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on Risk Management |
| By: | Pascal Fran\c{c}ois; Genevi\`eve Gauthier; Fr\'ed\'eric Godin; Carlos O. P\'erez-Mendoza |
| Abstract: | We propose an enhanced deep hedging framework for index option portfolios, grounded in a realistic market simulator that captures the joint dynamics of S&P 500 returns and the full implied volatility surface. Our approach integrates surface-informed decisions with multiple hedging instruments and explicitly accounts for transaction costs. The hedging strategy also considers the variance risk premium embedded in the hedging instruments, enabling more informed and adaptive risk management. In this setting, state-dependent no-trade regions emerge naturally, improving rebalancing efficiency and hedging performance. Tested across simulated and historical data from 1996 to 2020, our method consistently outperforms traditional delta and delta-gamma hedging, demonstrating superior adaptability and risk reduction. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06208 |
| By: | Fredy Pokou (CRIStAL, INOCS); Jules Sadefo Kamdem (MRE); Fran\c{c}ois Benhmad (MRE) |
| Abstract: | In an environment of increasingly volatile financial markets, the accurate estimation of risk remains a major challenge. Traditional econometric models, such as GARCH and its variants, are based on assumptions that are often too rigid to adapt to the complexity of the current market dynamics. To overcome these limitations, we propose a hybrid framework for Value-at-Risk (VaR) estimation, combining GARCH volatility models with deep reinforcement learning. Our approach incorporates directional market forecasting using the Double Deep Q-Network (DDQN) model, treating the task as an imbalanced classification problem. This architecture enables the dynamic adjustment of risk-level forecasts according to market conditions. Empirical validation on daily Eurostoxx 50 data covering periods of crisis and high volatility shows a significant improvement in the accuracy of VaR estimates, as well as a reduction in the number of breaches and also in capital requirements, while respecting regulatory risk thresholds. The ability of the model to adjust risk levels in real time reinforces its relevance to modern and proactive risk management. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16635 |
| By: | Daniel Gaigall; Stefan Weber |
| Abstract: | We introduce a framework for systemic risk modeling in insurance portfolios using jointly exchangeable arrays, extending classical collective risk models to account for interactions. We establish central limit theorems that asymptotically characterize total portfolio losses, providing a theoretical foundation for approximations in large portfolios and over long time horizons. These approximations are validated through simulation-based numerical experiments. Additionally, we analyze the impact of dependence on portfolio loss distributions, with a particular focus on tail behavior. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06287 |
| By: | Kewin P\k{a}czek; Damian Jelito; Marcin Pitera; Agnieszka Wy{\l}oma\'nska |
| Abstract: | This paper explores the applications of the 20/60/20 rule-a heuristic method that segments data into top-performing, average-performing, and underperforming groups-in mathematical finance. We review the statistical foundations of this rule and demonstrate its usefulness in risk management and portfolio optimization. Our study highlights three key applications. First, we apply the rule to stock market data, showing that it enables effective population clustering. Second, we introduce a novel, easy-to-implement method for extracting heavy-tail characteristics in risk management. Third, we integrate spatial reasoning based on the 20/60/20 rule into portfolio optimization, enhancing robustness and improving performance. To support our findings, we develop a new measure for quantifying tail heaviness and employ conditional statistics to reconstruct the unconditional distribution from the core data segment. This reconstructed distribution is tested on real financial data to evaluate whether the 20/60/20 segmentation effectively balances capturing extreme risks with maintaining the stability of central returns. Our results offer insights into financial data behavior under heavy-tailed conditions and demonstrate the potential of the 20/60/20 rule as a complementary tool for decision-making in finance. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.02840 |
| By: | , YingyingLiu; Wang, Mengmeng; , selfdef; Shui, Yan; Deng, Yao; Xue, Chenye; Mao, Tianxin; Rao, Hengyi |
