Liberty Street Economics

« | Main | »

February 17, 2016

High‑Frequency Cross‑Market Trading and Market Volatility

LSE_liquidity_460x288px_08

The close relationship between market volatility and trading activity is a long-established fact in financial markets. In recent years, much of the trading in U.S. Treasury and equity markets has been associated with nearly simultaneous trading between the leading cash and futures platforms. The striking cross-activity patterns that arise in both high-frequency cross-market trading and related cross-market order book changes in U.S. Treasury markets are also witnessed in other asset classes and naturally lead to the question that we investigate in this post of how the cross-market component of overall trading activity is related to volatility.


The chart below displays a measure of cross-market activity for the ten-year Treasury note cash and futures markets (left column) and the S&P 500 cash and futures markets (right column) across different millisecond offsets. Of note is the pronounced asymmetry of the spike in the measure at +5 milliseconds for the S&P 500 compared with the ten-year U.S. Treasury. The much higher spike for the positive 5 millisecond offset is consistent with the often-cited dominant role played by the S&P futures market in price discovery. Leaving this asymmetry aside, the spikes in cross-market activity on October 15 and 16, 2014, stand out as being well-aligned with the heightened volatility and trading observed on those days. Cross-market trading and quoting activity thus appears to be related to variations in market volatility, which can create (short-lived) dislocations in relative valuations as market participants respond to news about fundamentals or market activity itself.

LSE_2016_cross-market-trading_schaumburg_ch1_art

We further demonstrate empirically that the peak number of cross-active milliseconds (the largest cross-activity measure across all offsets expressed as a count rather than a fraction of total activity) comoves more strongly with market volatility than generic market-activity proxies such as trading volume and the number of transactions. This pattern is consistent with a positive feedback effect by which an increase in volatility can spur additional trading activity by creating cross-market trading opportunities. This observation stands in contrast to the more conventional view in the finance and economics literature which holds that trading activity predominantly influences volatility but not vice versa.

Intraday Patterns in Volatility and Cross-Market Trading Activity

We analyze the link between cross-market activity and volatility in both U.S. Treasuries (ten-year Treasury note cash and futures) and equities (S&P 500 E-mini and SPY ETF) over the six-month period from July 1 to December 31, 2014. We measure cross-market activity on each trading day as the peak number of cross-active milliseconds across all offsets and restrict attention to the most active electronic U.S. trading hours for each pair: from 7:00 to 16:00 ET for the ten-year Treasury note and from 9:30 to 16:00 ET for the S&P 500. While we carry out the analysis at millisecond frequency, it trivially generalizes to any other frequency with adequate time resolution for meaningful cross-activity measurements at different offsets.

The panel of charts below shows that for both the ten-year Treasury note and S&P 500 the prevailing intraday volatility pattern is matched very closely by the diurnal pattern in cross-market activity as measured by the peak number of cross-active milliseconds between the cash and futures markets. The biggest volatility spikes for U.S. Treasuries occur at 8:30, 10:00, 13:00, and 14:00 ET around known times of news announcements, Treasury auctions, and the release of Federal Open Market Committee announcements and meeting minutes. For the S&P 500, only the spikes at 10:00 and 14:00 ET stand out (to a lesser degree). For U.S. Treasuries, there is also a notable peak around 15:00 ET (corresponding to the CME market close for all pit-traded interest rate options).

LSE_2016_cross-market-trading_schaumburg_ch2_art

Furthermore, in terms of correlation, the peak number of cross-active milliseconds is tracking the intraday volatility pattern somewhat more closely than either trading volume or the number of trades. However, the tight range of most observed values within negative and positive one (excluding the extremes) suggests that interday as opposed to intraday variation may provide a better measure of the degree to which the different activity series relate to volatility.

Day-to-Day Variations in Volatility and Cross-Market Trading Activity

The panel of charts below shows changes in daily logarithmic realized volatility plotted against changes in each daily logarithmic activity measure over the July 1 to December 31, 2014, sample period. For both the ten-year Treasury (left column) and the S&P 500 (right column) markets, the day-to-day changes in volatility appear to be more closely correlated with the day-to-day changes in the peak number of cross-active milliseconds (bottom row) than with the changes in the number of trades (middle row) or trading volume (top row).

