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on Risk Management |
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Issue of 2024‒03‒11
twenty-one papers chosen by |
| By: | Katerina Rigana; Ernst C. Wit; Samantha Cook |
| Abstract: | Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictive approach. This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO VaR), a novel methodology that significantly advances causal inference in financial risk analysis. Embracing a causal network framework, this method adeptly captures and analyses volatility and spillover effects, effectively setting it apart from conventional contagion-based VaR models. Causal-NECO VaR's key innovation lies in its ability to derive directional influences among assets from observational data, thereby offering robust risk predictions that remain invariant to market shocks and systemic changes. A comprehensive simulation study and the application to the Forex market show the robustness of the method. Causal-NECO VaR not only demonstrates predictive accuracy, but also maintains its reliability in unstable financial environments, offering clearer risk assessments even amidst unforeseen market disturbances. This research makes a significant contribution to the field of risk management and financial stability, presenting a causal approach to the computation of VaR. It emphasises the model's superior resilience and invariant predictive power, essential for navigating the complexities of today's ever-evolving financial markets. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06032&r=rmg |
| By: | Yi Shen; Zachary Van Oosten; Ruodu Wang |
| Abstract: | We introduce the concept of partial law invariance, generalizing the concept of law-invariant risk measures widely used in statistical and financial applications. This new concept is motivated by practical considerations of decision-making under uncertainty, thus connecting the literature on decision theory and that on financial risk management. We fully characterize partially law-invariant coherent risk measures via a novel representation formula, which, surprisingly, has little resemblance to the classical formula for law-invariant coherent risk measures. A notion of strong partial law invariance is introduced, allowing for a representation formula akin to the classical one. We propose a few classes of new risk measures, including partially law-invariant versions of the Expected Shortfall and the entropic risk measures, and illustrate their applications in risk assessment under model uncertainty. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.17265&r=rmg |
| By: | Böhnke, Victoria; Ongena, Steven; Paraschiv, Florentina; Reite, Endre J. |
| Abstract: | The internal ratings-based (IRB) approach maps bank risk profiles more adequately than the standardized approach. After switching to IRB, banks' risk-weighted asset (RWA) densities are thus expected to diverge, especially across countries with different supervisory strictness and risk levels. However, when examining 52 listed banks headquartered in 14 European countries that adopted the IRB approach, we observe a downward convergence of their RWA densities over time. We test whether this convergence can be entirely explained by differences in the size of the banks, loss levels, country risk, and/or time of IRB implementation. Our findings indicate that this is not the case. Whereas banks in high-risk countries with less strict regulation and/or supervision, reduce their RWA densities, banks elsewhere increase theirs. Especially for banks in high-risk countries, RWA densities seem to underestimate banks' economic risk. Hence, the IRB approach enables regulatory arbitrage, whereby authorities may only enforce strict supervision on capital requirements if they do not jeopardize bank existence. |
| Keywords: | Capital regulation, credit risk, internal ratings-based approach, regulatory arbitrage, risk-weighted assets |
| JEL: | G21 G28 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:283007&r=rmg |
| By: | Kristy Jansen; Sven Klingler; Angelo Ranaldo; Patty Duijm |
| Abstract: | Pension funds rely on interest rate swaps to hedge the interest rate risk arising from their liabilities. Analyzing unique data on Dutch pension funds, we show that this hedging behavior exposes pension funds to liquidity risk due to margin calls, which can be as large as 15% of their total assets. Our analysis uncovers three key findings: (i) pension funds with tighter regulatory constraints use swaps more aggressively; (ii) in response to rising interest rates, triggering margin calls, pension funds predominantly sell safe and short-term government bonds; (iii) we demonstrate that this procyclical selling adversely affects the prices of these bonds. |
| Keywords: | Pension funds; fixed income; interest rate swaps; liability hedging; liquidity risk; margin calls; price impact |
| JEL: | E43 G12 G18 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:801&r=rmg |
