# Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets

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## Abstract

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## 1. Introduction

## 2. Liquidity in Systemic Risk

- Market infrastructures are efficient and transparent, leading to low search and transactions costs;
- Market participants have easy access to funding;
- Risk appetite is abundant;
- A diverse investor base ensures that factors affecting individual investors do not translate into broader price volatility.

#### 2.1. Systemic Illiquidity: Research and Existing Measures

#### 2.2. Measures of Systemic Illiquidity—Overview

#### 2.3. Measures of Systemic Illiquidity—Empirical Application Possibilities

- Developing (frontier or emerging) markets in terms of the structure (banking sector dominance, with traditional banking products), maturity (affecting data availability and historical data span), and depth (including the limited variety of markets, the size of the stock market, and the numbers and types of existing financial instruments);
- Relatively well-developed economies in terms of the stability of prices (relatively low and stable inflation), currency, capital flows, and monetary policy targets and tools.

## 3. Parametric Models and Their Potential in Systemic Liquidity Analysis

## 4. Empirical Application of the Systemic Illiquidity Noise-Based Measure

#### 4.1. Methodology

**C**is constructed, for which the elements correspond to the payments.

#### 4.2. Data and Empirical Results

#### 4.3. COVID-19 Pandemic

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

## Appendix B

Measurement Output | Authors | Short Description |
---|---|---|

Liquidity factor | Pastor and Stambaugh (2003) | A measure of market liquidity computed as the equally weighted average of the liquidity measures of individual stocks, using daily data. Specifically, the liquidity measure for a stock is the ordinary least squares regressed function of quantities of the daily returns on this stock in a given month, its volume, and the value-weighted market return. The measure relies on the principle that order flow induces greater return reversals when liquidity is lower, viewing volume-related return reversals as arising from liquidity effects. |

A set of interpretable parameters | Getmansky et al. (2004) | The proposal to use autocorrelation of returns of hedge funds as a proxy of their liquidity; the first-, second-, and third-order autocorrelations for each hedge fund’s returns are computed using an econometric model of return smoothing coefficients and used as a proxy for quantifying illiquidity exposure—the less liquid the fund, the more serial correlation is observed. |

Broader hedge-fund-based systemic risk measures | Chan et al. (2006) | A set of three measures quantifying the hedge funds’ impact on systemic risk by examining the risk/return profiles of hedge funds, using returns and sizes data, at the individual and aggregate levels in relation to the investment risk they bear: autocorrelation-based measure of illiquidity exposures, a liquidation probability-based measure, and the regime-switching-based model quantifying the aggregate distress level in the hedge fund sector. |

Five measures of contagion potential | Billio et al. (2012) | A structured approach to measure systemic risk with indicators based on illiquidity (quantified by autocorrelation) and correlation, using principal component analysis (indicating the degree of assets commonality), regimeswitching models, Granger causality tests (indicating the direction of propagation of systemic triggers), and network diagrams (visualizing the connectedness via directional networks), focused on detecting of interdependence between banks, brokers, insurers, and hedge funds, based on statistical relations among their market returns. This way, the authors quantify the potential contagion effects in the analyzed financial system. |

A system of liquidity risk charges (LRCs) | Perotti and Suarez (2011) | Pigouvian charges are calculated per unit of refinancing risk-weighted liabilities based on a vector of additional systemic factors (such as size and interconnectedness) in a given period. The weighting function is decreasing and smooth to avoid regulatory arbitrage, which could distort market rates. The model is aimed at making banks internalize negative systemic effects of fragile funding strategies, but the computed size of charges may be used as a tool for quantifying liquidity risk showing which institutions generate more risk for the financial system. |

Contrarian strategy liquidity measure (CSL) | Khandani and Lo (2011) | A proposal to apply mean-reversion equity market strategy (buying losers and selling winners over 5 to 60 min lagged returns) to proxy the market-making (i.e., liquidity-provisioning) profits and to obtain equity market liquidity measure by observing the performance of this trading strategy. The authors showed that when it does very well, there is less liquidity in the market, and vice versa. |

Price-impact liquidity measure (PIL) | An inverse proxy of liquidity, in which liquidity is measured with a linear-regression estimate of the volume required to move the price of a security by one dollar; i.e., higher values of lambda imply lower liquidity and market depth. The aggregate measure of market liquidity (PIL) is computed as the daily cross-sectional average of the estimated price-impact coefficients. | |

Systemic Liquidity Risk Index (SLRI) | Severo (2012) | The SLRI is calculated by integrating the deviations of the following basis spreads: covered interest parity, the on-the-run versus the off-the-run interest-rate spread on government bonds, and the interest-rate spread between the overnight index swap (OIS) and short-term government bonds and the CDS basis spread, to represent the degree of their comovement first component score from a principal component analysis (based on historical time-series data) is used. |

