Notes on Bonds: Illiquidity Feedback During the Financial Crisis

Musto, David K.; Nini, Greg; Schwarz, Krista

doi:10.1093/rfs/hhy022

Cited by 36 publications

(25 citation statements)

References 29 publications

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“…Microindividual risk spillovers and network effects formed by risk contagion have become decoding the focus of the tail event. When systemic risks accumulate to a certain extent and are released, it will cause a large number of financial institutions to close down, which will spread to the entire financial system and trigger a systemic financial crisis [15]. Price is used to determine the cost that must be paid to prevent various risks: probability is used to estimate the likelihood of these risks occurring; preference is used to determine the ability and willingness to bear risks and confidence.…”

Section: Similarity Measurementioning

confidence: 99%

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

Zhu

Liu

2021

Complexity

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The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management. The fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk early-warning mechanism. The main idea of the K-means clustering algorithm is to gradually optimize clustering results and constantly redistribute target dataset to each clustering center to obtain optimal solution; its biggest advantage lies in its simplicity, speed, and objectivity, being widely used in many research fields such as data processing, image recognition, market analysis, and risk evaluation. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk early-warning, elaborated the development background, current status and future challenges of the K-means clustering algorithm, introduced the related works of similarity measure and item clustering, proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk early-warning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and finally carried out an empirical experiments and its result analysis. The study results show that the K-means clustering method can effectively avoid the subjective negative impact caused by artificial division thresholds, continuously optimize the prediction process of financial risk and redistribute target dataset to each cluster center for obtaining optimized solution, so the algorithm can more accurately and objectively distinguish the state interval of different financial risks, determine risk occurrence possibility and its severity, and provide a scientific basis for risk prevention and management. The study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm.

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Section: Similarity Measurementioning

confidence: 99%

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

Zhu

Liu

2021

Complexity

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“…Fleckenstein, Longstaff, and Lustig (2014) find that a significant mispricing arose between Treasury bonds and inflation-swapped TIPS issues with replicating cash flows. Musto, Nini, and Schwarz (2018) detail a large and systematic mispricing during the crisis between notes and bonds with identical cash flows. Hu, Pan, and Wang (2013) show that "noise" in Treasury security prices rose sharply during the crisis.…”

Section: Introductionmentioning

confidence: 99%

Liquidity and volatility in the U.S. Treasury market

Nguyen

Engle

Fleming

et al. 2020

Journal of Econometrics

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We model the joint dynamics of intraday liquidity, volume, and volatility in the U.S. Treasury market, especially through the 2007-09 financial crisis and around important economic announcements. Using various specifications based on Bauwens and Giot's (2000) Log-ACD(1,1) model, we find that liquidity, volume, and volatility are highly persistent, with volatility having a lower short-term persistence than the other two. Market liquidity and volume are important to explaining volatility dynamics but not vice versa. In addition, market dynamics change during the financial crisis, with all variables exhibiting increased responsiveness to their most recent realizations. Our models also reveal different market dynamics around announcements. Finally, we introduce new measures of liquidity risk that are useful for continually monitoring liquidity conditions and the risk of liquidity stress in the market.

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“…For instance, a two-month security issued last month is typically less liquid than a two-month security issued last week, as the newest issued security becomes the benchmark – known as on the run securities – for pricing in that maturity bucket (see Biais et al., 2004 for European Tbills; Babbel et al., 2004; for the US). Dealers have no obligation to quote the older, off the run , Tbills or bonds, and rarely trade them with each other (Musto et al., 2017). Off-the-run securities are rarely traded, sitting instead in the portfolio of investors until maturity.…”

Section: Safe Assets: a Theoretical Viewmentioning

confidence: 99%

“…Off-the-run securities are rarely traded, sitting instead in the portfolio of investors until maturity. The on-the-run premium is higher at longer maturities (see Fleming, 2000) and increases during periods of market volatility (Musto et al., 2017). Even investors deemed patient, such as insurance companies, prefer the safety provided by on-the-run securities during bad times.…”

Section: Safe Assets: a Theoretical Viewmentioning

confidence: 99%

Chasing unicorns: The European single safe asset project

Gabor

Vestergaard

2018

Competition & Change

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For the past 20 years, Economic and Monetary Union (EMU) institutions have sought to engineer a single safe asset that would provide a credible store of value for capital market participants. Before 2008, the European Central Bank used shadow banking to create a single safe asset that we term shadow money, and in doing so also erased borders between Euro area government bond markets. Lacking appropriate ECB support, shadow euros could not withstand the pressures of the global financial crisis and brought down several periphery euro government bonds with them. Two new plans, the Capital Markets Union and the Sovereign Bond-Backed Securities, again turn to shadow banking, this time by using securitization to generate an entirely private safe asset or a public–private safe asset. Such plans cannot solve the enduring predicament of EMU’s bond markets architecture: that Member States have competed for investors (liquidity) since the introduction of the euro, betraying a deep hostility towards collective political solutions to the single safe asset problem. Technocratic-led, market-based initiatives need to persuade EMU states that there is little threat to their ability to issue debt in liquid markets. Without ECB interventions, market-based engineering of single safe assets runs the danger of repeatedly destabilizing national bond markets.

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Notes on Bonds: Illiquidity Feedback During the Financial Crisis

Cited by 36 publications

References 29 publications

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

Liquidity and volatility in the U.S. Treasury market

Chasing unicorns: The European single safe asset project

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