| Abstract: | Risk preference is a critical determinant of commercial insurance adoption, yet existing empirical studies present conflicting evidence regarding the relationship between risk preference and insurance purchasing behavior. This study challenges conventional adversarial or favorable selection perspectives by demonstrating a nonlinear inverse U-shaped association between risk preference and insurance uptake. Using two distinct samples, we provide robust evidence for this non-monotonic pattern. First, analysis of a nationally representative survey from the China Family Panel Studies (N = 9, 406) revealed that individuals with moderate risk preferences were more likely to purchase commercial insurance compared to those at the extremes of the risk spectrum (i.e., highly risk-seeking or risk-averse). Second, cross-validation through a separate sample (N = 208) confirmed the reliability of this inverted U-shaped relationship. Our findings suggest that moderate risk-tolerant individuals may strategically balance risk mitigation and cost-benefit considerations, driving their propensity to insure. These results advance theoretical frameworks in behavioral economics and have practical implications for insurers seeking to refine risk segmentation strategies and product design. By elucidating the nonlinear nature of risk preferences, this study bridges gaps in understanding insurance market dynamics therefore informs targeted marketing interventions for distinct consumer segments. |
| Date: | 2025–04–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:a543w_v1 |
| By: | Irma Alonso-Alvarez (BANCO DE ESPAÑA); Marina Diakonova (BANCO DE ESPAÑA); Javier J. Pérez (BANCO DE ESPAÑA) |
| Abstract: | Geopolitical risks and tensions are nowadays regularly presented by policymakers and analysts as key conditioning factors of economic activity in both the short and the medium-run. Widely accepted operational measures of geopolitical risks tend to be based on counting the number of newspaper articles related to adverse geopolitical events, in particular following the ground-breaking paper of Caldara and Iacoviello (2022) in which they build their Geopolitical Risk (GPR) indexes. In this paper we propose one avenue to make further progress in the measurement of such risks. We provide a decomposition of GPR by exploiting the idea that the geopolitical risks that a country faces can be traced back to the countries or entities that are the source of those risks. In this regard, we exploit the idea that geopolitical risk linked to a specific geography or political entity can be interpreted as a “bilateral GPR”, and that the aggregation of such bilateral GPRs provides a natural way of interpreting the overall GPR index. We show that our indexes add distinct information from the benchmark GPR, and together form a more accurate representation of the geopolitical tensions currently present between the major economies. We also show that the geographical origin of a given GPR shock determines its macroeconomic effect in a given economy (as computed from standard VAR models), both in terms of the intensity of such effect and even its sign (i.e. whether a particular GPR shock causes GDP to increase or decrease). |
| Keywords: | geopolitical risk, geopolitical tensions, textual analysis |
| JEL: | C43 F51 H56 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2522 |
| By: | Gianluca Cafiso; Giulia Rivolta |
| Abstract: | This study examines the relationship between sovereign spreads and banks in terms of risk transmission, using the seven largest Italian banks as a sample over the period from 2003 to 2023. Our objective is to quantify and compare volatility spillovers, and to investigate whether bank-specific characteristics explain them. We perform a dynamic connectedness analysis based on the estimation of a vector autoregression with time-varying parameters. Our results suggest that, with the exception of severe crisis periods, banks tend to transmit more spillovers than they absorb. Moreover, the magnitude of these spillovers is influenced by factors such as capital adequacy and the structure of banks' portfolios. |
| Keywords: | sovereign spread, banks, volatility, connectedness measures, spillovers, time-varying parameters, VAR. |
| JEL: | G01 G21 E60 H12 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11816 |
| By: | Jinhui Li; Wenjia Xie; Luis Seco |
| Abstract: | This study introduces a dynamic investment framework to enhance portfolio management in volatile markets, offering clear advantages over traditional static strategies. Evaluates four conventional approaches : equal weighted, minimum variance, maximum diversification, and equal risk contribution under dynamic conditions. Using K means clustering, the market is segmented into ten volatility-based states, with transitions forecasted by a Bayesian Markov switching model employing Dirichlet priors and Gibbs sampling. This enables real-time asset allocation adjustments. Tested across two asset sets, the dynamic portfolio consistently achieves significantly higher risk-adjusted returns and substantially higher total returns, outperforming most static methods. By integrating classical optimization with machine learning and Bayesian techniques, this research provides a robust strategy for optimizing investment outcomes in unpredictable market environments. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.02841 |
