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Volume Market Liquidity and Funding Liquidity. Brunnermeier , Markus K. Oxford Academic. Google Scholar. High trading volume does not necessarily imply high liquidity. The Flash Crash of May 6, , proved this with painful, concrete examples. In that case, according to the Securities and Exchange Commission SEC , sell algorithms were feeding orders into the system faster than they could be executed.
Volume jumped, but many backlog orders were not filled. According to the SEC, "especially in times of significant volatility, high trading volume is not necessarily a reliable indicator of market liquidity. In the case of exogenous liquidity risk, one approach is to use the bid-ask spread to directly adjust the metric. Let's illustrate with value-at-risk VAR.
The position has positive expected return , also referred to as drift, but as our horizon is daily, we bring our tiny daily expected return down to zero. This is a common practice. So let the expected daily return equal zero. If the returns are normally distributed, then the one-tailed deviate at 5. The full spread represents the cost of a round trip: Buying and selling the stock. But, as we are only interested in the liquidity cost if we need to exit sell the position, the liquidity adjustment consists of adding one-half 0.
In the case of VaR, we have:. In our example,. Liquidity risk can be parsed into funding cash-flow or market asset liquidity risk. Funding liquidity tends to manifest as credit risk , or the inability to fund liabilities produces defaults.
Market liquidity risk manifests as market risk , or the inability to sell an asset drives its market price down, or worse, renders the market price indecipherable. Market liquidity risk is a problem created by the interaction of the seller and buyers in the marketplace. If the seller's position is large relative to the market, this is called endogenous liquidity risk a feature of the seller. If the marketplace has withdrawn buyers, this is called exogenous liquidity risk—a characteristic of the market which is a collection of buyers—a typical indicator here is an abnormally wide bid-ask spread.
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The paper provides an empirical evidence that FL drives ML in each economy. The results are clear, statistically significant and robust. They can be understood as evidence for the importance of the role of the trader's FL for the liquidity of financial assets' markets. The results of the paper have important implications for monetary policy, as well as micro- and macro-prudential regulation. The recent emergence of a new monetary theory is not only the consequence of reaching the zero-lower bound and the need for new types of intervention by central banks, but also of the changes in the monetary transmission and of the emergence of new transmission channels.
One of the many significances of financialization in the past decades is how financial markets play an ever-increasing role not only in acquiring funds, but also in savings.
The line between the activities of financial and non-financial organizations is constantly narrowing as non-financial organizations are equally providing credits, as well as managing financial portfolios and finance their operations by issuing bonds. Due to these, the importance of financial markets' liquidity has appreciated, which made central banks to shift from their role of lenders of last resort to dealers of last resort Mehrling This means that the central bank takes over the role of market maker when other actors in the market cannot or will not do it.
During the times of crisis, it effectively entails stock purchases to help market participants' balance sheet adjustments. It is especially important on the market of those products which serve as collateral during money market operations.
From the perspective of the monetary policy what matters is the fact that the instruments' liquidity influences the smooth functioning of the entire financial network, the amount of new investments and the balance sheets of all businesses. They equally influence the capacity and demand for credit and even the households' wealth.
The literature distinguishes various types of liquidity, but in this study I focus only on two of them: funding liquidity FL and market liquidity ML. FL describes a company's or a bank's ability to mobilize additional financing to its operation quickly at the prevailing market price, while ML entails the pace at which a security can be traded in large volumes without significantly impacting the current price on the market.
It goes without saying that these liquidity concepts are related to each other. There is still no consensus among financial economists regarding the flow of liquidity and the relation between FL and ML. Gromb — Vayanos , Brunnermeier — Pedersen and Mehrling argued that increasing FL would result in an elevated ML because financial institutions provide liquidity of financial assets therefore their liquidity will impact the markets of financial assets. On the other hand, the broad literature of financial flexibility a concept that emerged recently consider financial markets' liquidity to determine the liquidity of the banking system and other companies.
