MTS Alpha

Delivers new alpha

Andy Webb, Consultant
Originally published on 16 September 2022

The relationship between cash and futures markets in government bonds is long-established and has accordingly been covered in considerable detail by academics, regulators and central banks. However, a recent academic paper, “Price and Liquidity Discovery in European Sovereign Bonds and Futures”,1 sheds new light on the relationship. It does so by analysing high frequency data for three European government bond markets to provide additional insights into the links between the cheapest to deliver bonds and the closest futures contract. In conjunction with the availability of a new cheapest to deliver MTS data feed, this has positive implications for those trading bond futures outright, as well as basis arbitrageurs.

The research

The paper’s analysis, which covers Italy, France and Germany for the period 2019-2021, was split into three sub-periods to investigate changes in liquidity discovery caused by the COVID-19 pandemic. The research team scrutinised the cash/futures relationship in terms of both price and liquidity, using a combination of MTS tick by tick data for the cash market and Eurex tick by tick futures data. Data was condensed into five-minute breakpoints for analysis (so proposals that were alive across a breakpoint were included).

For those who only trade government bond futures, the research reveals potential edges for both price and liquidity discovery.

Various analytical techniques were used, including cointegration testing (for investigating price discovery) and vector autoregressions (for liquidity discovery) in the context of the arbitrage relationship between cash and futures. This resulted in six primary conclusions:

  • Price discovery occurs on the futures market, where prices react more strongly to changes in the executable basis
  • The basis gradually converges to zero as delivery dates approach and uncertainty is resolved
  • There was strong empirical support for the presence of liquidity spill overs, with forecasting signals mostly originating in the cash market
  • There were regular illiquidity spikes in the futures market before delivery dates
  • Limits to arbitrage are significant even in the most liquid European markets, as the basis remains different from its equilibrium level for several consecutive days
  • Liquidity is strongly predictable and cross-market arbitrage connections add significant explanatory power

New alpha now…

These conclusions suggest some important new alpha opportunities may be available. However, a key requirement in realising these is to have access to the same granularity of data as used in the research, but in real time. In the past this would have been a stumbling block, because while high granularity/frequency historical and real time data has been widely available for bond futures for some time, the same has not been true for cheapest to deliver cash bond data. This potential issue is fortunately now resolved by the recent introduction of MTS Alpha2. As a result, the opportunities revealed in the research, rather than merely remaining theoretical, become immediately practical – both for those currently trading only futures, as well as for basis arbitrageurs. 

Futures traders

For those who only trade government bond futures, the research reveals potential edges for both price and liquidity discovery. By analysing price data for bond future deliverables (such as the Alpha feed) in conjunction with the corresponding futures data, it becomes possible to anticipate relative price discovery action. As the authors of the paper observe: “…a temporary divergence between the two legs of a carry trade has predictive information on which market will react more strongly”. A similar scenario applies to liquidity discovery, where the authors concluded that: “…the VAR3 evidence on liquidity discovery is suggestive of the important role of cash markets to forecast futures contracts’ liquidity”. In both cases, these additional insights could be used to support worthwhile enhancements to existing trading models, as well as the creation of new ones.

The opportunities revealed in the research, rather than merely remaining theoretical, become immediately practical – both for those currently trading only futures, as well as for basis arbitrageurs. 


The paper focused on the arbitrage relationship in order to investigate the dynamics of price and liquidity discovery, so unsurprisingly the predictive benefits of this relationship will also be of use to arbitrageurs. For them, feeds such as MTS Alpha can be used to enhance the performance of existing trading models. And, as for those only trading futures, an ultra-low latency cheapest to deliver bond feed may also unlock new previously inaccessible arbitrage opportunities. Ultimately, these changes might additionally encourage some existing futures-only participants to extend their operations into arbitrage. 

…and in the future

While the availability of a new ultra-low latency cheapest to deliver data feed could be leveraged to enhance the performance of existing trading models and programs, it is also feasible that it will uncover entirely new alpha opportunities too.

Higher frequency

The research paper primarily focused on five-minute breakpoints (daily data was also used for comparison and robustness), but the fact that extremely granular data is now available for both markets opens the door to exploring lower timeframes as well. The availability of ultra-low latency data feeds for futures markets has already made additional alpha accessible there. However, now the entire arbitrage relationship is made transparent at this microstructure level, it seems reasonable to assume that further micro-opportunities may emerge – both for outright futures, as well as arbitrage.

Existing markets, new liquidity

The availability of a new high frequency cash feed for European government bond deliverables is likely to have a positive impact on the liquidity of already-liquid futures markets. However, it might also improve the position in currently illiquid ones. A case in point is the futures contract on the Spanish 10-year bond (BONO), which has significantly lagged behind comparable French, German and Italian sovereign bond futures in terms of market depth. Improved price and liquidity transparency in the cash market could potentially ameliorate this and stimulate greater futures activity.

Felicitous timing

It must be said that at a macro level, the release of MTS Alpha is fortuitously timed. As a recent paper from TMX4 highlights, several years of low rates and flat bond yield curves have seen the importance of cheapest to deliver changes diminish for many investors. As rates are now rising and yield curves steepening, the frequency of cheapest to deliver switches is likely to increase, particularly if these changes persist or accelerate. This in turn is likely to increase the embedded quality option in short futures positions, as well as boosting trading opportunities more generally.


By having access to microstructure level data for European sovereign debt futures and cash, the authors of the paper have been able to draw some robust and interesting conclusions about price and liquidity discovery in both markets. Furthermore, the fact that ultra-low latency cheapest to deliver cash data is now readily available to all market players, alongside comparable futures data, means that the implications of these conclusions are not merely hypothetical. They are now tradable. 


Andy Webb has been writing about a broad range of topics relating to financial markets and technology for 30+ years. He also provides consulting services to hedge funds, prop desks and CTAs on systematic, automated and AI/machine learning alpha models, including programming those models in specialised environments. 

  2. MTS Alpha is an un-netted price feed (a subset of the MTS Live feed) that includes all price updates received on bond future deliverables that hit the MTS Cash market. The feed, which has a c600 microsecond update frequency, includes orders as well as trades.
  3. Vector autoregression