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on Risk Management |
| By: | Qiqi Li; Wei Wang; Yiying Zhang |
| Abstract: | In economic analysis, rational decision-makers often take actions to reduce their risk exposure. These actions include purchasing market insurance and implementing prevention measures to modify the shape of the loss distribution. Under the assumption that the insureds' actions are fully observed by the insurer, this paper investigates the interaction between self-protection and insurance demand when insurance premiums are determined by convex premium principles within the framework of distortion risk measures. Specifically, the insured selects an optimal proportional insurance share and prevention effort to minimize the risk measure of their end-of-period exposure. We explicitly characterize the optimal combination of prevention effort and insurance demand in a self-protection model when the insured adopts tail value-at-risk and strictly convex distortion risk measures, respectively. Additionally, we conduct comparative static analyses to illustrate our main findings under various premium structures, risk aversion levels, and loss distributions. Our results indicate that market insurance and self-protection are complementary, supporting classical insights from the literature regarding corner insurance policies (i.e., null and full insurance) in the absence of ex ante moral hazard. Finally, we consider the effects of moral hazard on the interaction between self-protection and insurance demand. Our findings show that ex ante moral hazard shifts the complementary effect into substitution effect. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19436 |
| By: | Ling Chen |
| Abstract: | This paper investigates the application of Feature-Enriched Generative Adversarial Networks (FE-GAN) in financial risk management, with a focus on improving the estimation of Value at Risk (VaR) and Expected Shortfall (ES). FE-GAN enhances existing GANs architectures by incorporating an additional input sequence derived from preceding data to improve model performance. Two specialized GANs models, the Wasserstein Generative Adversarial Network (WGAN) and the Tail Generative Adversarial Network (Tail-GAN), were evaluated under the FE-GAN framework. The results demonstrate that FE-GAN significantly outperforms traditional architectures in both VaR and ES estimation. Tail-GAN, leveraging its task-specific loss function, consistently outperforms WGAN in ES estimation, while both models exhibit similar performance in VaR estimation. Despite these promising results, the study acknowledges limitations, including reliance on highly correlated temporal data and restricted applicability to other domains. Future research directions include exploring alternative input generation methods, dynamic forecasting models, and advanced neural network architectures to further enhance GANs-based financial risk estimation. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.15519 |
| By: | Jiahao Zhu; Hengzhi Wu |
| Abstract: | This study explores the use of Transformer-based models to predict both covariance and semi-covariance matrices for ETF portfolio optimization. Traditional portfolio optimization techniques often rely on static covariance estimates or impose strict model assumptions, which may fail to capture the dynamic and non-linear nature of market fluctuations. Our approach leverages the power of Transformer models to generate adaptive, real-time predictions of asset covariances, with a focus on the semi-covariance matrix to account for downside risk. The semi-covariance matrix emphasizes negative correlations between assets, offering a more nuanced approach to risk management compared to traditional methods that treat all volatility equally. Through a series of experiments, we demonstrate that Transformer-based predictions of both covariance and semi-covariance significantly enhance portfolio performance. Our results show that portfolios optimized using the semi-covariance matrix outperform those optimized with the standard covariance matrix, particularly in volatile market conditions. Moreover, the use of the Sortino ratio, a risk-adjusted performance metric that focuses on downside risk, further validates the effectiveness of our approach in managing risk while maximizing returns. These findings have important implications for asset managers and investors, offering a dynamic, data-driven framework for portfolio construction that adapts more effectively to shifting market conditions. By integrating Transformer-based models with the semi-covariance matrix for improved risk management, this research contributes to the growing field of machine learning in finance and provides valuable insights for optimizing ETF portfolios. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19649 |
| By: | H\'el\`ene Cossette; Benjamin C\^ot\'e; Alexandre Dubeau; Etienne Marceau |
| Abstract: | We propose risk models for a portfolio of risks, each following a compound Poisson distribution, with dependencies introduced through a family of tree-based Markov random fields with Poisson marginal distributions inspired in C\^ot\'e et al. (2024b, arXiv:2408.13649). The diversity of tree topologies allows for the construction of risk models under several dependence schemes. We study the distribution of the random vector of risks and of the aggregate claim amount of the portfolio. We perform two risk management tasks: the assessment of the global risk of the portfolio and its allocation to each component. Numerical examples illustrate the findings and the efficiency of the computation methods developed throughout. We also show that the discussed family of Markov random fields is a subfamily of the multivariate Poisson distribution constructed through common shocks. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.00607 |
| By: | Alessandro Bondi; Sergio Pulido (ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise, LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Simone Scotti |