According to financial flexibility, a liquid financial market i. According to the argument, ML implies easier portfolio allocation of the brokerage companies, mutual funds, and on the bond markets — the commercial banks, which helps them to react easier on their FL needs, hence they are able to hoard fewer liquid assets.
In order to maintain the financial stability, we need to understand the sources of ML. Gaining a better comprehension on the drivers and directions of liquidity is not only necessary when financial markets unwind, but also during times of no turbulence in order to gain a sense of the vulnerabilities of system. A finer comprehension of the liquidity flow's direction would allow policymakers to fine-tune the current regulation regime. Empirical papers of the field focus on large, developed market economies, with special attention given to the USA.
In the meantime, research on the components of ML on the emerging markets and open economies remains scarce. Empirically analyzing ML on small, open economies is inevitable because of the increasing financial integration. Small, open economies are integrated into the global financial system and global financial conditions have a growing impact on the domestic economic conditions in these countries.
Changes in the global ML can directly lead to a change in ML on any national financial markets. On the other hand, changes in the global ML can lead to changes in the cost of financing thereby resulting in new conditions of FL therefore impacting ML. From the results it may be implied that due to globalization, the largest domestic financial market actors are also global actors.
Therefore, if they are facing difficulties worldwide that can lead to a change in their way of conducting business locally within the domestic market environment. In this paper, a recursive vector autoregressive model is utilized to empirically analyze the ways of detecting the causality relation between funding and ML of government securities in the cases of four small and open countries in Europe. Literature can divide into empirical and theoretical papers which analyze the determinants and sources of ML.
Amihud — Mendelson managed to publish their seminal paper entitled Dealership Market — Market Making with Inventory. This study describes the behavior and profit maximizing conditions of a price setting monopolistic market maker. The study assumes that the market makers' inventory determines the market conditions for certain securities. Therefore, the paper describes the inventory dependent behavior of market makers by using number of assumptions.
The conclusions of their model are that i market makers have a preferred inventory position which is aimed by the dealer's pricing policy and ii market traders cannot make profit by using information which is also available for the market maker. It confirms Bagehot's results that market makers trade with liquidity motivated traders. O'Hara — Oldfield showed that a market maker's bid-ask spread can be decomposed into a portion for the known limit orders, a risk-neutral adjustment for expected market orders, and a risk adjustment for market order and inventory value uncertainty.
It is demonstrated that inventory has a pervasive role in affecting both the placement and size of the spread. Treynor introduced the term of value-based investor who may be able to fulfill the dealer function, but at a significantly larger bid-asked spread than the market maker.
Comparing to the value-based investor, the dealer has limited ability and willingness to absorb risk therefore the market maker has constraint regarding the position-long or short-he is willing to take. The value-based investors determine the price thus the dealer's price is tied to the value-based investor's price.
The paper explains the asset prices by constant bid-ask spread based on their own liquidity and other risk exposures till the point the fundamental investors take the place of the market maker.
According to Treynor, the larger the long market risk exposure of the market maker the higher the price will be; while the larger the short market risk exposure of market maker the lower the price will be.
Recently the causal relationship between FL and ML has received a lot of attention. Gromb — Vayanos in their frequently cited paper built a theoretical multiperiod model where some traders can trade with two identical risky financial assets in segmented markets. In this case, the traders need to collateralize their positions separately in each market, which results in financial constraints. The financial constraints in the traders FL lead to an optimal level of liquidity being provided.
Their findings show that traders' ability to provide ML depends on the availability of the underlying funding, but the availability of their capital depends on the assets' ML. Their model leads to understand that ML and FL are mutually reinforcing and leading to liquidity spirals.
Adrian — Shin empirically tested broker-dealers' balance sheets and their role in providing liquidity. The paper argues that the availability of liquidity is significantly linked to the fluctuations in the leverage of financial actors. The paper also states that the changes in dealers' balance sheets can result changes in liquidity conditions therefore the financial intermediaries balance sheet can be used as macroeconomic variables to capture monetary policy framework.
A Bank for International Settlements BIS paper, published in , distinguishes two forms of liquidity — official and private liquidity. Official liquidity is defined as the unconditionally available form of liquidity provided by the central banks. While private liquidity is generated by the financial sector. The financial intermediaries provide ML to securities market for FL throughout interbank lending.
