factors are largely unrelated to the cross‐section of yields. We conclude that a broad class of affine diffusive, quadratic Gaussian, and affine jump‐diffusive models cannot accommodate the observed yield volatility dynamics. Hence, the Treasury market per se is incomplete, as yield volatility risk cannot be hedged solely through Treasury securities.

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 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.

Abstract Following the “flash crash” on May 6, 2010, warning signals for impending market stress have been in high demand, yet only the VPIN metric of Easley, López de Prado, and O’Hara (ELO) has claimed success. In addition, ELO find the metric useful in predicting short-term volatility. VPIN involves decomposing volume into active buys and sells. We utilize quotes and trade data to construct an accurate trade classification measure for E-mini S&P 500 futures. Against this benchmark, the ELO Bulk Volume Classification (BVC) scheme is inferior to a standard tick rule. Moreover, VPIN predicts volatility solely because increasing volatility induces systematic classification errors in the BVC procedure. We conclude that VPIN is unsuitable for capturing order flow toxicity or signaling ensuing market turbulence.

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.

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.