|
on Risk Management |
|
Issue of 2024‒04‒08
thirteen papers chosen by |
| By: | Tommaso Gasparini; Vivien Lewis; Stéphane Moyen; Stefania Villa |
| Abstract: | Increases in firm default risk raise the default probability of banks while decreasing output and inflation in US data. To rationalize the empirical evidence, we analyse firm risk shocks in a New Keynesian model where entrepreneurs and banks engage in a loan contract and both are subject to default risk. In the model, a wave of corporate defaults leads to losses on banks' balance sheets; banks respond by selling assets and reducing credit provision. A highly leveraged banking sector exacerbates the contractionary effects of firm defaults. We show that high minimum capital requirements jointly implemented with a countercyclical capital buffer are effective in dampening the adverse consequences of firm risk shocks. |
| Keywords: | Bank Default, Capital Buffer, Firm Risk, Macroprudential Policy |
| JEL: | E44 E52 E58 E61 G28 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:944&r=rmg |
| By: | Tiffany Eder; Claire Labonne; Caitlin O'Loughlin; Krish Sharma |
| Abstract: | We describe an important risk management tool at financial institutions, risk appetite frameworks. We observe those frameworks for credit cards portfolios at four large banks and analyze when and why banks adjust them. The risk appetite frameworks for these banks monitor 40 to 150 metrics. We focus on metrics related to outstanding balances of which we identified 79. Overall, we find that these frameworks are sticky. Most adjustments occur during scheduled annual reviews and are relatively limited. Limit breaches are rare. Thresholds are often changed the month after a breach or after the utilization rate crossed 90 percent, but most breaches imply risk mitigating measures such as tightening credit standards. Notably, managers’ reactions were even stickier in the pandemic period. |
| Keywords: | banking supervision; risk management; risk limits; risk appetite framework; credit cards |
| JEL: | G32 G21 G38 |
| Date: | 2024–02–15 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedbqu:97930&r=rmg |
| By: | Rambod Rahmani; Marco Parola; Mario G. C. A. Cimino |
| Abstract: | Due to the recent increase in interest in Financial Technology (FinTech), applications like credit default prediction (CDP) are gaining significant industrial and academic attention. In this regard, CDP plays a crucial role in assessing the creditworthiness of individuals and businesses, enabling lenders to make informed decisions regarding loan approvals and risk management. In this paper, we propose a workflow-based approach to improve CDP, which refers to the task of assessing the probability that a borrower will default on his or her credit obligations. The workflow consists of multiple steps, each designed to leverage the strengths of different techniques featured in machine learning pipelines and, thus best solve the CDP task. We employ a comprehensive and systematic approach starting with data preprocessing using Weight of Evidence encoding, a technique that ensures in a single-shot data scaling by removing outliers, handling missing values, and making data uniform for models working with different data types. Next, we train several families of learning models, introducing ensemble techniques to build more robust models and hyperparameter optimization via multi-objective genetic algorithms to consider both predictive accuracy and financial aspects. Our research aims at contributing to the FinTech industry in providing a tool to move toward more accurate and reliable credit risk assessment, benefiting both lenders and borrowers. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.03785&r=rmg |
| By: | Dalderop, J.; Linton, O. B. |
| Abstract: | Option-implied risk-neutral densities are widely used for constructing forward-looking risk measures. Meanwhile, investor risk aversion introduces a multiplicative pricing kernel between the risk-neutral and true conditional densities of the underlying asset’s return. This paper proposes a simple local estimator of the pricing kernel based on inverse density weighting, and characterizes its asymptotic bias and variance. The estimator can be used to correct biased density forecasts, and performs well in a simulation study. A local exponential linear variant of the estimator is proposed to include conditioning variables. In an application, we estimate a demand-based model for S&P 500 index options using net positions data, and attribute the U-shaped pricing kernel to heterogeneous beliefs about conditional volatility. |
| Keywords: | Density Forecasting, Nonparametric Estimation, Option Pricing, Trade Data |
| JEL: | C14 G13 |
| Date: | 2024–03–05 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2411&r=rmg |
| By: | Henrik Andersen; Ragnar E Juelsrud; Carola Müller |
| Abstract: | We use unique relationship-level data which includes banks' private risk assessments of corporate borrowers to quantify how competition among banks affects the risk sensitivity of interest rates in the corporate credit market. We show that an increase in competition makes corporate lending rates less sensitive to banks' own assessment of borrower probability of default and this is more pronounced in market segments with higher degree of asymmetric information. Our results are driven by banks with low franchise values, outlining a novel channel of how the competition-fragility nexus can operate. |
| Keywords: | banking competition, relationship lending, credit markets, risk-based pricing, financial stability |
