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on Risk Management |
| By: | Qiqin Zhou |
| Abstract: | The measure of portfolio risk is an important input of the Markowitz framework. In this study, we explored various methods to obtain a robust covariance estimators that are less susceptible to financial data noise. We evaluated the performance of large-cap portfolio using various forms of Ledoit Shrinkage Covariance and Robust Gerber Covariance matrix during the period of 2012 to 2022. Out-of-sample performance indicates that robust covariance estimators can outperform the market capitalization-weighted benchmark portfolio, particularly during bull markets. The Gerber covariance with Mean-Absolute-Deviation (MAD) emerged as the top performer. However, robust estimators do not manage tail risk well under extreme market conditions, for example, Covid-19 period. When we aim to control for tail risk, we should add constraint on Conditional Value-at-Risk (CVaR) to make more conservative decision on risk exposure. Additionally, we incorporated unsupervised clustering algorithm K-means to the optimization algorithm (i.e. Nested Clustering Optimization, NCO). It not only helps mitigate numerical instability of the optimization algorithm, but also contributes to lower drawdown as well. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00610 |
| By: | Alessandra Amendola; Vincenzo Candila; Antonio Naimoli; Giuseppe Storti |
| Abstract: | In order to meet the increasingly stringent global standards of banking management and regulation, several methods have been proposed in the literature for forecasting tail risk measures such as the Value-at-Risk (VaR) and Expected Shortfall (ES). However, regardless of the approach used, there are several sources of uncertainty, including model specifications, data-related issues and the estimation procedure, which can significantly affect the accuracy of VaR and ES measures. Aiming to mitigate the influence of these sources of uncertainty and improve the predictive performance of individual models, we propose novel forecast combination strategies based on the Model Confidence Set (MCS). In particular, consistent joint VaR and ES loss functions within the MCS framework are used to adaptively combine forecasts generated by a wide range of parametric, semi-parametric, and non-parametric models. Our results reveal that the proposed combined predictors provide a suitable alternative for forecasting risk measures, passing the usual backtests, entering the set of superior models of the MCS, and usually exhibiting lower standard deviations than other model specifications. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.06235 |
| By: | Walter Farkas (University of Zurich - Department Finance; Swiss Finance Institute; ETH Zürich); Patrick Lucescu (University of Zurich - Department of Finance) |
| Abstract: | This paper develops a simplified agent-based model to investigate the dynamics of risk transfer and its implications for systemic risk within financial networks, focusing specifically on Credit Default Swaps (CDSs) as instruments of risk allocation among banks and firms. Unlike broader models that incorporate multiple types of economic agents, our approach explicitly targets the interactions between banks and firms across three markets: credit, interbank loans, and CDSs. This model diverges from the frameworks established by Leduc, Poledna, and Thurner (2016) and Poledna and Thurner (2016) by simplifying the agent structure, which allows for more focused calibration to empirical data—specifically, a sample of Swiss banks—and enhances interpretability for regulatory use. Our analysis centers around two control variables, CDSc and CDSn, which modulate the likelihood of institutions participating in covered and naked CDS transactions, respectively. This approach allows us to explore the network’s behavior under varying levels of interconnectedness and differing magnitudes of deposit shocks. Our results indicate that the network can withstand minor shocks, but higher levels of CDS engagement significantly increase variance and kurtosis in equity returns, signaling heightened instability. This effect is amplified during severe shocks, suggesting that CDSs, instead of mitigating risk, propagate systemic risk, particularly in highly interconnected networks. These findings underscore the need for regulatory oversight to manage risk concentration and ensure financial stability. |
| Keywords: | Systemic Risk, Agent-Based Modeling, Financial Networks, Risk Transfer, Network Interconnectedness, Credit Default Swaps |
| JEL: | C63 D85 G01 G21 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2432 |
| By: | Mr. Jorge A Chan-Lau; Ruofei Hu; Luca Mungo; Ritong Qu; Weining Xin; Cheng Zhong |
| Abstract: | We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time. The model uses data from publicly-traded companies to construct a probability of default (PD) function. This function integrates high-frequency, market-based, aggregate distress signals with low-frequency, firm-level financial ratios, and macroeconomic indicators. When provided with private firms' financial ratios, the model, which we name signal-knowledge transfer learning model (SKTL), transfers insights gained from 35 thousand publicly-traded firms to more than 4 million private-held ones and performs well as an ordinal measure of privately-held firms' default risk. |
| Keywords: | Default risk; Corporate sector; Privately-held firm; Gradient boosting; Transfer learning |
| Date: | 2024–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/115 |
| By: | Guido Gazzani; Julien Guyon |
