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on Risk Management |
| By: | A. I. C. Pereda |
| Abstract: | This paper examines risk measures, systemic risk, and default cascades in global equity markets using a network of 20 assets (13 from Brazil, 7 from developed markets) over the period 2015-2025. An adapted Gai-Kapadia framework models exposure-based networks ($\theta = 0.3, 0.5$), incorporating market correlations and volatility. Monte Carlo simulations ($n = 1000$) assess default cascades triggered by shocks ranging from 10\% to 50\%. Value-at-Risk (VaR) and Conditional VaR (CVaR) highlight higher tail risks in emerging markets. Results suggest that clustering in Brazilian assets amplifies cascades, while developed markets exhibit greater resilience. These insights contribute to financial stability policies. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.01969 |
| By: | Amin Haeri; Jonathan Vitrano; Mahdi Ghelichi |
| Abstract: | Risk management in finance involves recognizing, evaluating, and addressing financial risks to maintain stability and ensure regulatory compliance. Extracting relevant insights from extensive regulatory documents is a complex challenge requiring advanced retrieval and language models. This paper introduces RiskData, a dataset specifically curated for finetuning embedding models in risk management, and RiskEmbed, a finetuned embedding model designed to improve retrieval accuracy in financial question-answering systems. The dataset is derived from 94 regulatory guidelines published by the Office of the Superintendent of Financial Institutions (OSFI) from 1991 to 2024. We finetune a state-of-the-art sentence BERT embedding model to enhance domain-specific retrieval performance typically for Retrieval-Augmented Generation (RAG) systems. Experimental results demonstrate that RiskEmbed significantly outperforms general-purpose and financial embedding models, achieving substantial improvements in ranking metrics. By open-sourcing both the dataset and the model, we provide a valuable resource for financial institutions and researchers aiming to develop more accurate and efficient risk management AI solutions. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06293 |
| By: | Travis Cable; Amir Mani; Wei Qi; Georgios Sotiropoulos; Yiyuan Xiong |
| Abstract: | United States (US) IG bonds typically trade at modest spreads over US Treasuries, reflecting the credit risk tied to a corporation's default potential. During market crises, IG spreads often widen and liquidity tends to decrease, likely due to increased credit risk (evidenced by higher IG Credit Default Index spreads) and the necessity for asset holders like mutual funds to liquidate assets, including IG credits, to manage margin calls, bolster cash reserves, or meet redemptions. These credit and liquidity premia occur during market drawdowns and tend to move non-linearly with the market. The research herein refers to this non-linearity (during periods of drawdown) as downside convexity, and shows that this market behavior can effectively be captured through a short position established in IG Exchange Traded Funds (ETFs). The following document details the construction of three signals: Momentum, Liquidity, and Credit, that can be used in combination to signal entries and exits into short IG positions to hedge a typical active bond portfolio (such as PIMIX). A dynamic hedge initiates the short when signals jointly correlate and point to significant future hedged return. The dynamic hedge removes when the short position's predicted hedged return begins to mean revert. This systematic hedge largely avoids IG Credit drawdowns, lowers absolute and downside risk, increases annualised returns and achieves higher Sortino ratios compared to the benchmark funds. The method is best suited to high carry, high active risk funds like PIMIX, though it also generalises to more conservative funds similar to DODIX. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06289 |
| By: | Ojo, Marianne |
| Abstract: | Will the April 2nd Announcement generate its intended objectives? It’s still early days – however, it appears increasingly likely that negotiation outcomes – particularly between those significantly impacted by the Announcement, will be a major determinant in deciding whether the tariff hikes resulting from the April 2nd Announcement, will be short or long term. As of the 11th April, 2025, President Trump’s universal tariffs on China had amounted to 145% whilst China announced tariffs of 125% on U.S imports. Amongst other things, this paper aims to address complexities and challenges faced by regulators in identifying and assessing risk, problems arising from different perceptions of risk, and solutions aimed at countering problems of risk regulation. It will approach these issues through an assessment of explanations put forward to justify the growing importance of risks, well known risk theories such as cultural theory, risk society theory and governmentality theory. In addressing the problems posed as a result of the difficulty in quantifying risks, it will consider a means whereby risks can be quantified reasonably without the consequential effects which result from the dual nature of risk that is, risks emanating from the management of institutional risks. |
| Keywords: | risk;regulation;banks;regulators;audit; tariffs; financial stability; global financial environment; global stock markets |
| JEL: | E52 E58 G15 G17 K2 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124358 |
