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on Risk Management |
| By: | Stulz, Rene M. (Ohio State U and ECGI) |
| Abstract: | Existing evidence shows convincingly that expected cash flows of non-financial firms can be negatively affected by their total risk, so that non-financial firms can create shareholder wealth by managing their total risk. After reviewing theories that demonstrate links between firm value and total risk, I examine how financial risk management is used to manage firm total risk. I conclude from the evidence that the use of financial risk management is mostly limited to near-term risk in non-financial firms. I offer explanations for this limited role of financial risk management. I argue that the limitations of financial risk management make it important for firms to also focus on resilience and call for more research on the costs and benefits of resilience. |
| JEL: | G23 G32 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:ohidic:2024-15 |
| By: | John Armstrong; James Dalby |
| Abstract: | We mathematically demonstrate how and what it means for two collective pension funds to mutually insure one another against systematic longevity risk. The key equation that facilitates the exchange of insurance is a market clearing condition. This enables an insurance market to be established even if the two funds face the same mortality risk, so long as they have different risk preferences. Provided the preferences of the two funds are not too dissimilar, insurance provides little benefit, implying the base scheme is effectively optimal. When preferences vary significantly, insurance can be beneficial. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07749 |
| By: | Pulikandala Nithish Kumar; Nneka Umeorah; Alex Alochukwu |
| Abstract: | Volatility forecasting is essential for risk management and decision-making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility clustering but often fail to model complex, non-linear interdependencies between multiple indices. This paper proposes a novel approach using Graph Neural Networks (GNNs) to represent global financial markets as dynamic graphs. The Temporal Graph Attention Network (Temporal GAT) combines Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) to capture the temporal and structural dynamics of volatility spillovers. By utilizing correlation-based and volatility spillover indices, the Temporal GAT constructs directed graphs that enhance the accuracy of volatility predictions. Empirical results from a 15-year study of eight major global indices show that the Temporal GAT outperforms traditional GARCH models and other machine learning methods, particularly in short- to mid-term forecasts. The sensitivity and scenario-based analysis over a range of parameters and hyperparameters further demonstrate the significance of the proposed technique. Hence, this work highlights the potential of GNNs in modeling complex market behaviors, providing valuable insights for financial analysts and investors. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.16858 |
| By: | Laabidi Khalid (UH2MC - Université Hassan II [Casablanca]); Mohamed EL Aallaoui (UH2MC - Université Hassan II [Casablanca]) |
| Abstract: | The quantification of credit portfolio losses using the wavelet approach offers an innovative methodology for assessing the financial risks associated with credit. This approach uses advanced mathematical techniques to analyse temporal fluctuations in credit data. In terms of quantifying losses, the wavelet approach allows the decomposition of loss time series into different time scales. This makes it possible to identify short-and long-term trends as well as irregular variations. By analysing these scales, analysts can better understand the dynamics of credit losses and identify the underlying factors that contribute to fluctuations. To quantify credit portfolio losses, the cumulative loss function is approximated by a finite combination of wavelet basis functions by computing the coefficients of the wavelet approximation (WA). Wavelet approximation is an accurate, robust and fast method that enables VaR to be estimated much more quickly than with other loss quantification methods, such as the Monte Carlo MC method. |
| Keywords: | Wavelet in finance, Portfolio management, Computational finance, The harmonic approach, Credit risk, Value at risk, Risk measures, Wavelet in finance Portfolio management Computational finance The harmonic approach Credit risk Value at risk Risk measures |
| Date: | 2024–08–11 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04713310 |
| By: | Bao, Jack (U of Delaware); Hou, Kewei (Ohio State U); Taoushianis, Zenon (U of Southampton) |
| Abstract: | We construct a measure of systemic risk, DRSFIN, that combines the high frequency information available in equity returns with a simple structural model of default. DRSFIN predicts future bank failures even after controlling for bank characteristics, macroeconomic conditions, uncertainty, and existing measures of aggregate systemic risk. We then show that DRSFIN is able to predict aggregate loan growth and nonfinancial firm failure, indicating that it not only predicts disruption in the financial sector, but also has real effects. Finally, we show that DRSFIN is also associated with elevated market uncertainty and stress in international markets. |
| JEL: | E44 E66 G01 G13 G21 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:ohidic:2024-16 |
| By: | Kola Akinsomi; Afees Salisu; Ametefe Frank; Hammed Yinka |
| Abstract: | We analyze the predictability of REITs returns based on gold market volatility for 11 sectors and five regions. Our findings show higher gains during volatile gold market conditions, but results vary in tranquil and turbulent periods. We observe sector-specific investment behaviour in the REITs market during the pre-GFC, but the post-GFC and COVID periods show otherwise. REITs offer a safe haven ability for gold, but their hedging power is sector-specific. For sensitivity analysis, stock market volatility is used in lieu of gold market volatility, and the outcome provides the expected counterfactual evidence with the REITs market. Our study has numerous policy implications for global financial market stakeholders. |