| Abstract: | We consider the path-dependent volatility (PDV) model of Guyon and Lekeufack (2023), where the instantaneous volatility is a linear combination of a weighted sum of past returns and the square root of a weighted sum of past squared returns. We discuss the influence of an additional parameter that unlocks enough volatility on the upside to reproduce the implied volatility smiles of S&P 500 and VIX options. This PDV model, motivated by empirical studies, comes with computational challenges, especially in relation to VIX options pricing and calibration. We propose an accurate neural network approximation of the VIX which leverages on the Markovianity of the 4-factor version of the model. The VIX is learned as a function of the Markovian factors and the model parameters. We use this approximation to tackle the joint calibration of S&P 500 and VIX options. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.02319 |
| By: | Jan Muckenhaupt (Technische Universität München (TUM)); Martin Hoesli (University of Geneva - Geneva School of Economics and Management (GSEM); Swiss Finance Institute; University of Aberdeen - Business School); Bing Zhu (Technische Universität München (TUM)) |
| Abstract: | Assets’ capability to hedge against inflation has again come to the forefront given the recent surge in inflation. This paper investigates the inflation-hedging capability of an important asset class, i.e., listed real estate (LRE), using data from 1990 to the end of 2023, for the main European countries in terms of LRE market capitalization, but also the U.S. By using a Panel Markov switching vector error correction model (MS-VECM), we identify the hedging ability of LRE in crisis and non-crisis periods, both in the short and long term. We additionally compare the hedging ability of LRE with that of other asset classes. Listed real estate provides an effective hedge against inflation in the long run, both in crisis and non-crisis periods. In the short term, listed real estate only hedges against inflation in stable periods. LRE effectively serves as a hedge against inflation shocks, particularly protecting against unexpected inflation from the first month and against energy inflation during stable periods. While stocks surpass LRE in long-term inflation protection and LRE has short-term benefits, gold distinguishes itself from LRE by offering reliable long-run protection, but only in economic downturns. The results should provide important insights to investors seeking to allocate resources more efficiently in those turbulent times, both for the short and long terms. |
| Keywords: | Inflation Hedging, Listed Real Estate Companies, Markov-Switching, Unexpected Inflation, Impulse Response Functions |
| JEL: | G11 G15 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2434 |
| By: | Stark, Oded (University of Bonn); Wlodarczyk, Julia (University of Economics in Katowice) |
| Abstract: | A perception at the core of studies that consider the link between social rank and stress (typically measured by the so-called stress hormone cortisol) is that the link is direct. Examples of such studies are Bartolomucci (2007), Beery and Kaufer (2015), and Koolhaas et al. (2017). A recent and stark representation of this body of work is a study by Smith-Osborne et al. (2023), who state that "social hierarchies directly influence stress status" (Smith-Osborne et al. p. 1537, italics added). In the present paper, we reflect on this "direct" perspective. We conjecture that the link between social rank and stress involves an intervening variable: an indirect relationship arises when the loss of rank triggers a behavioral response in the form of risk taking aimed at regaining rank, and it is the engagement in risk-taking behavior that is the cause of an elevated level of cortisol. Smith-Osborne et al., as well as others whose papers are cited by Smith-Osborne et al. and who, like Creel (2001) and Avitsur et al. (2006), conducted comprehensive research on the association between rank (social standing) and stress, do not refer to risk taking at all. We present four strands of research that lend support to our conjecture: evidence that in response to losing rank, individuals are stressed; evidence that in response to losing rank, individuals resort to risk-taking behavior aimed at regaining their lost rank; evidence that there exists a link between engagement in risky activities or exposure to risk and elevated levels of cortisol; and an analytical perspective on incidence and intensity, namely a perspective that shows how the willingness to take risks responds to a change in rank, specifically, how a loss of rank triggers a greater willingness to take risks and how this trigger is stronger for individuals whose rank is higher. |
| Keywords: | social rank, level of stress, hormonal response, risk-taking behavior, elevated level of cortisol |
| JEL: | D01 D31 D81 D87 D91 I12 I14 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17044 |
| By: | Ortega, Francesc (Queens College, CUNY); Petkov, Ivan (Northeastern University) |
| Abstract: | We present a theory of the demand for flood insurance and empirically analyze the effects of the adoption of Risk Rating 2.0, using individual insurance histories for all NFIP policies. The reform increased exit and reduced entry, both in the flood zone and its periphery. The reform had highly heterogeneous effects on insurance costs and triggered adjustments in coverage and deductibles. On average, RR2 increased costs for renewers outside of the flood zone but lowered them for renewers in the flood zone, resulting in an overall average increase. However, the reform reduced revenue and increased financial exposure to flood risk. |
| Keywords: | flood risk, insurance, Risk Rating 2.0, FEMA, NFIP |
| JEL: | R11 R30 Q54 G22 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17021 |
| By: | Maximilian Schr\"oder |
