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.

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.

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 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.

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.

In Andersen and Bondarenko (2014), using tick data for S&P 500 futures, we establish that the VPIN metric of Easley, López de Prado, and O'Hara (ELO), by construction, will be correlated with trading volume and return volatility (innovations). Whether VPIN is more strongly correlated with volume or volatility depends on the exact implementation. Hence, it is crucial for the interpretation of VPIN as a harbinger of market turbulence or as a predictor of short-term volatility to control for current volume and volatility. Doing so, we find no evidence of incremental predictive power of VPIN for future volatility. Likewise, VPIN does not attain unusual extremes prior to the flash crash. Moreover, the properties of VPIN are strongly dependent on the underlying trade classification. In particular, using more standard classification techniques, VPIN behaves in the exact opposite manner of what is portrayed in ELO (2011a, 2012a). At a minimum, ELO should rationalize this systematic reversal as the classification becomes more closely aligned with individual transactions.ELO (2014) dispute our findings. This note reviews the econometric methodology and the market microstructure arguments behind our conclusions and responds to a number of inaccurate assertions. In addition, we summarize fresh empirical evidence that corroborates the hypothesis that VPIN is largely driven, and significantly distorted, by the volume and volatility innovations. Furthermore, we note there is compelling new evidence that transaction-based classification schemes are more accurate than the bulk volume strategies advocated by ELO for constructing VPIN. In fact, using perfect classification leads to diametrically opposite results relative to ELO (2011a, 2012a).