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i-P-A has been built by seasoned investment performance measurement and portfolio risk practitioners with the aim to help further the investment analysis profession and also to help those using our outputs or services. This area of asset management is linked to so many others that i-P-A believe we can add value, colour and life
meta-tags and key words
performance analyst,client reporting manager,risk analyst,head of performance, head of risk,recruitment,performance,investment performance,sharpe ratio,information ratio,investment risk,GIPS,performance analysis,sharpe,ipa,i-p-a,i-performance-analysis,analytics,iperformanceanalysis,TWR,MWR,IIR,BAI,performance measurement,beta,alpha,tracking error,standard deviation,GIPS,CFA institute,CIPM,money weighted returns, time weighted returns,attribution,performance theory,attribution theory,global investment performance standards,portfolio analytics software,attribution analysis,performance and attribution,performance measurement,performance measurement software,aimr,aimr pps,aimr-pps,analytic solutions,anthony howland,carl bacon,colin morrison,david spaulding,spaulding,journal of performance,asset management software,asset management industry,attribution,attribution reporting,attribution tools,client verifiers,data analysis,data hub,data migration,data reconciliation,financial software,fund management software,geometric attribution,industry standards compliance,performance software,performance tool,personal portfolio,portfolios,portfolio analysis,portfolio analytics,portfolio management,pps composites,price performance management systems,aimr gips-pps,performance presentation standards,risk management,tracking error,transaction based analysis
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Dario Cintioli
Head of Risk, StatPro
MARKET LIQUIDITY RISK: A SCENARIO BASED APPROACH
Head of Risk, StatPro
MARKET LIQUIDITY RISK: A SCENARIO BASED APPROACH
This paper explains the StatPro approach for measuring Liquidity Risk. The traditional problem of Liquidity Risk is that the data needed for calibrating these models is only available for liquid instruments, trading on a regular basis and for which books of bid/ask and volumes are available. For this reason the current approaches to measuring Liquidity Risk fail providing any indication for the most opaque and illiquid instruments, or where the measurement of Liquidity Risk is mostly needed.
StatPro has introduced a new approach based on liquidity scenarios, which is universal, because it covers potentially any financial asset, from equities, to bonds, to OTC derivatives under a homogeneous and consistent approach.
The Liquidity Risk measure is divided into six different components. The most important component re-builds, with a quantitative approach based on observed market data, the fair value bid and asks of all the financial instruments that can be priced via an arbitrage-free pricing function, providing a solid and consistent benchmark of Liquidity Risk.
Liquidity Risk
What is Liquidity Risk? We can provide at least two definitions for it.
Funding Liquidity Risk. This definition refers to the Asset Liability Management (ALM) of an institution - normally a bank - identifying the gaps in the funding of the institution’s assets. E.g. in a bank there is usually a funding gap as the liabilities contain short-term deposits in large part against assets that invest in longer term horizons. Funding gaps generate a funding risk, the risk of rolling the short term funding at growing costs or even the risk of not being able to roll/over the shorter term liabilities.
Market Liquidity Risk. This is the risk of losing a certain amount of money when liquidating one or more positions in a portfolio. In financial terms, the loss is generated by the difference between the price at which the financial asset is marked and the price at which it can be sold.
This paper focuses on Market Liquidity Risk.
When all financial assets that lie in our portfolios have quotes on market bid and ask prices with their respective volumes (including book volumes), computing Liquidity Risk is straightforward.
Having the book of bids and asks at disposal, we can measure with objectivity and precision how much we would be losing by liquidating our positions, in one or more units of time.
The current approaches for measuring Liquidity Risk are centered around bid/ask and volumes.
The most popular measure is still the number of days needed for liquidating a position, obtained by dividing the position size by the daily trading volume.
However, there are a number of problems related to this approach:
a.The Liquidity Risk Paradox. Information on bid/asks, book and volumes is only available for liquid instruments, i.e. financial assets that trade on a daily basis in fairly transparent markets or trading venues. This information is not available for the most opaque and illiquid instruments. In other words, the financial information required to calibrate the “traditional” models of measurement of liquidity risk is not available for the instruments where a measure of this risk is mostly needed.
b.Proliferation of Trading Venues. The recent past has seen a proliferation of trading venues for many financial instruments including equities. Trades on stocks were traditionally concentrated in one main market; today their trading is split around multiple venues, spreading the information of volumes, books, bid and asks. This scattering of information opens new questions on the traditional approaches. Do we have the information on consolidated volumes across the different trading places? Have we access to all the venues where one stock is traded once we decide to liquidate the position? Should we take into account the consolidated volumes or only the volumes and bid/asks of the trading venues we have access to?
c.OTC Volumes vs Trading Venues Volumes. The last 10 years have been also accompanied by an increase of electronic trading venues for fixed income products. Bonds are now often traded electronically and information on traded volumes is available in open platforms. The issue is that the OTC market retains most of the liquidity in the fixed income space and the information on bid and asks on a trading venue may even be misleading: e.g. assume we have a bond issue that trades on average 100,000 $ in a regulated market but that in the OTC market you can find easily quotes for multiples of these volumes; if your position on the bond counts in millions the information conveyed by the regulated market becomes irrelevant.