| Abstract: | Assessing the contribution of various risk factors to future inflation risks was crucial for guiding monetary policy during the recent high inflation period. However, existing methodologies often provide limited insights by focusing solely on specific percentiles of the forecast distribution. In contrast, this paper introduces a comprehensive framework that examines how economic indicators impact the entire forecast distribution of macroeconomic variables, facilitating the decomposition of the overall risk outlook into its underlying drivers. Additionally, the framework allows for the construction of risk measures that align with central bank preferences, serving as valuable summary statistics. Applied to the recent inflation surge, the framework reveals that U.S. inflation risk was primarily influenced by the recovery of the U.S. business cycle and surging commodity prices, partially mitigated by adjustments in monetary policy and credit spreads. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.17237 |
| By: | Seulki Chung |
| Abstract: | This paper presents a comparative analysis of univariate and multivariate GARCH-family models and machine learning algorithms in modeling and forecasting the volatility of major energy commodities: crude oil, gasoline, heating oil, and natural gas. It uses a comprehensive dataset incorporating financial, macroeconomic, and environmental variables to assess predictive performance and discusses volatility persistence and transmission across these commodities. Aspects of volatility persistence and transmission, traditionally examined by GARCH-class models, are jointly explored using the SHAP (Shapley Additive exPlanations) method. The findings reveal that machine learning models demonstrate superior out-of-sample forecasting performance compared to traditional GARCH models. Machine learning models tend to underpredict, while GARCH models tend to overpredict energy market volatility, suggesting a hybrid use of both types of models. There is volatility transmission from crude oil to the gasoline and heating oil markets. The volatility transmission in the natural gas market is less prevalent. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.19849 |
| By: | Mauricio Elizalde; Stephan Sturm |
| Abstract: | We aim to provide an intertemporal, cost-efficient consumption model that extends the consumption optimization inspired by the Distribution Builder, a tool developed by Sharpe, Johnson, and Goldstein. The Distribution Builder enables the recovery of investors' risk preferences by allowing them to select a desired distribution of terminal wealth within their budget constraints. This approach differs from the classical portfolio optimization, which considers the agent's risk aversion modeled by utility functions that are challenging to measure in practice. Our intertemporal model captures the dependent structure between consumption periods using copulas. This strategy is demonstrated using both the Black-Scholes and CEV models. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.16336 |
| By: | Niushan Gao; Foivos Xanthos |
| Abstract: | In this short note, we show that every convex, order bounded above functional on a Banach lattice is automatically norm continuous. This improves a result in \cite{RS06} and applies to many deviation and variability measures. We also show that an order-continuous, law-invariant functional on an Orlicz space is strongly consistent everywhere, extending a result in \cite{KSZ14}. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.09766 |
| By: | Massimo Motta; Volker Nocke; Martin Peitz |
| Abstract: | With the increased risks of international trade frictions and geopolitical disruptions merger control that does not account for such risks may be too lenient. This article provides a proposal on how competition authorities should systematically assess mergers based on a risk assessment and how they should adjust their market share and UPP analysis. The authors also argue that the approach fits well into recent developments of merger analyses in the European Union. |
| Keywords: | merger control, market shares, UPP, resilience, geopolitical risks |
| JEL: | K21 L40 L13 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2024_568 |
| By: | Valérie Bertrand (UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University)); Virginie Cartier (UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University)) |
| Abstract: | This paper presents the results of collaborative research, part of the PrIORRA projet, on employee vulnerability with a business association and a co-op. Using interviews, we led with the employees, the social representations of vulnerability could be formalized. The co-construction workshops, based on risk management using the NF ISO 31 000: 2018 standard, highlighted the importance of a methodical approach to risk management and managerial support for employees, in order to rewrite the company's strategic plan. Thus, taking vulnerability into account generates management innovation and can strengthen the organization. |
| Abstract: | La communication présente les fruits d'une recherche-action coopérative menée, dans le cadre du projet PrIORRA, auprès d'une association et d'une coopérative d'entreprises sur le thème de la vulnérabilité des salariés. A travers l'exploitation des entretiens réalisés, les représentations sociales de la vulnérabilité ont pu être formalisées. Les ateliers de co-construction, en s'appuyant sur le management des risques, ont permis de mettre en évidence l'importance d'une approche méthodique du management du risque en utilisant la norme NF ISO 31 000 : 2018, de l'accompagnement managérial des salariés pour plus largement réécrire le projet stratégique de l'entreprise. Ainsi, la prise en compte de la vulnérabilité devient génératrice d'innovation managériale et peut agir comme un levier pour renforcer l'organisation. |