| By: | Yunhan Zhang (Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China); Qiang Ji (Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China); David Gabauer (Academy of Data Science in Finance, Vienna, Austria; Institute of Corporate Finance, Johannes Kepler University, Linz, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | By introducing a new generalized forecast error variance decomposition (GFEVD) approach that splits the same into its contemporaneous and lagged components, we investigate the risk spillover effects of different order moments, derived from intraday data, for the top 10 banks and top 10 oil and gas companies in the U.S., covering the period from December 29, 2017 to December 30, 2022. The study finds that, first, the dynamic total connectedness of all order moments is heterogeneous over time and economic events. Second, except realized volatility spillovers, the vast majority of overall spillovers are attributable to contemporaneous spillovers, while only a tiny fraction is associated with lagged spillovers. Finally, realized skewness (crash risk) and realized kurtosis (extreme events) in banks and oil and gas companies originate mainly from intra-industry rather than inter-industry transmission. |
| Keywords: | Banking connectedness; TVP-VAR; higher moments; dynamic connectedness; GFEVD decomposition |
| JEL: | C50 F65 G15 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202405&r=rmg |
| By: | Xisong Jin |
| Abstract: | This paper introduces a forward-looking bank-level stress testing framework for a large-scale system to assess three forms of banking system vulnerability– bank capital fragility, bank capital adequacy and bank solvency. Results for Luxembourg are provided with a decomposition by bank business model and domicile type. The paper goes on to assess how these systemic risk indicators are linked to macroeconomic variables, and investigates their predictive power for Luxembourg’s nominal GDP growth one year ahead. Several important findings are documented over 2003Q2 to 2023Q3. First, the systemic risk indicators responded to the main stock market crashes in a timely manner. However, contributions from different bank business models and domicile types varied over time. Second, association with key macroeconomic variables (interest rates, liquidity flow, euro area consumer confidence and business climate) depended on the different characteristics of systemic risk across bank business models. Third, the systemic risk indicators contributed to explaining nominal GDP growth one year ahead. However, the systemic risk component associated with search-for-yield behavior and fee & commission generating activities could also explain nominal GDP growth, suggesting that if banks became more dependent on these income sources, they could create financial stability issues in the long run. Overall, the framework provides a useful monitoring toolkit that tracks changes in forward-looking systemic risk and risk spillovers in the Luxembourg banking sector. |
| Keywords: | Financial stability, systemic risk, macro-prudential policy, dynamic dependence, banking business model, financial stress index, coronavirus COVID-19, macro-financial linkages. |
| JEL: | C1 E5 F3 G1 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:bcl:bclwop:bclwp182&r=rmg |
| By: | Loubergé, Henri (Université de Genève); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management) |
| Abstract: | The chapter reviews the evolution in risk and insurance economics over the past 50 years, first recalling the situation in 1973, then presenting the developments and new approaches that have flourished since then. We argue that these developments were only possible because steady advances were made in the economics of risk and uncertainty and in financial theory. Insurance economics has grown in importance to become a central theme in modern economics, providing not only practical examples and original data to illustrate new theories, but also inspiring new ideas that are relevant to the overall economy. |
| Keywords: | Insurance economics; optimal insurance protection; optimal self-protection; insurance pricing; insurance demand; economics of risk and uncertainty; financial economics; risk management; asymmetric information; insurance markets; climate finance |
| JEL: | A33 B15 D10 D20 D80 D82 G22 G32 G52 L22 |
| Date: | 2024–01–31 |
| URL: | http://d.repec.org/n?u=RePEc:ris:crcrmw:2024_001&r=rmg |
| By: | Blagov, Boris; Dirks, Maximilian; Funke, Michael |
| Abstract: | Using Russia as a case study and a global VAR model as a methodological tool, we analyze how heightened geopolitical risk shocks propagate across advanced economies and quantify the economic effects of these events. The global VAR impulse response functions in response to the skyrocketing Russian geopolitical risk shock after Russia's invasion of Ukraine revealed a contraction of GDP and an increase in inflation. Eastern European neighboring countries are particularly affected by the Russian geopolitical risk shock. We also document a strong component of the Russian geopolitical risk shock that is not driven by fossil fuel prices. |