| By: | Yuna Heo (University of Basel - Faculty of Business and Economics; Swiss Finance Institute); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)) |
| Abstract: | This study investigates the impact of skilled banker mobility risk on bank fragility. Using a novel measure of skilled banker mobility risk, we find that an increase in this risk raises the probability of bank default. The effect is more pronounced for banks facing higher competition and with a high proportion of non-performing loans. Further we show that labor mobility restriction laws can help mitigate the detrimental effect of skilled banker mobility risk. Our findings suggest that skilled banker mobility can exacerbate bank fragility, but proper policies can bolster resilience against possible adverse effects from labor market frictions. |
| Keywords: | skilled banker, labor mobility, financial stability, distance-to-default, bank default probability, bank fragility |
| JEL: | G15 G32 G38 Q54 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2534 |
| By: | Fangfang Wang (Autonomous University of Barcelona); Florina Silaghi (Autonomous University of Barcelona); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)); Miguel García-Cestona (Autonomous University of Barcelona) |
| Abstract: | We investigate the impact of ESG rating changes and daily ESG news sentiment on firm credit risk. We document a significant increase in CDS spreads following ESG rating downgrades, especially for the social pillar, while we find a muted reaction to ESG upgrades. A similar asymmetrical effect is documented for ESG news. We further show that the adverse effect of ESG downgrades on the CDS market is mitigated in the presence of positive ESG sentiment, a transparent information environment and higher rating disagreement. Lastly, the reaction is stronger for firms with lower creditworthiness, higher bankruptcy probability and tighter financial constraints. |
| Keywords: | ESG ratings, Credit default swaps, Event study, ESG news sentiment |
| JEL: | G14 G32 M14 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2524 |
| By: | Sung Hoon Choi; Donggyu Kim (Department of Economics, University of California Riverside) |
| Abstract: | Several approaches for predicting large volatility matrices have been developed based on high-dimensional factor-based Ito processes. These methods often impose restrictions to reduce the model complexity, such as constant eigenvectors or factor loadings over time. However, several studies indicate that eigenvector processes are also time-varying. To address this feature, this paper generalizes the factor structure by representing the integrated volatility matrix process as a cubic (order-3 tensor) form, which is decomposed into low-rank tensor and idiosyncratic tensor components. To predict conditional expected large volatility matrices, we propose the Projected Tensor Principal Orthogonal componEnt Thresholding (PT-POET) procedure and establish its asymptotic properties. The advantages of PT-POET are validated through a simulation study and demonstrated in an application to minimum variance portfolio allocation using high-frequency trading data. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202506 |
| By: | Chen, Yehning; Hasan, Iftekhar; Takalo, Tuomas |
| Abstract: | We study the effects of bank transparency on both banks' asset and liquidity risks, and ultimately, on banking sector stability and welfare. We show how enhanced bank transparency increases banks' vulnerability to excessive deposit outflows, but this threat of a liquidity crisis incentivizes banks to choose safer assets. We find that bank stability and welfare are a nonmonotonic function of transparency, and that they are maximized at an intermediate level of transparency, which is larger than the one preferred by banks but lower than what would result in excessive deposit outflows. Our model also suggests that bank transparency and deposit insurance are complementary policy tools, and that bank regulators should adjust disclosure requirements for banks procyclically |
| Keywords: | bank transparency, bank runs, asset risk taking, banking stability, deposit insurance |
| JEL: | G21 G28 D83 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bofrdp:316423 |
| By: | Fetzer, Thiemo (University of Warwick, University of Bonn and Centre for Economic Policy Research); Guin, Benjamin (Bank of England); Netto, Felipe (Bank of England); Saidi, Farzad (University of Bonn and Centre for Economic Policy Research) |