Liquidity Mismatch Index (LMI) | Brunnermeier et al. (2014) | Measures the difference between the cash-equivalent future values of the assets and liabilities of a bank; it utilizes the cash-equivalent value, which is the product of the asset or liability current value, multiplied by the liquidity weight (positive for assets, negative for liabilities), which depends on an assumed stress scenario, Value-at-Liquidity-Risk, defined as the quantile of worst losses (e.g., 5%), and the Expected Liquidity Loss, which corresponds to the average of the liquidity losses beyond this threshold. The authors proposed to use LMI to identify the most systemically important financial institutions. |

Systemic risk-adjusted liquidity (SRL) model | Jobst (2014) | Estimates the probability and severity of joint liquidity events; i.e., instances of banks jointly breaching their Net Stable Funding Ratios. Estimation process: 1. The components of the NSFR are valued at market prices in order to generate a time-varying measure of funding risk relative to prudential liquidity standards. 2. Aggregate cash flow implications of changes to liquidity risk are modeled as a put option to estimate losses expected from insufficient stable funding. 3. Individually estimated liquidity risk net exposures are aggregated via a multivariate distribution to determine the probabilistic measure of joint liquidity shortfalls on a system-wide level. |

Systemicness | Greenwood et al. (2015) | A linear model of fire-sale-induced liquidity crises, computing banks’ equity shock exposures to system-wide deleveraging and to spillovers induced by individual banks; systemicness is a (quantity) measure of a bank’s contribution to financial sector fragility, proportional to its size, leverage, and connectedness (owning large and illiquid asset classes to which other banks are also highly exposed).The key assumption is that banks target a given level of leverage, and this implies asset sales when leverage grows beyond the target. It allows the measurement of how the distribution of banks’ leverage and risk exposures contributes to systemic risk. |

Cumulative Distance to Default (CDD) | Karkowska (2015) | The distance-to-default measure is a market-based measure of credit risk based on Merton’s model, in which the equity of a firm is modeled as a call option on the value of its assets. The exercise price is equal to the value of the liabilities (the firm defaults when its assets’ value falls below its debt face value). For implementation, the face value of debt is assumed to be equal to the sum of short-term liabilities and half the long-term liabilities from the balance-sheet data. The model is calibrated using the analyzed institution’s market value and its equity price volatility. Karkowska used this method to derive the DD value for each institution forming the studied banking system and aggregated the data to obtain a systemic risk measure equal to the total probability of default of all the studied institutions. |

Aggregate vulnerability (AV) and illiquidity concentration | Duarte and Eisenbach (2019) | An extension of the systemicness measure that includes the panel analysis tracking vulnerabilities over time. It takes banks’ leverage, asset holdings, asset liquidation behavior, and the price impact of liquidating assets in the secondary market as given, and models banks’ responses to negative liquidity shocks (fire-sale spillovers); using information embedded in repo haircuts to account for changes in asset-specific liquidity and flow-of-funds data, it allows to measure aggregate liquidity, defined as the sum of all the second-round spillover losses (not the initial direct losses) as a share of the total equity capital in the system; the factors’ decomposition applied produces a new component of AV, namely illiquidity concentration. The authors showed that the measure Granger-causes most other systemic risk measures. |

## Notes

1 | Countries were classified according to the criteria of the S&P DJI’s Global Benchmark Index for the study period. |

2 | Poland instigated the emergency mechanism to limit public debt in 2014, when the debt was at 56% of GDP. |

3 | In that period, several cases of monely laundering were reported in the CEE region, including ABLV bank (Latvia), Danske Bank (Estonia), Versobank (Estonia), and other smaller banks in the Baltics. |

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**Figure 11.**SIN measure for Estonia, Latvia, Lithuania, and Slovakia (euro area) between 2019 and 2020. To compare the scale of risk with previous years, see Appendix A.

**Table 1.**The studies of illiquidity effects categorized by the focus and sector of the financial system.

Systemic Risk Occurrence | Liquidity Effects | Primary Sector of Occurrence | Other Sectors Possibly Affected by the Effect | Authors |
---|---|---|---|---|

Illiquidity exposure | Correlated exposures to illiquidity, free-riding | Banking sector | Shadow banking | Bhattacharya and Gale (1987) |

Maturity rat-race and excessive short-term debt ^{1} | Brunnermeier and Oehmke (2013) | |||

Illiquidity contagion | Fire sales and their effect on prices | Financial assets markets | Banking sector, shadow banking, investment funds, SIFIs | Shleifer and Vishny (1992) |

Market incompleteness and effects of illiquidity on prices | Allen and Gale (1994, 2000a, 2000b) | |||