| Abstract: | Unter Verwendung des Fallbeispiels Russland und eines globalen VAR-Modells als methodisches Instrument analysieren wir, wie sich Schocks eines erhöhten geopolitischen Risikos in fortgeschrittenen Volkswirtschaften verbreiten und quantifizieren die wirtschaftlichen Auswirkungen dieser Ereignisse. Die globalen VAR-Impuls-Antwort-Funktionen als Reaktion auf den sprunghaften Anstieg des geopolitischen Risikos in Russland nach dem Einmarsch in die Ukraine zeigen einen Rückgang des BIPs und einen Anstieg der Inflation. Staaten in Osteuropa sind von diesem geopolitischen Risikoschock in Russland besonders betroffen. Ein Großteil der beobachteten Effekte wird nicht durch Energiepreise getrieben. |
| Keywords: | Geopolitical risk, international business cycle transmission, global VAR model, Russia |
| JEL: | C32 E32 F51 F52 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:282988&r=rmg |
| By: | Alexandra Moura; Carlos Oliveira |
| Abstract: | We consider an investment model in which a firm decides to invest in the market, taking into account its future revenue and the possible occurrence of adverse events that may impact its reputation. The firm can buy an insurance contract at the investment time to mitigate reputation risk. The firm decides when to enter the market and the insurance strategy that maximizes its value. We consider three types of insurance contracts and different premium principles. We provide analytical conditions for the optimum and study several numerical examples. Results show that the firm’s optimal strategy depends on the risk size, the firm’s risk aversion, and the insurance premium. |
| Keywords: | Reputaion Risk, Insurance, Risk Mitigation, Investment Strategies, Real Options. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:ise:remwps:wp03092024&r=rmg |
| By: | Jan L. M. Dhaene; Moshe A. Milevsky |
| Abstract: | There is little disagreement among insurance actuaries and financial economists about the societal benefits of longevity-risk pooling in the form of life annuities, defined benefit pensions, self-annuitization funds, and even tontine schemes. Indeed, the discounted value or cost of providing an income for life is lower -- in other words, the amount of upfront capital required to generate a similar income stream with the same level of statistical safety is lower -- when participants pool their financial resources versus going it alone. Moreover, when participants' financial circumstances and lifespans are homogenous, there is consensus on how to share the "winnings" among survivors, namely by distributing them equally among survivors, a.k.a. a uniform rule. Alas, what is lesser-known and much more problematic is allocating the winnings in such a pool when participants differ in wealth (contributions) and health (longevity), especially when the pools are relatively small in size. The same problems arise when viewed from the dual perspective of decentralized risk sharing (DRS). The positive correlation between health and income and the fact that wealthier participants are likely to live longer is a growing concern among pension and retirement policymakers. With that motivation in mind, this paper offers a modelling framework for distributing longevity-risk pools' income and benefits (or tontine winnings) when participants are heterogeneous. Similar to the nascent literature on decentralized risk sharing, there are several equally plausible arrangements for sharing benefits (a.k.a. "skinning the cat") among survivors. Moreover, the selected rule depends on the extent of social cohesion within the longevity risk pool, ranging from solidarity and altruism to pure individualism. In sum, actuarial science cannot really offer or guarantee uniqueness, only a methodology. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.00855&r=rmg |
| By: | Jingyi Gu; Wenlu Du; Guiling Wang |
| Abstract: | Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality. This multi-faceted generator informs the creation of risk-sensitive intervals through statistical inference, incorporating horizon-wise insights. The interval's width is carefully adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC's evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.10760&r=rmg |
| By: | Audrius Jukonis; Elisa Letizia; Linda Rousova |
| Abstract: | Stricter derivative margin requirements have increased the demand for liquid collateral, but euro area investment funds, which use derivatives extensively, have been reducing their liquid asset holdings. Using transaction-by-transaction derivatives data, we assess whether the current levels of funds’ holdings of cash and other highly liquid assets would be adequate to meet funds’ liquidity needs to cover variation margin calls on derivatives under a range of stress scenarios. The estimates indicate that between 13 percent and 33 percent of euro area funds with sizeable derivatives exposures may not have sufficient liquidity buffers to meet the calls under adverse market shocks. As a result, they are likely to redeem money market fund (MMF) shares, procyclically sell assets, and draw on credit lines, thus amplifying the market dynamics under such stress scenarios. Our findings highlight the importance of further work to assess the potential role of macroprudential policies for nonbanks, particularly regarding liquidity risk in funds. |