| Abstract: | This paper examines how insurance companies monitor and react to cash‑flow shocks in commercial mortgage‑backed securities (CMBS). Using detailed micro data around the onset of the Covid pandemic, we show that lease expiration predicts commercial real estate mortgage delinquency, particularly for offices due to lower demand. Insurers monitor these risks and sell more exposed CMBS – mirrored by a surge in small banks holding CMBS. This monitoring effort also affects insurers’ trading in other assets, indicating limited risk assessment capacity. Our findings reveal that institutional investors actively monitor underlying asset risk and can even gain informational advantages over some banks. |
| Keywords: | Insurance sector; risk management; mortgage default; commercial real estate; CMBS; work from home |
| JEL: | G20 G21 G22 G23 |
| Date: | 2025–02–21 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1119 |
| By: | SeungJae Hwang |
| Abstract: | This paper examines the empirical failure of uncovered interest parity (UIP) and proposes a structural explanation based on a mean-reverting risk premium. We define a realized premium as the deviation between observed exchange rate returns and the interest rate differential, and demonstrate its strong mean-reverting behavior across multiple horizons. Motivated by this pattern, we model the risk premium using an Ornstein-Uhlenbeck (OU) process embedded within a stochastic differential equation for the exchange rate. Our model yields closed-form approximations for future exchange rate distributions, which we evaluate using coverage-based backtesting. Applied to USD/KRW data from 2010 to 2025, the model shows strong predictive performance at both short-term and long-term horizons, while underperforming at intermediate (3-month) horizons and showing conservative behavior in the tails of long-term forecasts. These results suggest that exchange rate deviations from UIP may reflect structured, forecastable dynamics rather than pure noise, and point to future modeling improvements via regime-switching or time-varying volatility. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06028 |
| By: | Carola Müller; Matias Ossandon Busch; Miguel Sarmiento; Freddy Pinzon-Puerto |
| Abstract: | We investigate the impact of large-scale investment fund redemptions on bank lending. Using detailed data on the link between commercial banks and investment funds in an emerging economy, we document that redemptions lead to a decrease in the demand for certificates of deposit and increasing volatility in this wholesale funding market. We find that banks subject to the fund-induced fragility in their funding markets adjust credit terms: while credit volumes remain stable, terms of credit deteriorate. Affected banks raise interest rates and reduce the maturity of newly issued loans. These findings showcase that wholesale deposit runs affect banks' incentives to engage in maturity transformation. |
| Keywords: | uninsured deposits, wholesale funding, liquidity risk, credit supply, non-bank financial intermediaries |
| JEL: | G01 G21 G23 E44 E58 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1263 |
| By: | Delfina Ricordi; Martín Sola; Fabio Spagnolo; Nicola Spagnolo |
| Abstract: | This paper investigates the relationship between financial markets and real economic activity. Based on a bivariate Markov switching model, we propose a procedure for analysing links between stock market volatility and output growth. The method provides a convenient way of interpreting the predictive content of different series’ first and second moments. We examine and discuss an empirical application of this procedure for a subset of developed countries (U.S., U.K., Japan, Germany, Italy and France). In the empirical analysis, we test whether changes in stock market volatility precede the change in the state of output growth. |
| Keywords: | Volatility of Stock Prices, Booms and Recessions, Markov Switching. |
| JEL: | C32 C52 C58 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:udt:wpecon:2025_03 |
| By: | George Woodman; Ruben S. Andrist; Thomas H\"aner; Damien S. Steiger; Martin J. A. Schuetz; Helmut G. Katzgraber; Marcin Detyniecki |
| Abstract: | We propose and implement modern computational methods to enhance catastrophe excess-of-loss reinsurance contracts in practice. The underlying optimization problem involves attachment points, limits, and reinstatement clauses, and the objective is to maximize the expected profit while considering risk measures and regulatory constraints. We study the problem formulation, paving the way for practitioners, for two very different approaches: A local search optimizer using simulated annealing, which handles realistic constraints, and a branch & bound approach exploring the potential of a future speedup via quantum branch & bound. On the one hand, local search effectively generates contract structures within several constraints, proving useful for complex treaties that have multiple local optima. On the other hand, although our branch & bound formulation only confirms that solving the full problem with a future quantum computer would require a stronger, less expensive bound and substantial hardware improvements, we believe that the designed application-specific bound is sufficiently strong to serve as a basis for further works. Concisely, we provide insurance practitioners with a robust numerical framework for contract optimization that handles realistic constraints today, as well as an outlook and initial steps towards an approach which could leverage quantum computers in the future. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16530 |
