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on Risk Management |
| By: | Ahnert, Toni (European Central Bank); Bertsch, Christoph (Research Department, Central Bank of Sweden); Leonello, Agnese (European Central Bank); Marquez, Robert (University of California, Davis) |
| Abstract: | Shocks to banks’ ability to raise liquidity at short notice can lead to depositor panics, as evidenced by recent bank failures. Why don’t banks take a more active role in managing these risks? In a standard bank-run model, we show that risk management failures are most prevalent when exposures are more severe and managing risk would be particularly valuable. Bank capital and deposit insurance coverage act as substitutes for risk management on the intensive margin but as complements on its extensive margin, encouraging the adoption of risk management operations. We provide insights for the appropriate regulation of bank risk-management operations. |
| Keywords: | Banking crises; depositor withdrawals; asset valuations; risk management |
| JEL: | G01 G21 G23 |
| Date: | 2024–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:rbnkwp:0441 |
| By: | G.M. Gallo; O. Okhrin; G. Storti |
| Abstract: | This paper compares the accuracy of tail risk forecasts with a focus on including realized skewness and kurtosis in "additive" and "multiplicative" models. Utilizing a panel of 960 US stocks, we conduct diagnostic tests, employ scoring functions, and implement rolling window forecasting to evaluate the performance of Value at Risk (VaR) and Expected Shortfall (ES) forecasts. Additionally, we examine the impact of the window length on forecast accuracy. We propose model specifications that incorporate realized skewness and kurtosis for enhanced precision. Our findings provide insights into the importance of considering skewness and kurtosis in tail risk modeling, contributing to the existing literature and offering practical implications for risk practitioners and researchers. |
| Keywords: | Realized Skewness;Realized Kurtosis;Value at Risk;CAViaR;Expected Shortfall |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202416 |
| By: | Anna Kiriliouk; Chen Zhou |
| Abstract: | This book chapter illustrates how to apply extreme value statistics to financial time series data. Such data often exhibits strong serial dependence, which complicates assessment of tail risks. We discuss the two main approches to tail risk estimation, unconditional and conditional quantile forecasting. We use the S&P 500 index as a case study to assess serial (extremal) dependence, perform an unconditional and conditional risk analysis, and apply backtesting methods. Additionally, the chapter explores the impact of serial dependence on multivariate tail dependence. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18643 |
| By: | Matteo Malavasi (School of Risk and Actuarial Studies, UNSW Business School, University of New South Wales, Australia); Gareth W. Peters (Department of Statistics and Applied Probability, University of California Santa Barbara, USA); Stefan Treuck (Department of Actuarial Studies and Business Analytics, Macquarie University, Australia); Pavel V. Shevchenko (Department of Actuarial Studies and Business Analytics, Macquarie University, Australia); Jiwook Jang (Department of Actuarial Studies and Business Analytics, Macquarie University, Australia); Georgy Sofronov (School of Mathematical and Physical Sciences, Macquarie University, Australia) |
| Abstract: | Cyber risk classifications are widely used in the modeling of cyber event distributions, yet their effectiveness in out of sample forecasting performance remains underexplored. In this paper, we analyse the most commonly used classifications and argue in favour of switching the attention from goodness-of-fit and in-sample predictive performance, to focusing on the out-of sample forecasting performance. We use a rolling window analysis, to compare cyber risk distribution forecasts via threshold weighted scoring functions. Our results indicate that business motivated cyber risk classifications appear to be too restrictive and not flexible enough to capture the heterogeneity of cyber risk events. We investigate how dynamic and impact-based cyber risk classifiers seem to be better suited in forecasting future cyber risk losses than the other considered classifications. These findings suggest that cyber risk types provide limited forecasting ability concerning cyber event severity distribution, and cyber insurance ratemakers should utilize cyber risk types only when modeling the cyber event frequency distribution. Our study offers valuable insights for decision-makers and policymakers alike, contributing to the advancement of scientific knowledge in the field of cyber risk management. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.05297 |