| Keywords: | and financial crisis; Gold; Real Estate Investment; Volatility |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-222 |
| By: | Luca Vincenzo Ballestra; Enzo D'Innocenzo; Christian Tezza |
| Abstract: | Christoffersen, Jacobs, Ornthanalai, and Wang (2008) (CJOW) proposed an improved Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for valuing European options, where the return volatility is comprised of two distinct components. Empirical studies indicate that the model developed by CJOW outperforms widely-used single-component GARCH models and provides a superior fit to options data than models that combine conditional heteroskedasticity with Poisson-normal jumps. However, a significant limitation of this model is that it allows the variance process to become negative. Oh and Park [2023] partially addressed this issue by developing a related model, yet the positivity of the volatility components is not guaranteed, both theoretically and empirically. In this paper we introduce a new GARCH model that improves upon the models by CJOW and Oh and Park [2023], ensuring the positivity of the return volatility. In comparison to the two earlier GARCH approaches, our novel methodology shows comparable in-sample performance on returns data and superior performance on S&P500 options data. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14513 |
| By: | Nathan Sauldubois; Nizar Touzi |
| Abstract: | We investigate model risk distributionally robust sensitivities for functionals on the Wasserstein space when the underlying model is constrained to the martingale class and/or is subject to constraints on the first marginal law. Our results extend the findings of Bartl, Drapeau, Obloj \& Wiesel \cite{bartl2021sensitivity} and Bartl \& Wiesel \cite{bartlsensitivityadapted} by introducing the minimization of the distributionally robust problem with respect to semi-static hedging strategies. We provide explicit characterizations of the model risk (first order) optimal semi-static hedging strategies. The distributional robustness is analyzed both in terms of the adapted Wasserstein metric and the more relevant standard Wasserstein metric. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.06906 |
| By: | Heidi Falkenbach; Islam Ibrahim |
| Abstract: | At heightened market volatility, investors migrate to safer investments prioritizing risk avoidance over gaining higher returns. Hence the value of safer, less volatile investment appreciates. As diversification diminishes volatility, we investigate whether diversified REITs is considered a safer investment at high market volatility. We find that the effect of diversification on the value and public debt cost of REITs varies with market volatility. At low level of market volatility geographical diversification has a negative net effect on the REIT value. However, as the market volatility increases, this negative impact diminishes. Similarly, we find that at low level of market volatility, geographical diversification has a yield-increasing effect on the debt issues of REITs. But, also, as market volatility increases the yield-increasing effect diminishes. These findings indicate that the value of diversification increases with market volatility. |
| Keywords: | Diversification; firm value; market volatility; REIT |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-083 |
| By: | Asuamah Yeboah, Samuel; Mogre, Diana; Nartey Menzo, Benjamin Prince |
| Abstract: | This review explores the critical impact of social and cultural factors on credit risk assessment and lending practices, especially in developing countries. By examining existing research, we delve into how cultural norms, social relationships, and community dynamics shape the evaluation of creditworthiness for small and medium-scale enterprises (SMEs). Our findings offer valuable insights for policymakers, financial institutions, and researchers aiming to enhance the accuracy and cultural sensitivity of credit risk management strategies for SMEs in these contexts. |
| Keywords: | Credit risk assessment, lending decisions, social factors, cultural dynamics, small and medium-scale enterprises (SMEs), developing countries, community relationships, cultural norms, creditworthiness evaluation, financial inclusion. |
| JEL: | D14 G21 O16 Z13 |
| Date: | 2024–07–13 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122363 |
| By: | Alain Coen; Aurelie Desfleurs |
| Abstract: | The aim of this study is to analyze the relative importance of geopolitical risk (GPR), as introduced by Caldara and Iacoviello (2022), on the dynamics of U.S. REITs returns. Using an extended conditional version of Merton (1973)’s capital asset pricing model, we highlight the role played by GPR and its two components, geopolitical acts (GPA) and geopolitical threats (GPT), on the expected returns of securitized real estate. Our robust results, report the level and the significancy of the geopolitical risk metrics on the decomposition of REITs returns grouped into different portfolios ( built from CRSP/Ziman series). We shed light on the link between the characteristics of REITs and the relative importance of geopolitical risk during the last decades. |
| Keywords: | Asset Pricing; Geopolitical Risks; Gmm; REITs |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-138 |
| By: | Couts, Spencer J. (U of Southern California); Goncalves, Andrei S. (Ohio State U); Liu, Yicheng (Ohio State U); Loudis, Johnathan (U of Notre Dame) |
| Abstract: | In this paper, we study the role of subjective risk premia in explaining subjective expected return time variation and disagreement using the long-term Capital Market Assumptions of major asset managers and investment consultants from 1987 to 2022. We find that market risk premia explain most of the expected return time variation, with the rest explained by alphas. The risk premia effect is almost entirely driven by time variation in risk quantities as opposed to risk price. Nevertheless, risk price explains about half of the transitory effect of risk premia on expected returns. Market risk premia also explain most of the expected return disagreement, but in this case alphas have a quantitatively significant effect, and risk price and risk quantities are roughly equally responsible for the risk premia effect. Our results provide benchmark moments that asset pricing models should match to be consistent with institutional investors' beliefs. |