There is an interaction between the above-mentioned liquidity categories. In normal times liquidity is generated by international financial actors while during financial turbulences liquidity is provided by the central banks. The paper outlines three major categories which are the drivers of liquidity. These are the i macroeconomic factors, ii other public sector policies, including financial regulation and iii financial factors.
Jean-Pierre Landau , who was the Chair of the Working Group which produced the above-quoted paper, also used the categories of private and public liquidity in his paper. The paper summarizes the behavior and interactions between the two components. Hedegaard empirically investigated the relation between the two liquidity categories by using time-varying margins on future contracts traded on the Chicago Mercantile Exchange CME.
The results showed that higher margins caused lower liquidity. Jylha showed that FL causally affects ML by using an exogenous reduction in margin requirements. In , the U. Securities and Exchange Commission SEC accepted a new methodology for margin requirements for index options but no changes were implemented for the margins of equity options.
As an exogenous shock from market conditions affecting only a part of the market, the method was handled as a quasi-experiment allowing the identification of the causal link between FL and ML. Just a few papers analyzed different dimensions of liquidity on the emerging markets and these papers mainly focused on equity markets.
The 21 countries jurisdictions responding to the questions mentioned four categories driving ML: i macro drivers; ii market microstructure; iii regulation; and iv products and services. The group aims to advance their military, cultural, economic and energy cooperation with each other with strong financial market interdependencies. This, as I argued, resulted in the emergence of a political-economic landscape in each country that made them to share a high degree of structural resemblance.
However, there have also been fine differences. Measurement of liquidity has been a challenging issue as liquidity has various definitions and various characteristics. According to Tirole , liquidity is a complex, multidimensional characteristic of the financial markets, hence a single statistic cannot describe this phenomenon well.
According to Lybek — Sarr , liquidity measures can be classified into four groups, which are strongly related to the characteristics listed above. The classifications of liquidity measures are i transaction cost measures, ii volume-based measures, iii equilibrium price-based measures, and iv market impact measures. The transaction cost measures can be further divided into explicit and implicit transaction cost. The former is related to every expense regarding a trade, including taxes, while the latter captures only the cost of execution.
The most commonly used transaction cost measure is the bid-ask spread, which is known to capture almost all of the costs.
If the transaction costs are lower, the investors prefer to trade with market makers, therefore lower transaction costs are associated with more liquid markets.
Therefore, quarterly bid-ask spreads are applied in the model and it is generated as an average of daily bid-ask spread for each country.
This is illustrated in Fig. Citation: Acta Oeconomica AOecon 70, 4; Measurement of FL is difficult. Jylha argues that TED spread would not even be acceptable for such an analysis. Drehmann — Nikolaou consider banks' bidding aggressiveness at the European Central Bank's auctions as a good proxy. The deposit ratio shows the proportion of deposits and short-term obligations for the banking system. The larger the ratio, the larger is the FL risk as the banking system faces a larger refinancing risk.
This ratio can be viewed as an FL metrics. However, in order to maintain their financial flexibility banks may not fulfill their full credit capacity. They might call for more credit when they need to finance their growth or finance a bank run.
That is why a ratio of deposit ratio and liquid assets ratio is applied. The liquid assets in proportion of the deposit ratio can demonstrate the extent to which financial institutions adjusted their asset side flexibility to their FL risk. The variable is very similar to the maturity mismatch calculations, which are commonly used to capture the FL risk of financial institutions see BIS working papers of de Haan — van den End ; Bai As Fig.
It is important to mention that, because of the introduction of Basel 3, and thus, the Liquidity Coverage Ratio and Net Stable Funding Ratio , the time series is not entirely homogeneous in terms of the systemic behavior. Time series of funding liquidity proxy for the analyzed countries Source : Author's calculation based on central banks data. Macro variables were also considered in the models based on the literature.
GDP was considered as a main macro driver for markets as a proxy that is primary related to macroeconomic performance.
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