| By: | Jun Cai; Zhanyi Jiao; Tiantian Mao |
| Abstract: | The expected regret and target semi-variance are two of the most important risk measures for downside risk. When the distribution of a loss is uncertain, and only partial information of the loss is known, their worst-case values play important roles in robust risk management for finance, insurance, and many other fields. Jagannathan (1977) derived the worst-case expected regrets when only the mean and variance of a loss are known and the loss is arbitrary, symmetric, or non-negative. While Chen et al. (2011) obtained the worst-case target semi-variances under similar conditions but focusing on arbitrary losses. In this paper, we first complement the study of Chen et al. (2011) on the worst-case target semi-variances and derive the closed-form expressions for the worst-case target semi-variance when only the mean and variance of a loss are known and the loss is symmetric or non-negative. Then, we investigate worst-case target semi-variances over uncertainty sets that represent undesirable scenarios faced by an investors. Our methods for deriving these worst-case values are different from those used in Jagannathan (1977) and Chen et al. (2011). As applications of the results derived in this paper, we propose robust portfolio selection methods that minimize the worst-case target semi-variance of a portfolio loss over different uncertainty sets. To explore the insights of our robust portfolio selection methods, we conduct numerical experiments with real financial data and compare our portfolio selection methods with several existing portfolio selection models related to the models proposed in this paper. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.01732 |
| By: | Vanderveken, Rodolphe (Université catholique de Louvain, LIDAM/LFIN, Belgium); Lassance, Nathan (Université catholique de Louvain, LIDAM/LFIN, Belgium); Vrins, Frédéric (Université catholique de Louvain, LIDAM/LFIN, Belgium) |
| Abstract: | Estimation risk in portfolio selection can be mitigated with sparse approaches such as lasso that penalizes for the norm of the portfolio weights and excludes assets from the investment universe. The latter are revealed a posteriori, by identifying which assets receive an optimal weight of zero. We show instead that in the presence of parameter uncertainty, it is desirable to remove assets before computing the portfolio weights. In particular, we show that the optimal portfolio size strikes a tradeoff between accessing additional investment opportunities and limiting estimation risk. Our approach disentangles the determination of the optimal portfolio size from the asset selection rule, making it more easily implementable and robust to estimation risk than alternative sparse methods. Empirically, our restricted portfolios substantially outperform their counterparts applied to all available assets. Our methodology renders portfolio theory valuable even when the full dataset dimension is comparable to the sample size. |
| Keywords: | Portfolio selection ; estimation risk ; dimension reduction ; out-of-sample performance ; portfolio combination rules |
| JEL: | G11 G12 |
| Date: | 2024–07–05 |
| URL: | https://d.repec.org/n?u=RePEc:ajf:louvlf:2024004 |
| By: | Peijun Liu (Graduate School of Economics, Osaka University) |
| Abstract: | This paper investigates the association between ownership structure with business risk disclosure in Japan, and the relationship between information content of risk disclosure and investors' risk perception. In the sample of Japanese firms over the period 2014-2021, the main results indicate a significant and nonmonotonic relationship between managerial ownership and annual modification in business risk disclosure. In particular, the modification of business risk disclosure decreases as managerial ownership increases for both high and low levels of management share holdings, while it increases for intermediate levels of it. In addition, I find that the annual increase in business risk disclosure is negatively associated with changes in daily stock return volatility and trading volume, suggesting a greater opinion convergence among investors after the release of business risk disclosure. This study contributes to existing literature in support of the nonboilerplate argument and informativeness of risk disclosure. |
| Keywords: | Managerial Ownership, Business Risk Disclosure, Narrative Financial Disclosure, Information Content, Market Reactions |
| JEL: | M41 M48 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:osk:wpaper:2411 |