| JEL: | G10 G11 G12 G23 G40 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:ohidic:2024-17 |
| By: | Lukas Petrasek (Charles University, Faculty of Social Sciences, Institute of Economic Studies, Prague, Czechia); Jiri Kukacka (Charles University, Faculty of Social Sciences, Institute of Economic Studies, Prague, Czechia & Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czechia) |
| Abstract: | We analyze the announcement risk premia on the US market between September 1987 and March 2023 and find that the market index exhibits average excess returns of 8.3 bps for macroeconomic announcement days. This strongly contrasts with 1.4 bps returns for non-announcement days. We further measure the individual stocks´ sensitivities to macroeconomic data announcements over various lookback periods and show that stocks in the high-sensitivity portfolios offer investors significantly higher returns than stocks in the low-sensitivity portfolios. The average returns on the difference portfolios amount to 18 bps per month for the 60-month sensitivities. The Fama-MacBeth regression coefficients for the announcement sensitivity are positive and statistically significant across all lookback periods. |
| Keywords: | Asset pricing, macroeconomic data announcements, risk premia |
| JEL: | C58 G12 G14 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:fau:wpaper:wp2024_38 |
| By: | Yannick Hoga |
| Abstract: | Forecasting risk (as measured by quantiles) and systemic risk (as measured by Adrian and Brunnermeiers's (2016) CoVaR) is important in economics and finance. However, past research has shown that predictive relationships may be unstable over time. Therefore, this paper develops structural break tests in predictive quantile and CoVaR regressions. These tests can detect changes in the forecasting power of covariates, and are based on the principle of self-normalization. We show that our tests are valid irrespective of whether the predictors are stationary or near-stationary, rendering the tests suitable for a range of practical applications. Simulations illustrate the good finite-sample properties of our tests. Two empirical applications concerning equity premium and systemic risk forecasting models show the usefulness of the tests. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.05861 |
| By: | Bauwens, Luc (Université catholique de Louvain, LIDAM/CORE, Belgium); Dzuverovic, Emilija (Universita di Pisa); Hafner, Christian (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | We introduce asymmetric effects in the BEKK-type conditional autoregressive Wishart model for realized covariance matrices. The asymmetry terms are specified either by interacting the lagged realized covariances with the signs of the lagged daily returns or by using the decomposition of the lagged realized covariance matrix into positive, negative, and mixed semi-covariances, thus relying on the lagged intra-daily returns and their signs. We provide a detailed comparison of models with different complexity, for example with respect to restrictions on the parameter matrices. In an extensive empirical study, our results suggest that the asymmetric models outperform the symmetric one in terms of statistical and economic criteria. The asymmetric models using the signs of the daily returns tend to have a better in-sample fit and out-of-sample predictive ability than the models using the signed intra-daily returns. |
| Keywords: | High frequency data ; asymmetric volatility ; realized covariance ; conditional autoregressive Wishart model |
| Date: | 2024–10–08 |
| URL: | https://d.repec.org/n?u=RePEc:cor:louvco:2024024 |
| By: | Anubha Goel; Puneet Pasricha; Juho Kanniainen |
| Abstract: | This study is the first to explore the application of a time-series foundation model for VaR estimation. Foundation models, pre-trained on vast and varied datasets, can be used in a zero-shot setting with relatively minimal data or further improved through finetuning. We compare the performance of Google's model, called TimesFM, against conventional parametric and non-parametric models, including GARCH, Generalized Autoregressive Score (GAS), and empirical quantile estimates, using daily returns from the S\&P 100 index and its constituents over 19 years. Our backtesting results indicate that, in terms of the actual-over-expected ratio, the fine-tuned TimesFM model consistently outperforms traditional methods. Regarding the quantile score loss function, it achieves performance comparable to the best econometric approach, the GAS model. Overall, the foundation model is either the best or among the top performers in forecasting VaR across the 0.01, 0.025, 0.05, and 0.1 VaR levels. We also found that fine-tuning significantly improves the results, and the model should not be used in zero-shot settings. Overall, foundation models can provide completely alternative approaches to traditional econometric methods, yet there are challenges to be tackled. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.11773 |
| By: | Francesco Audrino; Jessica Gentner; Simon Stalder |
| Abstract: | This paper presents an innovative method for measuring uncertainty via large language models (LLMs), which offer greater precision and contextual sensitivity than the conventional methods used to construct prominent uncertainty indices. By analysing newspaper texts with state-of-the-art LLMs, our approach captures nuances often missed by conventional methods. We develop indices for various types of uncertainty, including geopolitical risk, economic policy, monetary policy, and financial market uncertainty. Our findings show that shocks to these LLM-based indices exhibit stronger associations with macroeconomic variables, shifts in investor behaviour, and asset return variations than conventional indices, underscoring their potential for more accurately reflecting uncertainty. |
| Keywords: | Uncertainty measurement, Large language models, Economic policy, Geopolitical risk, Monetary policy, Financial markets |
| JEL: | C45 C55 E44 G12 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2024-12 |