Snowball effect, in which the loss spiral interacts with a margin spiral ^{1} | Brunnermeier and Pedersen (2009) | |||

Market illiquidity contagion | Cespa and Foucault (2014) | |||

Illiquidity-driven crises | Constraints to arbitrage adding to illiquidity | Financial assets markets | - | Shleifer and Vishny (1997) |

Arbitrage affecting liquidity both ways | Gromb and Vayanos (2002) | |||

Runs caused by mark-to-market accounting | Banking sector, shadow banking, investment funds, SIFIs | Cifuentes et al. (2005) | ||

Bank runs triggering illiquidity, which triggers further bank runs | Banking sector | Diamond and Rajan (2005) | ||

Leverage, illiquidity spirals, and financial frictions | Brunnermeier et al. (2013) | |||

Brunnermeier and Sannikov (2014) | ||||

Informationally driven market freezes | Interbank market fragility due to fear of adverse selection | Banking sector | - | Flannery (1996) |

Lack of information about the counterparty risk causes the banks to stop lending to each other upon large shocks | Caballero and Simsek (2013) | |||

Interbank market freezes caused by information asymmetry | Heider et al. (2015) | |||

Information asymmetry as a source of repo markets collapse | Financial assets markets | Banking sector, financial markets, shadow banking, investment funds, | Acharya et al. (2011) | |

Collateral value vs. its price | Gorton and Ordonez (2014) |

^{1}A loss spiral occurs when the losses on a few assets induce the market participants to reduce their positions in many other assets. Then these sales depress market prices, prompting further losses; a margin spiral occurs when market participants apply higher margin requirements because of the reduced market liquidity. Both effects reinforce each other, increasing the pressure to sell more assets (Brunnermeier and Pedersen 2009).

Quantity-Based Indicators | Price-Based Indicators | |
---|---|---|

Monetary liquidity | Base money and broader monetary aggregates | Policy and money-market interest rates |

Access to central bank liquidity facility (e.g., bidding volume) | ||

Monetary conditions indices | ||

Foreign exchange reserves | ||

Funding liquidity | Bank liquidity ratios | Unsecured interbank lending (Libor–OIS spreads) |

Secured interbank lending (repo rates) | ||

Bank net cash flow estimates | Margins and haircuts on repo collateral | |

FX swap basis | ||

Maturity mismatch measures | Violation of arbitrage conditions (bond–CDS basis, covered interest rate parity) | |

Commercial paper market volumes | Spreads between assets with similar credit characteristics | |

Qualitative surveys of funding conditions | ||

Market liquidity | Transaction volumes | Bid–ask spreads on selected global assets |

Qualitative fund manager surveys |

Measure | Authors | Is the Application Possible? (Data Limitations) | Is Contemporaneous Measurement Possible? (Issues of Lags and Frequency) | Does it Facilitate Systemic Risk Analysis? (Coverage/Proxying the Whole Financial System) |
---|---|---|---|---|

Liquidity factor | Pastor and Stambaugh (2003) | YES | YES | NO |

A set of interpretable parameters | Getmansky et al. (2004) | NO | x | x |

Broader hedge-fund-based systemic risk measures | Chan et al. (2006) | NO | x | x |

A system of liquidity risk charges (LRCs) | Perotti and Suarez (2011) | YES | NO | x |

Contrarian strategy liquidity measure (CSL) | Khandani and Lo (2011) | YES | YES | NO |

Price-impact liquidity measure (PIL) | Khandani and Lo (2011) | YES | YES | NO |

Systemic Liquidity Risk Index (SLRI) | Severo (2012) | NO | x | x |

Daily liquidity noise measure | Hu et al. (2013) | NO | x | x |

Liquidity Mismatch Index (LMI) | Brunnermeier et al. (2014) | YES | NO | x |

Systemic risk-adjusted liquidity (SRL) model | Jobst (2014) | YES | NO | x |

Systemicness | Greenwood et al. (2015) | NO | x | x |

Cumulative Distance to Default (CDD) | Karkowska (2015) | YES | NO | x |

Aggregate vulnerability (AV) and illiquidity concentration | Duarte and Eisenbach (2019) | NO | x | x |

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Dziwok, E.; Karaś, M.A.
Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets. *Risks* **2021**, *9*, 124.
https://doi.org/10.3390/risks9070124

**AMA Style**

Dziwok E, Karaś MA.
Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets. *Risks*. 2021; 9(7):124.
https://doi.org/10.3390/risks9070124

**Chicago/Turabian Style**

Dziwok, Ewa, and Marta A. Karaś.
2021. "Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets" *Risks* 9, no. 7: 124.
https://doi.org/10.3390/risks9070124