Corporate Liquidity and Nber Recession Announcements

Stone, Anna‐Leigh; Gup, Benton E.

doi:10.1111/jfir.12191

Cited by 3 publications

(3 citation statements)

References 59 publications

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“…The role of macro-economy in the liquidity literature has always assumed a key role (Stone and Gup, 2019). The empirical literature on Fixed Income has explored the macro factors affecting bond yields and prices (Csonto and Ivaschenko, 2013;Poghosyan, 2014), but we find a dearth of studies in the area of liquidity determinants of sovereign bonds across the varying maturity spectrum.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 79%

Rippling effect of liquidity risk in the sovereign term structure

Anthony

Prasanna

2023

JRF

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PurposeThe study attempts to identify the linkages in the term structure of illiquidity and the impact of global and domestic factors on sovereign bonds in emerging Asia. The objective of the study ensues on defining the direction of illiquidity spillover across bonds of varying tenors.Design/methodology/approachThis study explores the joint dynamics of contemporary liquidity risk premia and its time-varying effect on the term structure spectrum using the Diebold and Yilmaz (2012) spillover framework.FindingsA substantial relationship was found to exist between the liquidity of bonds with closer terms to maturity. The macroeconomic environment primarily impacts the liquidity of 10-year bonds, and they spiral down to the subsequent bond liquidity, exhibiting a rippling effect. The authors further show that the direction of liquidity shock transmission is from long- to medium- and thence to short-term bonds. Among the global factors, foreign investments and S & P 500 VIX significantly affect the liquidity of 10-year bonds.Research limitations/implicationsThe study has several implications for academicians, policymakers and domestic and global investment professionals. The drivers of liquidity risk and the transmission across the term structure help investors in designing efficient portfolio diversification strategies. The results are relevant for cross-border investors in the valuation of emerging Asian sovereign bonds while deciding on asset allocations and hedging strategies. The monetary regulators strive on a continuous basis to improve the liquidity in sovereign bond markets in order to ensure efficient funding of development activities. This study finds that short-term bonds are more liquid than long-term bonds. Their auction framework with higher series of short-term bond issues helps to provide the required liquidity in the markets.Practical implicationsThe term structure of illiquidity is upward sloping, inferring a higher underlying liquidity risk of long-term bonds compared to short-term bonds. This finding suggests that a higher representation of short-term bonds in the auction framework helps to enhance the overall market liquidity.Originality/valueThis study offers insights into the debate on the shape of the term structure of illiquidity and the point of origination of liquidity shocks. Further, the direction of spillover across a wide spectrum of bonds is also demonstrated.

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Section: Literature Review and Hypotheses Developmentmentioning

confidence: 79%

Rippling effect of liquidity risk in the sovereign term structure

Anthony

Prasanna

2023

JRF

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“…Notably, financially constrained or smaller firms sharply increase their cash level just after the financial crises; thus, they hold more cash compared to financially unconstrained or larger firms as supported by precautionary and transaction motives. Furthermore, Stone and Gup (2019) mention that US firms hoard more cash after the global financial crisis 2007-2009. Since the rise in cash holdings implies an increase in SOA of cash, we formulate our third hypothesis:…”

Section: Hypothesis Developmentmentioning

confidence: 99%

“…Our sample period starts in 2002 to eliminate the effect of Dot-com crisis and the subsequent turbulence(Dang et al 2014: 232). Also, we have a five year window to examine optimal cash decisions before and after the financial uncertainties.5 We describe the GFC period 2007-2009 as stated byKahle and Stulz (2013) andStone and Gup (2019).6 We follow De Marco (2019) who defines the EDC period as 2010-2012.7 Freund et al (2006) specify that the VIF values should be smaller than 10 not to face any multicollinearity problem. InTable A2(Appendix A), we report the maximum VIF with 3.11; that is why our sample does not suffer from multicollinearity issue.…”

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confidence: 99%

Is saving vital? Evidence from the financial crisis

Tekin

Polat

2020

Econ Bus Lett

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We use a sample of 8,869 firm-years from the highly regulated Main Market (MAIN) and relatively unregulated Alternative Investment Market (AIM) in the United Kingdom to analyse the impact of financial restrictions on optimal cash holdings in the context of financial crises. Employing system generalised methods of moments, we find that AIM firms have a faster adjustment speed of cash as confirmed by precautionary and transaction motives over 2001-2017. However, AIM firms decrease (increase) their adjustment speed of cash more than MAIN firms during (after) the financial crises.

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A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities

Chen

Rehman

2021

Energies

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The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy.

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Corporate Liquidity and Nber Recession Announcements

Cited by 3 publications

References 59 publications

Rippling effect of liquidity risk in the sovereign term structure

Rippling effect of liquidity risk in the sovereign term structure

Is saving vital? Evidence from the financial crisis

A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities

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