Liquidity, Volatility, and Information Asymmetry

We tend to take many things in trading for granted, and I think that liquidity and price discovery are two things that we rarely think about. Let’s imagine for a second, that we trade E-minis but we can’t see real-time price and when we call our broker, she quotes us a 1% bid-ask spread. Obviously, we will not be able to trade like that and make money. This is why price discovery is crucial for practitioners in financial markets(no matter whether they are large hedge funds or retail investors).


In normal market conditions, we are awash with liquidity. Most liquid assets are traded at the minimum bid-ask spread. The reason for the deep liquidity that we see is the market microstructure. We can describe financial markets as a system with different agents which have different utility functions (if you are interested in Agent-Based Models see the link in the appendix). Each agent who’s trading in the market is part of the market microstructure and affects the price and liquidity in the market. There are agents with the sole purpose of “making markets”, which means that they will show liquidity to other agents who are “market takers”. Usually, market makers are banks, dealers, brokers, HFT traders, and other large funds that have sufficiently large portfolios and inventories that allow them to warehouse risk.

There are few ways that we can measure liquidity:

  1. Orderbook depth
  2. Bid-Ask spread
  3. Liquidity Provisions Levels

Orderbook Depth

The first measure that we usually look to gauge the level of an asset’s liquidity is the Orderbook Depth. The more liquid the asset is the larger the size of the top-tier liquidity (the $-value of the best bid/ask), which means that larger orders can be executed at the tightest bid/ask spread. Orderbook depth is not constant and depends on the time of day and general market conditions. Let’s compare the order-book depth during the cash market opening to the order-book depth during the European markets opening (8 am Ldn time)

Cash equity market open:

Source : Bloomberg

European markets open:

Source : Bloomberg

We can clearly see a significant difference between the two, which makes perfect sense, as there are fewer participants who are focused on the US equity market during the European market hours.

Bid-Ask spread

The Bid-Ask spread is probably the most intuitive measure of an asset’s liquidity. As the asset becomes more liquid, the tighter the Bid-Ask spread is going to be. The main reason is that more participants buying and selling the asset so they will try to be more competitive(especially HFT traders, who are trying to use latency arbitrage to buy/sell the asset in different venues).

Liquidity Provisions Levels

While liquidity provisions levels are not directly related to the spot liquidity of assets, they are affecting the market maker's ability to warehouse risk and provide liquidity. Following the 2008 GFC regulators limited the ability of dealers (mainly banks and financial institutions) to hold risky assets on their balance sheet, as they are bounded by “Liquidity Ratio”. The immediate effect of liquidity provisions is that market makers have to hold liquid assets (cash or cash equivalent assets, such as US Treasury bonds) against their risky liabilities. Liquidity provisions essentially limit dealers’ ability to provide liquidity in unfavorable times as they force them to hold more liquid assets just at the time their liabilities and leverage grow (financial crises such as 2008 GFC and March 2020 are good examples…)

Information Asymmetry

So far it seems like the deep liquidity that we see is mainly due to market markers’ willingness to show that liquidity. As we all know, there are no free lunches in financial markets, so there must be a downside to the liquidity and low transaction costs. By now you are probably asking yourself “where is the downside of having such a deep liquidity?”

Well… the reason that we (as market takers) get such deep liquidity (and minimal transaction costs) is that our orders are extremely valuable for market makers (banks, large HFT firms, dealers/brokers, etc..), so they are willing to do anything to get this information…

Think about any app that you have on your phone/desktop (Facebook/Google/Twitter). The reason that those services are free is that our information is much more valuable than any subscription fee. The same goes for our orders and order-book distribution...

This is information asymmetry in essence — The ability of one side to gain more power and use the information to make better trading decisions. When the market markers see the entire order-book picture they pretty much have the treasure map, as they can operate with very little surprises, and occasionally front-run clients’ orders…

Now, you probably think — “that’s not really fair… If they have my orders I’m essentially giving them a free option….” well you are kind of right, but otherwise they will be reluctant to show liquidity (bear in mind that most of the market makers are voluntary makers, which means that they are not obligated to make prices). The thing about information asymmetry is that sometimes market markers tend to go on the borderline between front-running clients’ orders and price manipulation.

Back in the early 2000s two large scandals in FX and LIBOR markets set precedence with regards to what is constitutes as price rigging (and sent few traders to jail), so nowadays dealers really try to avoid that practice, But when you are sitting on the sell-side (i.e., making markets), and you know that there is a large barrier/stop/trigger close to the market it’s pretty easy to “drive” the market to that level.

Dealers will actually go a long way in sourcing information about our trades and orders. Sometimes dealers will show a very tight bid/ask spread just to win a trade as this trade will help them with their price discovery.

Think about the following example: Jim is an expert SPX volatility trader, and his dealer knows that when he buys gamma it tends to perform, and when he sells gamma market just gets stuck in a range. Given that Jim asks for a two-way the dealer has no idea whether he wants to buy or sell, so in order to win the trade, the dealer is inclined to show a choice price just to win the trade.


Now you ask how all this is related to volatility. Well, order-book information and information asymmetry have everything to do with volatility.

When you look at @SqueezeMetrics / @nope_its_lily / @spotgamma you are basically sourcing order-book information using publicly available data for your trading (while the entire market is probably doing the same).

When we, as a collective, act based on that information we essentially create volatility. This phenomenon is reflexivity — a circular relationship between cause and effect. We believe we know the order-book distribution, therefore we base our trading upon that, and essentially drive the market volatility. The fact that there are many different agents in the market exaggerates that volatility, as each agent’s utility function is different, but they all trade in the same market.

Mandelbrot’s Seven States of Randomness touches on that subject in a very elegant way, as he proved that financial markets’ dynamic is constantly oscillating on the scale of randomness (from mild to extreme randomness).

While we like to assume that financial markets follow a normal distribution, given the many different agents, order-book imbalance, and the reflexivity effect, markets move quite quickly from mild randomness to extreme randomness.

Flash crashes are perfect examples of the complex and explosive dynamic that can lead to extreme volatility (More about the Anatomy of Flash Crash)

A good example is the May 6th, 2010 flash crash, which drove the market 6% down in a matter of few minutes, followed by a swift recovery

May 6th, 2010. SPX 1-minute price action

To sum things up — in the complex system which is financial markets, liquidity and price discovery are double-edge swords. While we believe that they make our trading more efficient (due to low transaction costs), they actually make the markets more fragile and create information asymmetry, reflexivity, and eventually turn the markets more volatile and explosive. Our orders are being sourced and used by market makers, which means that we end up paying for the liquidity without realizing that.

Feel free to share your thoughts

Twitter: Harel Jacobson


Agent Based Models in financial markets —




Global Volatility Trading. Python addict. Bloomberg Junkie. Amateur Boxer and boxing coach (RSB cert.)!No investment advice!

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Harel Jacobson

Harel Jacobson

Global Volatility Trading. Python addict. Bloomberg Junkie. Amateur Boxer and boxing coach (RSB cert.)!No investment advice!

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