| Keywords: | variation margin; EMIR data; market stress; big data; nonbank financial intermediaries |
| Date: | 2024–02–09 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2024/026&r=rmg |
| By: | Zhenglong Li; Vincent Tam; Kwan L. Yeung |
| Abstract: | Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years. In many cases, due to the very complex correlations among various financial sectors, and the fluctuating trends in different financial markets, a deep or reinforcement learning based agent can be biased in maximising the total returns of the newly formulated investment portfolio while neglecting its potential risks under the turmoil of various market conditions in the global or regional sectors. Accordingly, a multi-agent and self-adaptive framework namely the MASA is proposed in which a sophisticated multi-agent reinforcement learning (RL) approach is adopted through two cooperating and reactive agents to carefully and dynamically balance the trade-off between the overall portfolio returns and their potential risks. Besides, a very flexible and proactive agent as the market observer is integrated into the MASA framework to provide some additional information on the estimated market trends as valuable feedbacks for multi-agent RL approach to quickly adapt to the ever-changing market conditions. The obtained empirical results clearly reveal the potential strengths of our proposed MASA framework based on the multi-agent RL approach against many well-known RL-based approaches on the challenging data sets of the CSI 300, Dow Jones Industrial Average and S&P 500 indexes over the past 10 years. More importantly, our proposed MASA framework shed lights on many possible directions for future investigation. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.00515&r=rmg |
| By: | Johannes Carow (Johannes Gutenberg University Mainz); Niklas M. Witzig (Johannes Gutenberg University Mainz) |
| Abstract: | We study the impact of time pressure on strategic risk-taking of professional chess players. We propose a novel machine-learning-based measure for the degree of strategic risk of a single chess move and apply this measure to the 2013-2023 FIDE Chess World Cups that allow for plausibly exogenous variation in thinking time. Our results indicate that time pressure leads chess players to opt for more risk-averse moves. We additionally provide correlational evidence for strategic loss aversion, a tendency for risky moves after a mistake/ in a disadvantageous position. This suggests that high-proficiency decision-makers in highstake situations react to time pressure and contextual factors more broadly. We discuss the origins and implication of this finding in our setting. |
| Keywords: | Chess, Risk, Time Pressure, Loss Aversion, Machine Learning |
| JEL: | C26 C45 D91 |
| Date: | 2024–02–22 |
| URL: | http://d.repec.org/n?u=RePEc:jgu:wpaper:2404&r=rmg |
| By: | Jose J. Canals-Cerda |
| Abstract: | I examine the challenges of economic forecasting and model misspecification errors confronted by financial institutions implementing the novel current expected credit loss (CECL) allowance methodology and its impact on model risk and bias in CECL projections. We document the increased sensitivity to model and macroeconomic forecasting error of the CECL framework with respect to the incurred loss framework that it replaces. An empirical application illustrates how to leverage simple machine learning (ML) strategies and statistical principles in the design of a nimble and flexible CECL modeling framework. We show that, even in consumer loan portfolios with tens of millions of loans, like mortgage, auto, or credit card portfolios, one can develop, estimate, and deploy an array of models quickly and efficiently, and without a forecasting performance penalty. Drawing on more than 20 years of auto loans data and the experience from the Great Recession and the COVID-19 pandemic, we leverage basic econometric principles to identify strategies to deal with biased model projections in times of high economic uncertainty. We advocate for a focus on resiliency and adaptability of models and model infrastructures to novel shocks and uncertain economic conditions. |
| Keywords: | CECL; Allowance for Loan and Lease Losses; Accounting Regulations; Model Risk |
| JEL: | G01 G21 G28 G50 M41 |
| Date: | 2024–02–13 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:97748&r=rmg |
| By: | Camilo Gómez (Central Bank of Colombia); Daniela Rodríguez-Novoa (Central Bank of Colombia) |
| Abstract: | This paper examines the relationship between three government support measures (debt moratorium, credit guarantee programs, and payroll subsidies) and the firm's payment behavior on loans in Colombia. To do so, we take advantage of the COVID-19 pandemic and use it as a case study. Using highly granular data at the bank-firm level and a difference-in-difference approach, we find that firms subject to debt reliefs and government guarantee programs experienced a lower probability of default while these policies were in force. Subsequently, once the programs ended, the dynamic of the payment behavior of these firms was similar to that of those untreated. On the contrary, payroll subsidies did not affect firms' payment behavior. Regarding the effect on banks' risk assessment, our results suggest that participation in relief programs provided banks with new information about debtors' risk, which could indicate unintended consequences of government support programs. |