| Keywords: | Innovation, managerial practices, social representation, collaborative action research, vulnerability, risk management, pratiques managériale, représentations sociales, recherche-action coopérative, vulnérabilité, management du risque |
| Date: | 2023–10–17 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04578251 |
| By: | Daniele Maria Di Nosse; Federico Gatta |
| Abstract: | Decentralized Exchanges are becoming even more predominant in today's finance. Driven by the need to study this phenomenon from an academic perspective, the SIAG/FME Code Quest 2023 was announced. Specifically, participating teams were asked to implement, in Python, the basic functions of an Automated Market Maker and a liquidity provision strategy in an Automated Market Maker to minimize the Conditional Value at Risk, a critical measure of investment risk. As the competition's winning team, we highlight our approach in this work. In particular, as the dependence of the final return on the initial wealth distribution is highly non-linear, we cannot use standard ad-hoc approaches. Additionally, classical minimization techniques would require a significant computational load due to the cost of the target function. For these reasons, we propose a three-step approach. In the first step, the target function is approximated by a Kernel Ridge Regression. Then, the approximating function is minimized. In the final step, the previously discovered minimum is utilized as the starting point for directly optimizing the desired target function. By using this procedure, we can both reduce the computational complexity and increase the accuracy of the solution. Finally, the overall computational load is further reduced thanks to an algorithmic trick concerning the returns simulation and the usage of Cython. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.07200 |
| By: | H. Peter Boswijk; Jun Yu (Faculty of Business Administration, University of Macau); Yang Zu |
| Abstract: | Based on a continuous-time stochastic volatility model with a linear drift, we develop a test for explosive behavior in financial asset prices at a low frequency when prices are sampled at a higher frequency. The test exploits the volatility information in the high-frequency data. The method consists of devolatizing log-asset price increments with realized volatility measures and performing a supremum-type recursive Dickey-Fuller test on the devolatized sample. The proposed test has a nuisance-parameter-free asymptotic distribution and is easy to implement. We study the size and power properties of the test in Monte Carlo simulations. A real-time date-stamping strategy based on the devolatized sample is proposed for the origination and conclusion dates of the explosive regime. Conditions under which the real-time date-stamping strategy is consistent are established. The test and the date-stamping strategy are applied to study explosive behavior in cryptocurrency and stock markets. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:2024 |
| By: | Francesco Audrino; Jonathan Chassot |
| Abstract: | We investigate the predictive abilities of the heterogeneous autoregressive (HAR) model compared to machine learning (ML) techniques across an unprecedented dataset of 1, 455 stocks. Our analysis focuses on the role of fitting schemes, particularly the training window and re-estimation frequency, in determining the HAR model's performance. Despite extensive hyperparameter tuning, ML models fail to surpass the linear benchmark set by HAR when utilizing a refined fitting approach for the latter. Moreover, the simplicity of HAR allows for an interpretable model with drastically lower computational costs. We assess performance using QLIKE, MSE, and realized utility metrics, finding that HAR consistently outperforms its ML counterparts when both rely solely on realized volatility and VIX as predictors. Our results underscore the importance of a correctly specified fitting scheme. They suggest that properly fitted HAR models provide superior forecasting accuracy, establishing robust guidelines for their practical application and use as a benchmark. This study not only reaffirms the efficacy of the HAR model but also provides a critical perspective on the practical limitations of ML approaches in realized volatility forecasting. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.08041 |
| By: | Luiz de Mello; Teresa Ter-Minassian |
| Abstract: | Subnational governments face a range of fiscal risks, defined as events whose realisation leads to significant deviations of revenue and/or expenditure from budgeted amounts. Fiscal risks reflect unforeseen macroeconomic developments, as well as structural shifts in the economy, including digitalisation and climate change. Sound management of these risks requires a comprehensive framework involving their identification, analysis, mitigation, sharing or transfer, and prudent accommodation. Within this framework, subnational governments need to strengthen their capacity to manage their own risks, but national governments also have a role to play. This includes mitigating risks created by national policies, minimising moral hazard in supporting subnational governments affected by exogenous shocks, and using their legislative powers to avert excessive subnational risk-taking. Effective intergovernmental cooperation is key to the sound management of subnational fiscal risks. The paper discusses how different levels of government can work together in applying this framework to the main types of risks. It also provides some examples of good international practices in the management of risks. |
| Keywords: | fiscal risks, fiscal sustainability, intergovernmental relations, risk management, subnational governments |
| JEL: | H12 H70 H77 |
| Date: | 2024–06–13 |
| URL: | https://d.repec.org/n?u=RePEc:oec:ctpaab:46-en |