| By: | Emma Kroell; Sebastian Jaimungal; Silvana M. Pesenti |
| Abstract: | We consider the problem of an agent who faces losses over a finite time horizon and may choose to share some of these losses with a counterparty. The agent is uncertain about the true loss distribution and has multiple models for the losses. Their goal is to optimize a mean-variance type criterion with model ambiguity through risk sharing. We construct such a criterion by adapting the monotone mean-variance preferences of Maccheroni et al. (2009) to the multiple models setting and exploit a dual representation to mitigate time-consistency issues. Assuming a Cram\'er-Lundberg loss model, we fully characterize the optimal risk sharing contract and the agent's wealth process under the optimal strategy. Furthermore, we prove that the strategy we obtain is admissible and prove that the value function satisfies the appropriate verification conditions. Finally, we apply the optimal strategy to an insurance setting using data from a Spanish automobile insurance portfolio, where we obtain differing models using cross-validation and provide numerical illustrations of the results. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.02987 |
| By: | Gita Gopinath; Josefin Meyer; Carmen Reinhart; Christoph Trebesch |
| Abstract: | Theory suggests that corporate and sovereign bonds are fundamentally different, also because sovereign debt has no bankruptcy mechanism and is hard to enforce. We show empirically that the two assets are more similar than you think, at least when it comes to high-yield bonds over the past 20 years. We use rich new data to compare high-yield US corporate (“junk”) bonds to high-yield emerging market sovereign bonds 2002-2021. Investor experiences in these two asset classes were surprisingly aligned, with (i) similar average excess returns, (ii) similar average risk-return patterns (Sharpe ratios), (iii) similar default frequency, and (iv) comparable haircuts. A notable difference is that the average default duration is higher for sovereigns. Moreover, the two markets co-move differently with domestic and global factors. US “junk” bond yields are more closely linked to US market conditions such as US stock returns, US stock price volatility (VIX), or US monetary policy. |
| Keywords: | sovereign debt and default, default risk, corporate bonds, corporate default, junk bonds, chapter 11, crisis resolution. |
| JEL: | F30 G10 F40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11799 |
| By: | Nie, George Y. (Concordia University) |
| Abstract: | We argue that a payment’s risk approaches zero as maturity approaches zero, and that the central bank’s short-term rate best captures the risk-free rate of various assets. We employ two factors to model the expected risk-free rate that the market expects the current monetary policy to move towards the neutral rate over a certain period. Expecting that the T-bill risk (i.e., the macrorisk) largely reflects a country’s inflation risk, we measure the risk as a 5-year payment’s risk to be comparable across assets. To solve the model factors, we use repeated trials to minimize the prediction errors. Our models thus split US and Canada T-bill yields into the risk and risk-free rate, on average explaining 98.7% of the returns. The models assuming independence of the two returns show similar power in predicting T-bill returns, which can significantly simplify the formulas. We also find that the inclusion of a risk constant over maturity, which has a small value of several basis points, significantly reduces the prediction errors. The risk and the risk-free rate is the gateway to corporate the risk of various assets in the country. |
| Date: | 2025–03–01 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:2dazg_v2 |
| By: | Marco Migueis |
| Abstract: | Banks have incentives to operate with lower capital ratios than would be socially optimal due to deposit insurance and implicit government guarantees that socialize part of the costs of bank failures, particularly for the largest banks. Given these incentives, regulatory capital requirements contribute to the safety and soundness of individual banks and to financial stability by setting minimum expectations for the amount of loss-absorbing equity that banks need to employ in their funding. |
| Date: | 2025–03–28 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfn:2025-03-28-3 |
| By: | Andrei Neagu; Fr\'ed\'eric Godin; Leila Kosseim |
| Abstract: | Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep Reinforcement Learning (DRL) algorithms have been used to find optimal solutions to dynamic hedging problems by framing them as sequential decision-making problems. However, most previous work assesses the performance of only one or two DRL algorithms, making an objective comparison across algorithms difficult. In this paper, we compare the performance of eight DRL algorithms in the context of dynamic hedging; Monte Carlo Policy Gradient (MCPG), Proximal Policy Optimization (PPO), along with four variants of Deep Q-Learning (DQL) and two variants of Deep Deterministic Policy Gradient (DDPG). Two of these variants represent a novel application to the task of dynamic hedging. In our experiments, we use the Black-Scholes delta hedge as a baseline and simulate the dataset using a GJR-GARCH(1, 1) model. Results show that MCPG, followed by PPO, obtain the best performance in terms of the root semi-quadratic penalty. Moreover, MCPG is the only algorithm to outperform the Black-Scholes delta hedge baseline with the allotted computational budget, possibly due to the sparsity of rewards in our environment. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.05521 |