Moreover, the peak number of cross-active milliseconds often appears to crowd out both trading volume and the number of trades if included jointly as regressors for volatility. This result is quite remarkable since it establishes that the peak number of cross-active milliseconds subsumes both trading volume and the number of trades in terms of information content about volatility. It is also worth highlighting that while October 15 (in red) and October 16 (in blue) are known to have exhibited extreme volatility, they are not large outliers in terms of the strong linear relationship observed between changes in volatility and changes in the peak number of cross-active milliseconds (bottom row).

LSE_2016_cross-market-trading_schaumburg_ch3_art

Evolution of the Relationship between Volatility and Cross-Market Trading Activity

To better assess the extent to which market volatility has become more closely associated with high-frequency cross-market trading activity, the next panel of charts juxtaposes the above historical correlations between trading activity and volatility for the ten-year U.S. Treasury (left column) and the S&P 500 (right column) markets each year from January 1, 2004, to September 30, 2015. In particular, measuring the peak number of cross-active milliseconds day by day and correlating logarithmic differences of the daily measures during each twelve-month period limits the impact of secular trends in latency and trading practices over the past decade resulting from technological improvements and the related evolution in high-frequency trading. The charts below thus strongly indicate that with the rise in high-frequency trading in recent years, cross-market activity as measured by the peak number of cross-active milliseconds between the cash and futures markets has typically been more tightly linked to volatility than standard activity measures such as overall trading volume or the number of trades.

LSE_2016_cross-market-trading_schaumburg_ch4_art

Summary

We document that a measure of cross-market activity expressed as the peak number of cross-active milliseconds (across all offsets) is more strongly linked to volatility than trading volume and the number of trades in both U.S. Treasury and equity markets. This observation may reflect the fact that volatility can create brief dislocations in relative values spurring bursts of cross-market activity by high-frequency traders seeking to exploit these trading opportunities. When liquidity is ample, measures of cross-market activity can therefore capture incremental information about market volatility beyond traditional measures of overall market activity such as trading volume and the number of transactions. Our findings strongly suggest the need to study activity in arbitrage-linked markets jointly rather than in isolation in order to account for the significant volatility-related surges in cross-market trading observed in the data.

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.



Dobrislav Dobrev is a senior economist at the Board of Governors of the Federal Reserve System.

Ernst_schaumburgErnst Schaumburg is the head of analytical development and an assistant vice president in the Federal Reserve Bank of New York’s Integrated Policy Analysis Group.

About the Blog

Liberty Street Economics features insight and analysis from New York Fed economists working at the intersection of research and policy. Launched in 2011, the blog takes its name from the Bank’s headquarters at 33 Liberty Street in Manhattan’s Financial District.

The editors are Michael Fleming, Andrew Haughwout, Thomas Klitgaard, and Asani Sarkar, all economists in the Bank’s Research Group.

Liberty Street Economics does not publish new posts during the blackout periods surrounding Federal Open Market Committee meetings.

The views expressed are those of the authors, and do not necessarily reflect the position of the New York Fed or the Federal Reserve System.

Economic Research Tracker

Image of NYFED Economic Research Tracker Icon Liberty Street Economics is available on the iPhone® and iPad® and can be customized by economic research topic or economist.

Economic Inequality

image of inequality icons for the Economic Inequality: A Research Series

This ongoing Liberty Street Economics series analyzes disparities in economic and policy outcomes by race, gender, age, region, income, and other factors.

Most Read this Year

Comment Guidelines

 

We encourage your comments and queries on our posts and will publish them (below the post) subject to the following guidelines:

Please be brief: Comments are limited to 1,500 characters.

Please be aware: Comments submitted shortly before or during the FOMC blackout may not be published until after the blackout.

Please be relevant: Comments are moderated and will not appear until they have been reviewed to ensure that they are substantive and clearly related to the topic of the post.

Please be respectful: We reserve the right not to post any comment, and will not post comments that are abusive, harassing, obscene, or commercial in nature. No notice will be given regarding whether a submission will or will
not be posted.‎

Comments with links: Please do not include any links in your comment, even if you feel the links will contribute to the discussion. Comments with links will not be posted.

Send Us Feedback

Disclosure Policy

The LSE editors ask authors submitting a post to the blog to confirm that they have no conflicts of interest as defined by the American Economic Association in its Disclosure Policy. If an author has sources of financial support or other interests that could be perceived as influencing the research presented in the post, we disclose that fact in a statement prepared by the author and appended to the author information at the end of the post. If the author has no such interests to disclose, no statement is provided. Note, however, that we do indicate in all cases if a data vendor or other party has a right to review a post.

Archives