Bank risk, financial stress, and bank derivative use

Bliss, Barbara A.; Clark, Jeffrey A.; DeLisle, Jared

doi:10.1002/fut.21902

Cited by 13 publications

(12 citation statements)

References 55 publications

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“…This raises concerns that the panel data in Tables 8 and 9 is subject to selection bias when interest rate derivatives are the dependent variable. Similar to Bliss et al (2018) and Purnanandam (2007), I deploy a Heckman (1979) two‐stage model that uses maximum‐likelihood estimation to correct for any sample selection bias. Using this approach, I obtain similar results as Tables 8 and 9.…”

Section: Empirical Model and Resultsmentioning

confidence: 99%

“…Using this approach, I obtain similar results as Tables 8 and 9. The first stage in Equation (6) is a Probit model where the independent variables are motivated by Sinkey and Carter (2000), Bliss et al (2018), Purnanandam (2007), and Dahl and Drew (2018) specified as:

\begin{matrix} 0.33em \\ P r MathClass-open(I R D U s e r_{i, t} = 1 MathClass-close) & = & ϕ [α + γ_{1} l o g MathClass-open(A s s e t s MathClass-close) + γ_{2} \frac{L o a n s_{i, t}}{A s s e t s_{i, t}} + γ_{3} M a t u r i t y G a p_{i, t} + γ_{4} \frac{N P A_{i, t}}{A s s e t s_{i, t}} \\ + γ_{5} \frac{N I E_{i, t}}{A s s e t s_{i, t}} + γ_{6} \frac{{MathClass-open[R e s i d e n t i a l M o r t g a g e s S o l d, H e l d f o r S a l e MathClass-close]}_{i, t}}{A s s e t s_{i, t}} + ε_{i, t}] \end{matrix}

…”

Section: Empirical Model and Resultsmentioning

confidence: 99%

“…Cash flow risk can stem from many difference sources such as mismatched maturities (Purnanandam, 2007), repricing risk, bankruptcy risk (Smith & Stulz, 1985), financing risks (Froot et al, 1993), interest rate risk, monetary policy risks, and market risks. Hedging allows for lending policies become less sensitive to macroeconomic shocks with the use of interest rate derivatives (Bliss et al, 2018; Purnanandam, 2007) and subsequently lend more than nonusers of derivatives. This paper specifically focuses on banks hedging of interest rate risk of mortgage originations sold to third parties, which were new regulatory reporting requirements in the Call Reports starting in 2006 Q3, and has not been covered previously in the banking literature.…”

Section: Literature Review and Discussionmentioning

confidence: 99%

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Disproportionate costs of uncertainty: Small bank hedging and Dodd‐Frank

Kim

2021

Journal of Futures Markets

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Uncertainty in banking regulation may impose widespread economic costs by increasing financial constraints on credit availability. Four years of Dodd‐Frank uncertainty over undecided risk weightings increased regulatory uncertainty for smaller banks, restricting “vanilla” interest rate hedging activities. This paper uses newly reported mortgage banking data as an identification strategy and finds that when costs of uncertainty are removed, small banks hedge 97%–120% more interest rate risk while mortgage securitization income increases by 65.2% compared to large banks. These findings support the need for tailored regulations that consider the higher costs of regulatory uncertainty for smaller banks.

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Section: Empirical Model and Resultsmentioning

confidence: 99%

\begin{matrix} 0.33em \\ P r MathClass-open(I R D U s e r_{i, t} = 1 MathClass-close) & = & ϕ [α + γ_{1} l o g MathClass-open(A s s e t s MathClass-close) + γ_{2} \frac{L o a n s_{i, t}}{A s s e t s_{i, t}} + γ_{3} M a t u r i t y G a p_{i, t} + γ_{4} \frac{N P A_{i, t}}{A s s e t s_{i, t}} \\ + γ_{5} \frac{N I E_{i, t}}{A s s e t s_{i, t}} + γ_{6} \frac{{MathClass-open[R e s i d e n t i a l M o r t g a g e s S o l d, H e l d f o r S a l e MathClass-close]}_{i, t}}{A s s e t s_{i, t}} + ε_{i, t}] \end{matrix}

…”

Section: Empirical Model and Resultsmentioning

confidence: 99%

Section: Literature Review and Discussionmentioning

confidence: 99%

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Disproportionate costs of uncertainty: Small bank hedging and Dodd‐Frank

Kim

2021

Journal of Futures Markets

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“…In addition, we investigate the determinants of derivative usage by emerging country banks. Numerous studies have examined the relationships between financial derivatives and bank risk, stability, and value for developed market banks (Li and Marinč, 2014;Mayordomo, Rodriguez-Moreno and Peña, 2014;Bliss, Clark and DeLisle, 2017;Chang, Ho and Hsiao, 2017), but few studies have been dedicated to analyzing these relationships in emerging market banks Chan, 2011, 2013;Keffala, 2015;Titova, Penikas and Gomayun, 2018). To further our understanding of emerging market bank derivative usage, we analyze a unique dataset by hand collecting derivative data from the financial statements of listed banks and classifying derivatives according to their main purpose, namely, hedging or speculation.…”

Section: Introductionmentioning

confidence: 99%

Deriving value or risk? Determinants and the impact of emerging market banks’ derivative usage

Bazih

Vanwalleghem

2021

Research in International Business and Finance

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“…The ECB's research team believes that financial pressures are contributed by financial markets, financial institutions, and financial infrastructure, and based on this classification, the CISS index is used to measure the level of financial stress in the EU economies from 1999 to 2011. Bliss et al (2018) considered commercial banks' credit, derivatives, and mutual lending risks, and then constructed a financial stress index based on market volatility.…”

Section: Literature Review 21 Financial Risk Contagionmentioning

confidence: 99%

Debt risk analysis of non-financial corporates using two-tier networks

Cao

Lei

2020

IMDS

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PurposeNon-financial corporate debt is one of the important sources of systematic risk in the real economy. Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model.Design/methodology/approachAssessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model based on four dimensions: concept definition, data structure, risk contagion network construction, and risk measurement indicators construction. We take the Jiangsu bond issuer guarantee network as a sample area.FindingsTaking the Jiangsu bond issuer guarantee network as a sample area, we find that there is a strong correlation between the debts of non-financial corporation in China, and it is easy to become a potential regional systematic risk source. In addition, our empirical research also reveals that external risk exposure and node degree of network are two key indicators when identifying key risk-contagion enterprises.Originality/valueThe main contributions of this study are two-fold. First, this article proposes a two-tier risk contagion networks model to measure systematic risk in non-financial corporation. Second, this article describes the structure of the corporate risk contagion network.

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Bank risk, financial stress, and bank derivative use

Cited by 13 publications

References 55 publications

Disproportionate costs of uncertainty: Small bank hedging and Dodd‐Frank

Disproportionate costs of uncertainty: Small bank hedging and Dodd‐Frank

Deriving value or risk? Determinants and the impact of emerging market banks’ derivative usage

Debt risk analysis of non-financial corporates using two-tier networks

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