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.

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.

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.

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 nodiscernible 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.