| By: | Afees A. Salisu (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Ahamuefula E. Ogbonna (Centre for Econometrics & Applied Research, Ibadan, Nigeria.); Elie Bouri (School of Business, Lebanese American University, Lebanon.); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | Using generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) model with monthly Economic Policy Uncertainty (EPU) index and daily stock volatility of 149 banks in the United States from August 2000 to August 2023, we show that EPU plays a significant role in predicting bank stock volatility. Across the groups of large, mid, and small cap banks, stock volatility tends to increase in response to EPU, suggesting that growing uncertainty induces higher volatility in bank stocks. EPU has a stronger impact on large-cap banks. The outperformance of the GARCH-MIDAS-EPU model holds in an out-of-sample analysis, regardless of market capitalization and forecast horizons. |
| Keywords: | Economic policy uncertainty (EPU), Bank-level stock returns volatility, GARCH-MIDAS model |
| JEL: | C32 C53 D80 G10 G21 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202444 |
| By: | Simon Levy; Maxime L. D. Nicolas |
| Abstract: | This paper presents a novel approach to evaluating blue-chip art as a viable asset class for portfolio diversification. We present the Arte-Blue Chip Index, an index that tracks 100 top-performing artists based on 81, 891 public transactions from 157 artists across 584 auction houses over the period 1990 to 2024. By comparing blue-chip art price trends with stock market fluctuations, our index provides insights into the risk and return profile of blue-chip art investments. Our analysis demonstrates that a 20% allocation of blue-chip art in a diversified portfolio enhances risk-adjusted returns by around 20%, while maintaining volatility levels similar to the S&P 500. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18816 |
| By: | Clements, Adam; Vasnev, Andrey L. |
| Abstract: | Forecasts of the covariance matrix of returns is a crucial input into portfolio construction. In recent years multivariate version of the Heterogenous AutoRegressive (HAR) models have been designed to utilise realised measures of the covariance matrix to generate forecasts. This paper shows that combining forecasts from simple HAR-like models provide more coefficients estimates, stable forecasts and lower portfolio turnover. The economic benefits of the combination approach become crucial when transactions costs are taken into account. This combination approach also provides benefits in the context of direct forecasts of the portfolio weights. Economic benefits are observed at both 1-day and 1-week ahead forecast horizons. |
| Keywords: | Realized volatility, realized covariance, forecast combination, HAR model, multivariate HAR, portfolio |
| JEL: | C53 C58 |
| Date: | 2023–11–03 |
| URL: | https://d.repec.org/n?u=RePEc:syb:wpbsba:2123/31836 |
| By: | Luigi Guiso (EIEF and CEPR); Tullio Jappelli (University of Naples Federico II, CSEF, and CEPR) |
| Abstract: | We use panel data from the 2023-24 Italian Survey of Consumer Expectations which provides information on the expected consumption growth, income growth, energy prices, health expenditure distributions, and expectations related to aggregate variables (GDP growth, inflation, unemployment, house prices, interest rates). We quantify the impact of underlying risks on the expected consumption risk estimating the pass-through coefficients of the individual and aggregate risks. Idiosyncratic risks account for 75% of the predicted consumption risk: health risk has the largest impact, followed by income risk. We find that aggregate risks also matter, especially the expected GDP variability and increase in house prices but account for less than 20% of the consumption risk. Thus, most of the uncertainty harming consumer welfare is due not to business cycle but to idiosyncratic shocks. The income risk pass-through is larger for young working individuals with low levels of cash-in-hand and reflects their greater exposure and fewer insurance opportunities. In the final step of our analysis we use subjective expectations data and an instrumental variables approach and show that expected consumption growth is related positively to expected consumption risk, as predicted by precautionary savings models. Our estimates imply a coefficient of relative prudence in the plausible range of 2-3. |
| Keywords: | Consumption Risk; Income Risk; Health Risk; Aggregate Risk. |
| JEL: | D12 D14 D15 C8 C99 |
| Date: | 2024–07–05 |
| URL: | https://d.repec.org/n?u=RePEc:sef:csefwp:732 |
| By: | Cai, Zhaokun (Stevens Institute of Technology); Cui, Zhenyu (Stevens Institute of Technology); Lassance, Nathan (Université catholique de Louvain, LIDAM/LFIN, Belgium); Simaan, Majeed (Stevens Institute of Technology) |