| JEL: | G21 G28 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1169&r=rmg |
| By: | Huy N. Chau; Duy Nguyen; Thai Nguyen |
| Abstract: | This paper investigates short-term behaviors of implied volatility of derivatives written on indexes in equity markets when the index processes are constructed by using a ranking procedure. Even in simple market settings where stock prices follow geometric Brownian motion dynamics, the ranking mechanism can produce the observed term structure of at-the-money (ATM) implied volatility skew for equity indexes. Our proposed models showcase the ability to reconcile two seemingly contradictory features found in empirical data from equity markets: the long memory of volatilities and the power law of ATM skews. Furthermore, the models allow for the capture of a novel phenomenon termed the quasi-blow-up phenomenon. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.16509&r=rmg |
| By: | Jamotton, Charlotte (Université catholique de Louvain, LIDAM/ISBA, Belgium); Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | This article explores the application of Latent Dirichlet Allocation (LDA) to structured tabular insurance data. LDA is a probabilistic topic modelling approach initially developed in Natural Language Processing (NLP) to uncover the underlying structure of (unstructured) textual data. It was designed to represent textual documents as mixture of latent (hidden) topics, and topics as mixtures of words. This study introduces the LDA’s document-topic distribution as a soft clustering tool for unsupervised learningtasks in the actuarial field. By defining each topic as a risk profile, and by treating insurance policies as documents and the modalities of categorical covariates as words, we show how LDA can be extended beyond textual data and can offer a framework to uncover underlying structures within insurance portfolios. Our experimental results and analysis highlight how the modelling of policies based on topic cluster membership, and the identification of dominant modalities within each risk profile, can give insights into the prominent risk factors contributing to higher or lower claim frequencies. |
| Keywords: | Latent dirichlet allocation ; topic modelling ; soft clustering ; insurance data ; risk profile ; natural language processing |
| Date: | 2024–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:aiz:louvad:2024008&r=rmg |
| By: | Daixin Wang; Zhiqiang Zhang; Yeyu Zhao; Kai Huang; Yulin Kang; Jun Zhou |
| Abstract: | User financial default prediction plays a critical role in credit risk forecasting and management. It aims at predicting the probability that the user will fail to make the repayments in the future. Previous methods mainly extract a set of user individual features regarding his own profiles and behaviors and build a binary-classification model to make default predictions. However, these methods cannot get satisfied results, especially for users with limited information. Although recent efforts suggest that default prediction can be improved by social relations, they fail to capture the higher-order topology structure at the level of small subgraph patterns. In this paper, we fill in this gap by proposing a motif-preserving Graph Neural Network with curriculum learning (MotifGNN) to jointly learn the lower-order structures from the original graph and higherorder structures from multi-view motif-based graphs for financial default prediction. Specifically, to solve the problem of weak connectivity in motif-based graphs, we design the motif-based gating mechanism. It utilizes the information learned from the original graph with good connectivity to strengthen the learning of the higher-order structure. And considering that the motif patterns of different samples are highly unbalanced, we propose a curriculum learning mechanism on the whole learning process to more focus on the samples with uncommon motif distributions. Extensive experiments on one public dataset and two industrial datasets all demonstrate the effectiveness of our proposed method. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.06482&r=rmg |
| By: | Siahaan, Fernando; Amberger, Harald; Sureth, Caren |
| Abstract: | This study investigates the effect of a Turnover-based Corporate Income Tax (TbCIT) on corporate risk-taking. TbCIT is a simplified presumptive tax levied on a firm's turnover and commonly applied to SMEs and hard-to-tax income. Using a rich sample of Indonesian firms for the years 2009 to 2021, we provide evidence that corporate risk-taking is negatively associated with a firm's TbCIT exposure. The negative effect is stronger for firms in industries with high profit margins and firms with prior year losses. However, we find no association between risk-taking and the effective TbCIT rate. Overall, our findings extend prior research on the effects of limited risk sharing between taxpayers and the government by showing that turnover-based taxation can depress corporate risk-taking. Our study also informs policymakers about potential unintended consequences of adopting simplified, turnover-based tax regimes. |
| Keywords: | turnover-based tax, corporate income tax, risk-taking, SMEs taxation |
| JEL: | H25 H32 G32 O53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:arqudp:285364&r=rmg |
| By: | Briggs, Joseph (Goldman Sachs); Cesarini, David (New York University); Chanwook Lee, Sean (Harvard University); Lindqvist, Erik (Swedish Institute for Social Research, Stockholm University); Östling, Robert (Stockholm School of Economics) |