| By: | Chow, Nikolai Sheung-Chi |
| Abstract: | This study advances the understanding of risk measures and portfolio choice for investors exhibiting gain-loss dependent risk attitudes by integrating stochastic dominance (SD) concepts, including prospect stochastic dominance (PSD) and Markowitz stochastic dominance (MSD). We demonstrate that partial moments serve as effective risk measures, aligning with various SD criteria to capture diverse investor attitudes toward gains and losses. One contribution of this paper is the development of a decision-making criterion to identify the segment of the mean-variance efficient frontier that is efficient under different SD conditions, applicable to elliptical distributions. Leveraging partial moments, we adopt a portfolio optimization method that constructs portfolios dominating a benchmark from multiple SD perspectives, facilitating comparisons across gain-loss utility models. This approach enables a more direct comparison of alternative gain-loss utility models without relying on parameter assumptions, which often lead to differing risk-return priorities within a model. |
| Keywords: | Gain-Loss Utility, Mean-Variance Analysis, Stochastic Dominance, Partial Moments, Prospect Theory |
| JEL: | C0 G0 |
| Date: | 2025–04–17 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124440 |
| By: | Claudia Kl\"uppelberg; Mario Krali |
| Abstract: | We present a methodology for causal risk analysis in a network. Causal dependence is formulated by a max-linear structural equation model, which expresses each node variable as a max-linear function of its parental node variables in a directed acyclic graph and some exogenous innovation. We determine directed~paths~responsible~for extreme risk propagation in the network. We give algorithms for structure learning and parameter estimation and apply them to a network of financial data. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.00523 |
| By: | Alejandro Rodriguez Dominguez |
| Abstract: | Fundamental and necessary principles for achieving efficient portfolio optimization based on asset and diversification dynamics are presented. The Commonality Principle is a necessary and sufficient condition for identifying optimal drivers of a portfolio in terms of its diversification dynamics. The proof relies on the Reichenbach Common Cause Principle, along with the fact that the sensitivities of portfolio constituents with respect to the common causal drivers are themselves causal. A conformal map preserves idiosyncratic diversification from the unconditional setting while optimizing systematic diversification on an embedded space of these sensitivities. Causal methodologies for combinatorial driver selection are presented, such as the use of Bayesian networks and correlation-based algorithms from Reichenbach's principle. Limitations of linear models in capturing causality are discussed, and included for completeness alongside more advanced models such as neural networks. Portfolio optimization methods are presented that map risk from the sensitivity space to other risk measures of interest. Finally, the work introduces a novel risk management framework based on Common Causal Manifolds, including both theoretical development and experimental validation. The sensitivity space is predicted along the common causal manifold, which is modeled as a causal time system. Sensitivities are forecasted using SDEs calibrated to data previously extracted from neural networks to move along the manifold via its tangent bundles. An optimization method is then proposed that accumulates information across future predicted tangent bundles on the common causal time system manifold. It aggregates sensitivity-based distance metrics along the trajectory to build a comprehensive sensitivity distance matrix. This matrix enables trajectory-wide optimal diversification, taking into account future dynamics. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.05743 |
| By: | Stéphane Crépey (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité); Noufel Frikha (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Azar Louzi (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité); Gilles Pagès (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité) |
| Abstract: | Crépey, Frikha, and Louzi (2023) introduced a nested stochastic approximation algorithm and its multilevel acceleration to compute the value-at-risk and expected shortfall of a random financial loss. We hereby establish central limit theorems for the renormalized estimation errors associated with both algorithms as well as their averaged versions. Our findings are substantiated through a numerical example. |
| Keywords: | value-at-risk, expected shortfall, stochastic approximation, multilevel Monte Carlo, Polyak-Ruppert averaging, central limit theorem |
| Date: | 2023–11–24 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04304985 |
| By: | Markus Bibinger (Faculty of Mathematics and Computer Science, Institute of Mathematics, University of Würzburg); Jun Yu (Faculty of Business Administration, University of Macau); Chen Zhang (Faculty of Business Administration, University of Macau) |