| Abstract: | When designing and evaluating estimators, the mean squared error (MSE) is the most commonly used generic statistical loss function because it captures the bias-variance tradeoff and allows easy analytical and numerical treatment. However, MSE estimators are often applied to decision problems for which the loss function is different, raising questions about how much value there is in using a generic statistical loss function like the MSE rather than a decision loss function. We elucidate this question through the lens of the portfolio selection problem by showing that for several important portfolio rules, there is a positive linear relation between the MSE and a portfolio-decision loss function. Moreover, shrinkage portfolio estimators derived under these two loss functions are typically close to each other. Our findings highlight the economic value of MSE to serve as a general-purpose statistical loss function in portfolio selection. |
| Keywords: | Loss functions ; decision theory ; out-of-sample risk ; investment |
| JEL: | G11 |
| Date: | 2024–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:ajf:louvlf:2024003 |
| By: | Sanjay Sathish; Charu C Sharma |
| Abstract: | Our research presents a new approach for forecasting the synchronization of stock prices using machine learning and non-linear time-series analysis. To capture the complex non-linear relationships between stock prices, we utilize recurrence plots (RP) and cross-recurrence quantification analysis (CRQA). By transforming Cross Recurrence Plot (CRP) data into a time-series format, we enable the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for predicting stock price synchronization through both regression and classification. We apply this methodology to a dataset of 20 highly capitalized stocks from the Indian market over a 21-year period. The findings reveal that our approach can predict stock price synchronization, with an accuracy of 0.98 and F1 score of 0.83 offering valuable insights for developing effective trading strategies and risk management tools. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.06728 |
| By: | Hang Gao; Shuohua Yang; Xinli Liu |
| Abstract: | Weather parametric insurance relies on weather indices rather than actual loss assessments, enhancing claims efficiency, reducing moral hazard, and improving fairness. In the context of increasing climate change risks, despite growing interest and demand, , weather parametric insurance's market share remains limited due to inherent basis risk, which is the mismatch between actual loss and payout, leading to loss without payout or payout without loss. This paper proposes a novel empirical research using Monte Carlo simulations to test whether basis risk can be managed through diversification and hedged like other risks. Key findings include: Firstly, portfolio basis risk and volatility decrease as the number of contracts increases. Secondly, spatial relationships significantly impact basis risk, with risk levels correlating with the ratio between insured location, weather station, and disaster footprint radius, and thirdly, event severity does not significantly impact basis risk, suggesting that catastrophic disaster severity should not hinder parametric insurance development. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.16599 |
| By: | Hendrik Jenett; Maximilian Nagl; Cathrine Nagl; McKay Price; Wolfgang Schäfers |
| Abstract: | In the current context of heighted market tensions driven by rising interest rates, there is vital interest for both researchers and practitioners to understand the dynamics of Real Estate Investment Trust (REIT) returns and their accompanying uncertainties. To address this concern, we examine the drivers of REIT returns and volatility in a time-varying framework, spanning the modern REIT era (1991 to 2022). Our study is the first to simultaneously forecast both REIT returns and their associated volatility using an artificial neural network. We contribute to the literature by opening the black-box character of neural networks, enabling the identification of individual feature impacts on predictions and their evolution over time.The key focus revolves around understanding how the influence of accounting and macroeconomic variables changes during periods of financial crises compared to non-crisis periods. The results showcase superior predictive capabilities of the neural network compared to conventional regression models. We shed light on the intricate interplay of diverse variables influencing the performance of REITs. Our findings hold implications for investors, policymakers and researchers navigating the complex landscape of real estate investments in a dynamically evolving market environment. |
| Keywords: | Machine Learning; Neural Network; REIT Return; Volatility |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-107 |