| Keywords: | firm support; credit default; credit risk |
| JEL: | G18 G21 G38 |
| Date: | 2024–02–08 |
| URL: | http://d.repec.org/n?u=RePEc:gii:giihei:heidwp03-2024&r=rmg |
| By: | Bernhard Hientzsch |
| Abstract: | We consider two data driven approaches, Reinforcement Learning (RL) and Deep Trajectory-based Stochastic Optimal Control (DTSOC) for hedging a European call option without and with transaction cost according to a quadratic hedging P&L objective at maturity ("variance-optimal hedging" or "final quadratic hedging"). We study the performance of the two approaches under various market environments (modeled via the Black-Scholes and/or the log-normal SABR model) to understand their advantages and limitations. Without transaction costs and in the Black-Scholes model, both approaches match the performance of the variance-optimal Delta hedge. In the log-normal SABR model without transaction costs, they match the performance of the variance-optimal Barlett's Delta hedge. Agents trained on Black-Scholes trajectories with matching initial volatility but used on SABR trajectories match the performance of Bartlett's Delta hedge in average cost, but show substantially wider variance. To apply RL approaches to these problems, P&L at maturity is written as sum of step-wise contributions and variants of RL algorithms are implemented and used that minimize expectation of second moments of such sums. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.08600&r=rmg |
| By: | Prehn, Soren |
| Abstract: | It is common practice in the literature to apply the same hedging practices (i.e., full hedging and minimum variance hedging) to storable and non-storable commodities. But is this approach also suitable for fluid milk? Dairy farmers have very different hedging objectives than grain farmers. The former want to lock in profitable forward prices for fluid milk, while the latter are looking for profitable storage margins for grain. In this paper, we will discuss not only why standard hedging practices are inappropriate when the goal is to lock in a forward price for fluid milk but also which hedging practice should be used instead. |
| Keywords: | Agribusiness |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iamodp:340077&r=rmg |
| By: | Michael S. Barr |
| Date: | 2024–02–14 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgsq:97753&r=rmg |
| By: | Eungik Lee; Kathleen Ngangoue; Andrew Schotter; Maryse Kathleen Ngangoue |
| Abstract: | We compare preferences for temporal resolution when uncertainty is resolved over a probability rather than a value. In various frameworks–e.g., Kreps and Porteus (1978)–, preferences over gradual versus one-shot resolution do not depend on whether values or probabilities define the main object of uncertainty. In our experiment, however, most subjects resolved uncertain values gradually but uncertain probabilities all at once–both in the gain and loss frames. This systematic discrepancy motivates an explanation for it that we call “process utility”, which highlights the importance of information processing when deducing revealed preferences for temporal resolution from choice data. |
| Keywords: | resolution of uncertainty, probability, gradual resolution, one-shot resolution, process utility, non-instrumental information, Kreps-Porteus |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10898&r=rmg |
| By: | Elena Capatina; Gary Hansen; Minchung Hsu |
| Abstract: | This paper compares the impact of long term care (LTC) risk on single and married households and studies the roles played by informal care (IC), consumption sharing within households, and Medicaid in insuring this risk. We develop a life-cycle model where individuals face survival and health risk, including the possibility of becoming highly disabled and needing LTC. Households are heterogeneous in various important dimensions including education, productivity, and the age difference between spouses. Health evolves stochastically. Agents make consumption-savings decisions in a framework featuring an LTC statedependent utility function. We find that household expenditures increase significantly when LTC becomes necessary, but married individuals are well insured against LTC risk due to IC. However, they still hold considerable assets due to the concern for the spouse who might become a widow/widower and can expect much higher LTC costs. IC significantly reduces precautionary savings for middle and high income groups, but interestingly, it encourages asset accumulation among low income groups because it reduces the probability of meanstested Medicaid LTC. |
| Keywords: | Long Term Care, Household Risk, Precautionary Savings, Medicaid |
| JEL: | D91 E21 H31 I10 I38 J14 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:acb:cbeeco:2023-697&r=rmg |