| Abstract: | A multivariate fractional Brownian motion (mfBm) with component-wise Hurst exponents is used to model and forecast realized volatility. We investigate the interplay between correlation coefficients and Hurst exponents and propose a novel estimation method for all model parameters, establishing consistency and asymptotic normality of the estimators. Additionally, we develop a time-reversibility test, which is typically not rejected by real volatility data. When the data-generating process is a time-reversible mfBm, we derive optimal forecasting formulae and analyze their properties. A key insight is that an mfBm with different Hurst exponents and non-zero correlations can reduce forecasting errors compared to a one-dimensional model. Consistent with optimal forecasting theory, out-of-sample forecasts using the time-reversible mfBm show improvements over univariate fBm, particularly when the estimated Hurst exponents differ significantly. Empirical results demonstrate that mfBm-based forecasts outperform the (vector) HAR model. |
| Keywords: | Forecasting, Hurst exponent, multivariate fractional Brownian motion, realized volatility, rough volatility |
| JEL: | C12 C58 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202528 |
| By: | Agustin Mu\~noz Gonzalez; Juan Ignacio Sequeira; Ariel Dembling |
| Abstract: | This work analytically characterizes impermanent loss for automated market makers (AMMs) in decentralized markets such as Uniswap or Balancer (CPMM). We derive a static replication formula for the pool's value using a combination of European calls and puts. Furthermore, we establish a result guaranteeing hedging coverage for all final prices within a predefined interval. These theoretical results motivate a numerical example where we illustrate the strangle strategy using real cryptocurrency options data from Deribit, one of the most liquid markets available. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.21967 |
| By: | Mario Ghossoub; Bin Li; Benxuan Shi |
| Abstract: | We consider a monopoly insurance market with a risk-neutral profit-maximizing insurer and a consumer with Yaari Dual Utility preferences that distort the given continuous loss distribution. The insurer observes the loss distribution but not the risk attitude of the consumer, proxied by a distortion function drawn from a continuum of types. We characterize the profit-maximizing, incentive-compatible, and individually rational menus of insurance contracts, show that equilibria are separating, and provide key properties thereof. Notably, insurance coverage and premia are monotone in the level of risk aversion; the most risk-averse consumer receives full insurance $(\textit{efficiency at the top})$; the monopoly absorbs all surplus from the least-risk averse consumer; and consumers with a higher level of risk aversion induce a higher expected profit for the insurer. Under certain regularity conditions, equilibrium contracts can be characterized in terms of the marginal loss retention per type of consumer, and they consist of menus of layered deductible contracts, where each such layered structure is determined by the risk type of the consumer. In addition, we examine the effect of a fixed insurance provision cost on equilibria. We show that if the fixed cost is prohibitively high, then there will be no $\textit{ex ante}$ gains from trade. However, when trade occurs, separating equilibrium contracts always outperform pooling equilibrium contracts, and they are identical to those obtained in the absence of fixed costs, with the exception that only part of the menu is excluded. The excluded contracts are those designed for consumers with relatively lower risk aversion, who are less valuable to the insurer. Finally, we characterize incentive-efficient menus of contracts in the context of an arbitrary type space. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.01095 |
| 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. |
| Keywords: | global liquidity, international bank lending, international bond flows, emerging markets, advanced economies |
| JEL: | G10 F34 G21 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1262 |
| By: | Buchetti, Bruno; Bouteska, Ahmed; Harasheh, Murad; Santon, Alessandro |
| Abstract: | The primary objective of this study is to explore the dynamic relationships between equity returns or volatility and sentiment factors in European markets during both the periods preceding the COVID-19 pandemic, the COVID-19 itself, and the Russia-Ukraine war. We achieve this by applying the network methodology initially introduced by Diebold & Yilmaz (2014), along with its extensions based on realized measures and generalized forecast error variance decomposition, as proposed by Baruník & Křehlík (2018) and Chatziantoniou et al. (2023). Additionally, we investigate how the global sentiment factor influences the overall connectedness index by employing a quantile-on-quantile approach, following the methods outlined by Sim & Zhou (2015) and Bouri et al. (2022). To conduct our analysis, we utilize daily-frequency data encompassing the period from January 1, 2011, to December 31, 2023, covering the entirety of the COVID-19 pandemic in 2020 and the Russia-Ukraine conflict in 2022 across six European stock indices. Our primary discovery is the interconnectedness of both returns and sentiment. Furthermore, our resultsindicate that during the COVID-19 and Russia-Ukraine war, there is a notable increase in volatility spillovers among the analyzed stock indices, driven by the heightened interconnectedness between stock market returns. JEL Classification: G11, G12, G14, G40 |
| Keywords: | COVID-19, dynamic spillover and connectedness, European financial markets, investor sentiment, Russia-Ukraine war |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253050 |