| By: | Xialu Liu; John Guerard; Rong Chen; Ruey Tsay |
| Abstract: | Searching for new effective risk factors on stock returns is an important research topic in asset pricing. Factor modeling is an active research topic in statistics and econometrics, with many new advances. However, these new methods have not been fully utilized in asset pricing application. In this paper, we adopt the factor models, especially matrix factor models in various forms, to construct new statistical factors that explain the variation of stock returns. Furthermore, we evaluate the contribution of these statistical factors beyond the existing factors available in the asset pricing literature. To demonstrate the power of the new factors, U.S. monthly stock data are analyzed and the partial F test and double selection LASSO method are conducted. The results show that the new statistical factors bring additional information and add explanatory power in asset pricing. Our method opens a new direction for portfolio managers to seek additional risk factors to improve the estimation of portfolio returns. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.17182 |
| By: | Hayley Clatterbuck; Clinton Castro; Arvo Mu\~noz Mor\'an |
| Abstract: | Agentic AIs $-$ AIs that are capable and permitted to undertake complex actions with little supervision $-$ mark a new frontier in AI capabilities and raise new questions about how to safely create and align such systems with users, developers, and society. Because agents' actions are influenced by their attitudes toward risk, one key aspect of alignment concerns the risk profiles of agentic AIs. Risk alignment will matter for user satisfaction and trust, but it will also have important ramifications for society more broadly, especially as agentic AIs become more autonomous and are allowed to control key aspects of our lives. AIs with reckless attitudes toward risk (either because they are calibrated to reckless human users or are poorly designed) may pose significant threats. They might also open 'responsibility gaps' in which there is no agent who can be held accountable for harmful actions. What risk attitudes should guide an agentic AI's decision-making? How might we design AI systems that are calibrated to the risk attitudes of their users? What guardrails, if any, should be placed on the range of permissible risk attitudes? What are the ethical considerations involved when designing systems that make risky decisions on behalf of others? We present three papers that bear on key normative and technical aspects of these questions. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.01927 |
| By: | Daniel Huerta; Chris Mothorpe |
| Abstract: | The function of a REIT manager is to effectively and profitably manage a real estate portfolio. Among the ways to achieve this goal is to improve performance by managing market exposure through geographic diversification. Previous research recognizes the role of portfolio geographic diversification on efficiency and firm value. For example, Campbell, Petrova and Sirmans (2003) explain REIT property acquisitions produce wealth benefits when companies reconfirm their geographical focus suggesting firms benefit from more geographically concentrated portfolios. Similarly, Hartzell, Sun and Titman (2014) find that as REITs increase the geographical dispersion of their properties, firm value significantly decreases, also suggesting a REIT geographical diversification discount. More recently, Feng, Pattanapanchai, Price and Sirmans (2021) find a relationship between the level of firm transparency and the benefit of geographical diversification; that is, less transparent firms benefit from geographical concentration while more transparent firms benefit from diversification. Relatedly, Zhu and Lizieri (2022) suggest REITs with more geographically concentrated portfolios observe higher risk when the portfolio is exposed to more volatile property markets but if portfolios are geographically diversified, the effect of local market risk decreases. In sum, the evidence of whether geographical diversification improves or decreases REIT efficiency and value remains inconclusive. In this paper, we explore the impact of geographical diversification from an international perspective. We find that although most U.S. equity REITs tend to concentrate assets in the continental United States, a non-trivial portion of the U.S. REIT market tends to diversify internationally. Therefore, we raise the question of whether the strategy of international geographical diversification is value-enhancing. |
| Keywords: | REIT international diversification; Reit Performance; REIT Portfolio Management; REIT value |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-117 |