| By: | Zinuo You; John Cartlidge; Karen Elliott; Menghan Ge; Daniel Gold |
| Abstract: | Existing portfolio management approaches are often black-box models due to safety and commercial issues in the industry. However, their performance can vary considerably whenever market conditions or internal trading strategies change. Furthermore, evaluating these non-transparent systems is expensive, where certain budgets limit observations of the systems. Therefore, optimizing performance while controlling the potential risk of these financial systems has become a critical challenge. This work presents a novel Bayesian optimization framework to optimize black-box portfolio management models under limited observations. In conventional Bayesian optimization settings, the objective function is to maximize the expectation of performance metrics. However, simply maximizing performance expectations leads to erratic optimization trajectories, which exacerbate risk accumulation in portfolio management. Meanwhile, this can lead to misalignment between the target distribution and the actual distribution of the black-box model. To mitigate this problem, we propose an adaptive weight Lagrangian estimator considering dual objective, which incorporates maximizing model performance and minimizing variance of model observations. Extensive experiments demonstrate the superiority of our approach over five backtest settings with three black-box stock portfolio management models. Ablation studies further verify the effectiveness of the proposed estimator. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.13529 |
| By: | Stefan Avdjiev; Leonardo Gambacorta; Linda S. Goldberg; Stefano Schiaffi |
| Abstract: | The period after the Global Financial Crisis (GFC) was characterized by a considerable risk migration within global liquidity flows, away from cross-border bank lending towards international bond issuance. We show that the post-GFC shifts in the risk sensitivities of global liquidity flows are related to the tightness of the balance sheet (capital and leverage) constraints faced by international (bank and non-bank) lenders and to the migration of borrowers across funding sources. We document that the risk sensitivity of global liquidity flows is higher when funding is provided by financial intermediaries that are facing greater balance sheet constraints. We also provide evidence that the post-GFC migration of borrowers from cross-border loans to international debt securities was associated with a decline in the risk sensitivity of global liquidity flows to EME borrowers. |
| JEL: | F30 F34 F42 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33674 |
| By: | Afrasiab Mirza (Department of Economics, University of Birmingham); Eric Stephens (Department of Economics, Carleton University) |
| Abstract: | This paper considers the general equilibrium implications of moral hazard in private health insurance markets. We show that the structure of standard contracts gives rise to a pecuniary externality whereby individuals ignore the impact of their insurance purchases on the future price of care. At the equilibrium, individuals over-insure against health expenditure risk, and over-spend on medical services while facing an excessive price of care. Reducing insurance coverage at the margin can mitigate the externality by exerting downward pressure on prices, thereby raising welfare. |
| JEL: | D52 I11 I13 I18 |
| Date: | 2024–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:car:carecp:25-02 |
| By: | Martín Sola; Fabio Spagnolo; Francisco Terfi |
| Abstract: | Stock markets experience periods where stocks or market returns are consistently higher than their mean and other periods where the individual stocks and markets’ volatility fluctuates from high to low. Since these periods do not necessarily coincide, a related question is whether periods where individual stock markets are higher than their mean, usually identified as αs different from zero in the conditional regressions, disappear once the researcher accounts for changing states of the economy. In this spirit, we develop and estimate a state-dependent version of the CAPM pricing model that accounts for considerable swings in the data. We use U.S. financial data to assess the model’s validity and find support for a state-dependent version of the CAPM for the data under consideration. We show how important it is to consider changes in stock and market returns and changes in their variance-covariances, and that, when not accounting for changes in market conditions, may spuriously yield significant α values. We stress that to assess changes in the risk premium, we should not only focus on βs but also allow for changes in the market premium; otherwise, changes in risk premia may be over- or underestimated. In addition, the classification between investment opportunities may be mistaken for a single regime model, even when rolling regressions are used. |