| By: | Brewer, Mike (London School of Economics); Cominetti, Nye; Jenkins, Stephen P. (London School of Economics) |
| 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 play 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: | administrative record data, PAYE data, earnings volatility, income volatility, survey data |
| JEL: | D31 I31 J31 J38 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17808 |
| By: | Hamed Farahani; R. A. Serota |
| Abstract: | We study decades-long historic distributions of accumulated S\&P500 returns, from daily returns to those over several weeks. The time series of the returns emphasize major upheavals in the markets -- Black Monday, Tech Bubble, Financial Crisis and Covid Pandemic -- which are reflected in the tail ends of the distributions. De-trending the overall gain, we concentrate on comparing distributions of gains and losses. Specifically, we compare the tails of the distributions, which are believed to exhibit power-law behavior and possibly contain outliers. Towards this end we find confidence intervals of the linear fits of the tails of the complementary cumulative distribution functions on a log-log scale, as well as conduct a statistical U-test in order to detect outliers. We also study probability density functions of the full distributions of the returns with the emphasis on their asymmetry. The key empirical observations are that the mean of de-trended distributions increases near-linearly with the number of days of accumulation while the overall skew is negative -- consistent with the heavier tails of losses -- and depends little on the number of days of accumulation. At the same time the variance of the distributions exhibits near-perfect linear dependence on the number of days of accumulation, that is it remains constant if scaled to the latter. Finally, we discuss the theoretical framework for understanding accumulated returns. Our main conclusion is that the current state of theory, which predicts symmetric or near-symmetric distributions of returns cannot explain the aggregate of empirical results. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.24241 |
| By: | Černý, Aleš; Czichowsky, Christoph |
| Abstract: | The law of one price (LOP) broadly asserts that identical financial flows should command the same price. We show that when properly formulated, the LOP is the minimal condition for a well-defined mean–variance portfolio allocation framework without degeneracy. Crucially, the paper identifies a new mechanism through which the LOP can fail in a continuous-time L2 -setting without frictions, namely “trading from just before a predictable stopping time”, which surprisingly identifies LOP violations even for continuous price processes. Closing this loophole allows us to give a version of the “fundamental theorem of asset pricing” appropriate in the quadratic context, establishing the equivalence of the economic concept of the LOP with the probabilistic property of the existence of a local ℰ-martingale state price density. The latter provides unique prices for all square-integrable contingent claims in an extended market and subsequently plays an important role in mean–variance portfolio selection and quadratic hedging. Mathematically, we formulate a novel variant of the uniform boundedness principle for conditionally linear functionals on the L0-module of conditionally square-integrable random variables. We then study the representation of time-consistent families of such functionals in terms of stochastic exponentials of a fixed local martingale. |
| Keywords: | law of one price; efficient frontier; mean-variance portfolio selection; quadratic hedging; ℰ-density |
| JEL: | G11 G12 C61 |
| Date: | 2025–04–09 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:125805 |
| By: | Sydney Carlino; Nathan Foley-Fisher; Nathan Heinrich; Stéphane Verani |
| Abstract: | This note quantifies life insurers' role in the intermediation of public and private credit to risky firms. Since the 2007-09 financial crisis, the share of life insurers' general account assets exposed to below-investment-grade ('risky') corporate debt has roughly doubled. |
| Date: | 2025–03–21 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfn:2025-03-21-1 |
| By: | Dzemski, Andreas (Department of Economics, School of Business, Economics and Law, Göteborg University); Farago, Adam (Department of Economics, School of Business, Economics and Law, Göteborg University); Hjalmarsson, Erik (Department of Economics, School of Business, Economics and Law, Göteborg University); Kiss, Tamas (The School of Business, Örebro University, Sweden) |
| Abstract: | We analyze empirical estimation of the distribution of total payoffs for stock investments over very long horizons, such as 30 years. Formal results for recently proposed bootstrap estimators are derived and alternative parametric methods are proposed. All estimators should be viewed as inconsistent for longer investment horizons. Valid confidence bands are derived and should be the focus when performing inference. Empirically, confidence bands around long-run distributions are very wide and point estimates must be interpreted with great caution. Consequently, it is difficult to distinguish long-run aggregate return distributions across countries; long-run U.S. returns are not significantly different from global returns. |
| Keywords: | Estimation uncertainty; Long-run stock returns; Quantile estimation |
| JEL: | C58 G10 |
| Date: | 2025–04–28 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:gunwpe:0853 |