| Abstract: | We investigate the impact of financial windfalls on household portfolio choices and risk exposure. Exploiting the randomized assignment of lottery prizes in three Swedish lotteries, we find a windfall gain of $100K leads to a 5-percentage-point de- crease in the risky share of household portfolios. We show theoretically that negative wealth effects are consistent with both constant and decreasing relative risk aversion and analyze how our empirical estimates help distinguish between competing models of portfolio choice. We further show our results are quantitatively aligned with the predictions of a calibrated dynamic portfolio choice model with nontradable human capital and consumption habits. |
| Keywords: | risk preferences; portfolio choice |
| JEL: | G11 G50 |
| Date: | 2023–11–21 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:sofiwp:2023_015&r=rmg |
| By: | Spiros Bougheas; David I. Harvey; Alan Kirman; Douglas Nelson; Alan P. Kirman; Douglas R. Nelson |
| Abstract: | We develop a dynamic computational network model of the banking system where fire sales provide the amplification mechanism of financial shocks. Each period a finite number of banks offers a large, but finite, number of loans to households. Banks with excess liquidity also offer loans to other banks with insufficient liquidity. Thus, each period an interbank loan market is endogenously formed. Bank assets are hit by idiosyncratic shocks drawn from a thin tailed distribution. The uneven distribution of shocks across banks implies that each period there are banks that become insolvent. If insolvent banks happen also to be heavily indebted to other banks, their liquidation can trigger other bank failures. We find that the distribution across time of the growth rate of banking assets has a ‘fat left tail’ that corresponds to rare economic disasters. We also find that the distribution of initial shocks is not a perfect predictor of economic activity; that is some of the uncertainty is endogenous and related to the structure of the interbank network. |
| Keywords: | systemic risk, fire sales, banking network, macroeconomic shocks |
| JEL: | E44 G01 G21 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10991&r=rmg |
| By: | Leila Ben Salem; Montassar Zayati; Ridha Nouira; Christophe Rault |
| Abstract: | This paper investigates the co-movements of oil prices and the exchange rates of 10 top oil-importing and oil-exporting countries. Firstly, we estimated the total static spillover index based on vector autoregressive (VAR) models. Secondly, we adopted the recent DCC-GARCH-CONNECTEDNESS approach proposed by Gabauer (2020) to conduct a time-varying analysis that investigates the directionally dynamic connectedness among WTI and Shanghai crude oil futures and currency markets. We explored contagion spillover volatility by focusing on a sample of major oil-exporting and oil-importing countries using daily data from 4 March 2018 to 25 August 2023. We analysed this relationship during four phases: the entire sample; before COVID-19; during COVID-19; and during the Russian‒Ukrainian war. Our results confirm the persistence of volatility for the series studied, thereby justifying the use of the dynamic connectedness approach. Our findings also reveal strong evidence of volatility transmission between oil prices and exchange-rate markets. However, the COVID-19 pandemic and the Russian‒Ukrainian war have altered this link. The connectedness between the two markets (petrol and exchange) was stronger at the beginning of the crisis period and then gradually depreciated in value over time. Our findings reveal that exchange rates for both oil-exporting and oil-importing countries are more sensitive to oil price shocks during crises than in normal periods. This suggests that volatility contagion between these two markets continues to exist, thus emphasising the role of oil price shocks as net transmitters across the network during extreme scenarios. |
| Keywords: | Shanghai futures, WTI, exchange rates, DCC-GARCH-CONNECTEDNESS, Covid-19, Russian-Ukraine war |
| JEL: | C50 Q40 Q43 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10989&r=rmg |
| By: | Savvakis C. Savvides (Visiting Lecturer, John Deutsch International Executive Programs, Queens University, Canada.) |
| Abstract: | Through this paper the author highlights the importance of constructing an integrated financial model and in using growth patterns in projecting the key parameter projections to generate consistent and meaningful scenarios during a Monte Carlo simulation risk analysis application and to avoid and contain the correlation problem. The Integrated Financial Model© by Savvakis C. Savvides was created and tested after many years of expertise of the author in corporate lending and project finance as well as from teaching investment appraisal and risk analysis and the development of several related software. It is argued that to apply Monte Carlo Simulation Risk Analysis in a meaningful manner and to enhance the decision-making process the methodology should not be used “as a toy†but rather in a thoughtful manner that takes into consideration all aspects of a prudently constructed business plan and as this is manifested through an integrated financial model. The use of growth pattern functions for the key risk variables is essential so as to contain the correlation problem and for the simulation to be based on consistent and realistic scenarios. |
| Keywords: | Market analysis, quantity demanded, elasticity of demand, project evaluation, market segmentation, market penetration. |
| JEL: | D11 D61 H43 L21 M31 |
| Date: | 2024–03–18 |
| URL: | http://d.repec.org/n?u=RePEc:qed:dpaper:4615&r=rmg |