These issues constitute a structural impediment to a coherent and universal approach to the measurement of Liquidity Risk. The traditional approaches will constantly fail to deliver any information on illiquid bonds, OTC derivatives, certificates, leaving us uncovered where the problem of liquidity mostly hits.
Is there an alternative? Can we design a model framework that is at the same time:
-universal, covering all financial instruments;
-coherent, applying a consistent methodology to all instruments;
-objective, using a quantitative method based on observable market data to build in full or in part the Liquidity Risk measure.
These questions have inspired our research on the topic and have, in a way, driven it. The remainder of the paper explains our approach to measuring Liquidity Risk and how it complies with the requirements described above.
The complete 11-page whitepaper by Dario Cintioli is available free of charge at StatPro’s website http://www.statpro.com/resources/whitepapers/liquidity_risk.aspx
www.statpro.com
StatPro has introduced a new approach based on liquidity scenarios, which is universal, because it covers potentially any financial asset, from equities, to bonds, to OTC derivatives under a homogeneous and consistent approach.
The Liquidity Risk measure is divided into six different components. The most important component re-builds, with a quantitative approach based on observed market data, the fair value bid and asks of all the financial instruments that can be priced via an arbitrage-free pricing function, providing a solid and consistent benchmark of Liquidity Risk.
Liquidity Risk
What is Liquidity Risk? We can provide at least two definitions for it.
Funding Liquidity Risk. This definition refers to the Asset Liability Management (ALM) of an institution - normally a bank - identifying the gaps in the funding of the institution’s assets. E.g. in a bank there is usually a funding gap as the liabilities contain short-term deposits in large part against assets that invest in longer term horizons. Funding gaps generate a funding risk, the risk of rolling the short term funding at growing costs or even the risk of not being able to roll/over the shorter term liabilities.
Market Liquidity Risk. This is the risk of losing a certain amount of money when liquidating one or more positions in a portfolio. In financial terms, the loss is generated by the difference between the price at which the financial asset is marked and the price at which it can be sold.
This paper focuses on Market Liquidity Risk.
When all financial assets that lie in our portfolios have quotes on market bid and ask prices with their respective volumes (including book volumes), computing Liquidity Risk is straightforward.
Having the book of bids and asks at disposal, we can measure with objectivity and precision how much we would be losing by liquidating our positions, in one or more units of time.
The current approaches for measuring Liquidity Risk are centered around bid/ask and volumes.
The most popular measure is still the number of days needed for liquidating a position, obtained by dividing the position size by the daily trading volume.
However, there are a number of problems related to this approach:
a.The Liquidity Risk Paradox. Information on bid/asks, book and volumes is only available for liquid instruments, i.e. financial assets that trade on a daily basis in fairly transparent markets or trading venues. This information is not available for the most opaque and illiquid instruments. In other words, the financial information required to calibrate the “traditional” models of measurement of liquidity risk is not available for the instruments where a measure of this risk is mostly needed.
b.Proliferation of Trading Venues. The recent past has seen a proliferation of trading venues for many financial instruments including equities. Trades on stocks were traditionally concentrated in one main market; today their trading is split around multiple venues, spreading the information of volumes, books, bid and asks. This scattering of information opens new questions on the traditional approaches. Do we have the information on consolidated volumes across the different trading places? Have we access to all the venues where one stock is traded once we decide to liquidate the position? Should we take into account the consolidated volumes or only the volumes and bid/asks of the trading venues we have access to?
c.OTC Volumes vs Trading Venues Volumes. The last 10 years have been also accompanied by an increase of electronic trading venues for fixed income products. Bonds are now often traded electronically and information on traded volumes is available in open platforms. The issue is that the OTC market retains most of the liquidity in the fixed income space and the information on bid and asks on a trading venue may even be misleading: e.g. assume we have a bond issue that trades on average 100,000 $ in a regulated market but that in the OTC market you can find easily quotes for multiples of these volumes; if your position on the bond counts in millions the information conveyed by the regulated market becomes irrelevant.
These issues constitute a structural impediment to a coherent and universal approach to the measurement of Liquidity Risk. The traditional approaches will constantly fail to deliver any information on illiquid bonds, OTC derivatives, certificates, leaving us uncovered where the problem of liquidity mostly hits.
Is there an alternative? Can we design a model framework that is at the same time:
-universal, covering all financial instruments;
-coherent, applying a consistent methodology to all instruments;
-objective, using a quantitative method based on observable market data to build in full or in part the Liquidity Risk measure.
These questions have inspired our research on the topic and have, in a way, driven it. The remainder of the paper explains our approach to measuring Liquidity Risk and how it complies with the requirements described above.
The complete 11-page whitepaper by Dario Cintioli is available free of charge at StatPro’s website http://www.statpro.com/resources/whitepapers/liquidity_risk.aspx
www.statpro.com