| Keywords: | Non-diversifiable Risk Premium; Markov Chain; Structural Breaks. |
| JEL: | G00 G12 E44 C32 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:udt:wpecon:2025_02 |
| By: | JD Opdyke |
| Abstract: | We live in a multivariate world, and effective modeling of financial portfolios, including their construction, allocation, forecasting, and risk analysis, simply is not possible without explicitly modeling the dependence structure of their assets. Dependence structure can drive portfolio results more than many other parameters in investment and risk models, sometimes even more than their combined effects, but the literature provides relatively little to define the finite-sample distributions of dependence measures in useable and useful ways under challenging, real-world financial data conditions. Yet this is exactly what is needed to make valid inferences about their estimates, and to use these inferences for a myriad of essential purposes, such as hypothesis testing, dynamic monitoring, realistic and granular scenario and reverse scenario analyses, and mitigating the effects of correlation breakdowns during market upheavals (which is when we need valid inferences the most). This work develops a new and straightforward method, Nonparametric Angles-based Correlation (NAbC), for defining the finite-sample distributions of any dependence measure whose matrix of pairwise associations is positive definite (e.g. Pearsons, Kendalls Tau, Spearmans Rho, Chatterjees, Lancasters, Szekelys, and their many variants). The solution remains valid under marginal asset distributions characterized by notably different and varying degrees of serial correlation, non-stationarity, heavy-tailedness, and asymmetry. Notably, NAbCs p-values and confidence intervals remain analytically consistent at both the matrix level and the pairwise cell level. Finally, NAbC maintains validity even when selected cells in the matrix are frozen for a given scenario or stress test, that is, unaffected by the scenario, thus enabling flexible, granular, and realistic scenarios. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.15268 |
| By: | Masashige Hamano (School of Political Science and Economics, Waseda University) |
| Abstract: | This paper examines how heterogeneity in product-level tastes and firm-level technologies shapes macroeconomic fluctuations. We develop a general equilibrium model with multiproduct firms and endogenous entry, where firms adjust their product mix in response to aggregate shocks. Calibrated to U.S. data, the model replicates key business cycle moments and shows that low taste dispersion amplifies aggregate volatility by limiting per-product profit adjustments, whereas high dispersion dampens fluctuations. While firm-level productivity granularity also affects volatility, its impact is comparatively minor. A simplified analytical model reinforces these findings, highlighting the critical role of aggregate shock propagation to firm- and product-level fixed costs, as well as heterogeneity in tastes and technologies, in determining macroeconomic volatility. |
| Keywords: | Firm Heterogeneity, Multi-Product Firms, Business Cycles, Product Quality |
| JEL: | D24 E23 E32 L11 L60 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:wap:wpaper:2502 |
| By: | Brewer, Mike; Cominetti, Nye; Jenkins, Stephen P. |
| Abstract: | We first review research about income and earnings volatility and second provide new UK evidence about the latter using high‐quality administrative record data. The USA stands out as a high‐volatility country relative to the UK and other high‐income countries, but volatility levels have remained constant in these countries recently. Almost all research has considered volatility from an annual perspective whereas we provide new evidence about month‐to‐month earnings volatility. There is a distinct within‐year seasonal pattern to volatility, and volatility is highest for the top and bottom tenths of earners. High earnings volatility among top earners and its seasonality reflect pay bonus patterns whereas, for low earners, the instability of hours including zero‐hours contracts likely plays important roles. Our findings have relevance to the design of cash transfer support in the UK because the monthly reference periods it uses do not align with many earners' pay periods. |
| Keywords: | income volatility; earnings volatility; PAYE data; administrative record data; survey data |
| JEL: | D31 I31 J31 J38 |
| Date: | 2025–05–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127659 |