| Abstract: | We study an extension of the Heston stochastic volatility model that incorporates rough volatility and jump clustering phenomena. In our model, named the rough Hawkes Heston stochastic volatility model, the spot variance is a rough Hawkes-type process proportional to the intensity process of the jump component appearing in the dynamics of the spot variance itself and the log returns. The model belongs to the class of affine Volterra models. In particular, the Fourier-Laplace transform of the log returns and the square of the volatility index can be computed explicitly in terms of solutions of deterministic Riccati-Volterra equations, which can be efficiently approximated using a multi-factor approximation technique. We calibrate a parsimonious specification of our model characterized by a power kernel and an exponential law for the jumps. We show that our parsimonious setup is able to simultaneously capture, with a high precision, the behavior of the implied volatility smile for both S&P 500 and VIX options. In particular, we observe that in our setting the usual shift in the implied volatility of VIX options is explained by a very low value of the power in the kernel. Our findings demonstrate the relevance, under an affine framework, of rough volatility and self-exciting jumps in order to capture the joint evolution of the S&P 500 and VIX. |
| Keywords: | Stochastic volatility, Rough volatility, Hawkes processes, Jump clusters, Leverage effect, affine Volterra processes, VIX, joint calibration of S&, P 500 and VIX smiles |
| Date: | 2024–03–02 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-03827332 |
| By: | Mario Ghossoub; Qinghua Ren; Ruodu Wang |
| Abstract: | We study risk sharing among agents with preferences modeled by heterogeneous distortion risk measures, who are not necessarily risk averse. Pareto optimality for agents using risk measures is often studied through the lens of inf-convolutions, because allocations that attain the inf-convolution are Pareto optimal, and the converse holds true under translation invariance. Our main focus is on groups of agents who exhibit varying levels of risk seeking. Under mild assumptions, we derive explicit solutions for the unconstrained inf-convolution and the counter-monotonic inf-convolution, which can be represented by a generalization of distortion risk measures. Furthermore, for a group of agents with different levels of risk aversion or risk seeking, we consider a portfolio manager's problem and explicitly determine the optimal investment strategies. Interestingly, we observe a counterintuitive phenomenon of comparative statics: even if all agents in the group become more risk seeking, the portfolio manager acting on behalf of the group may not necessarily allocate a larger proportion of investments to risky assets, which is in sharp contrast to the case of risk-averse agents. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.00655 |
| By: | Peter Albrecht; Evžen Kočenda; Evžen Kocenda |
| Abstract: | Using novel methods, we comprehensively analyze volatility connectedness among most traded currencies using high-frequency data from 2009 to 2023. Our study presents the first empirical evidence of a statistically significant association between increases in connectedness and endogenously selected impactful events for most traded currencies. Moreover, we uncover the previously unexplored relationship between twenty-three events affecting global forex connectedness up to one business month ahead and further analyze pre-event connectedness changes. We also distinguish between the transitory and permanent impacts of events on connectedness and confirm the association of four events with a permanent shift in connectedness; two events are associated with the EU and US debt crises. We compute the portfolio weights and hedge ratios for portfolio optimization and uncover the Swiss franc and Japanese yen as the most suitable tools for managing currency risk. The effects of intra-day currency depreciation versus appreciation against the U.S. dollar differ significantly, but the extent of asymmetries declines over time. |
| Keywords: | volatility connectedness, global currencies, bootstrap-after-bootstrap procedure, transitory and permanent effects, debt crisis, portfolio composition and hedging, uncertainty |
| JEL: | C58 F31 F65 G01 G15 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11606 |
| By: | Bo Yuan; Damiano Brigo; Antoine Jacquier; Nicola Pede |
| Abstract: | Deep learning methods have become a widespread toolbox for pricing and calibration of financial models. While they often provide new directions and research results, their `black box' nature also results in a lack of interpretability. We provide a detailed interpretability analysis of these methods in the context of rough volatility - a new class of volatility models for Equity and FX markets. Our work sheds light on the neural network learned inverse map between the rough volatility model parameters, seen as mathematical model inputs and network outputs, and the resulting implied volatility across strikes and maturities, seen as mathematical model outputs and network inputs. This contributes to building a solid framework for a safer use of neural networks in this context and in quantitative finance more generally. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19317 |
| By: | Fengler, Matthias; Koeniger, Winfried; Minger, Stephan |
| Abstract: | We analyze the transmission of monetary policy to the costs of hedging using options order book data. Monetary policy transmits to hedging costs both by changing the relevant state variables, such as the value of the underlying, its volatility and tail risk, and by affecting option market liquidity, including the bid-ask spread and market depth. Our estimates suggest that during the peak of the pandemic crisis in March 2020, monetary policy decisions resulted in substantial changes in hedging costs even within short intraday time windows around the decisions, amounting approximately to the annual expenses of a typical equity mutual fund. |
| Keywords: | Liquidity, Monetary policy, Option order books, Option markets, COVID-19 pandemic |
| JEL: | G13 G14 D52 E52 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cfswop:308803 |
| By: | Kun Liu; Jin Zhao |
| Abstract: | In the field of finance, the prediction of individual credit default is of vital importance. However, existing methods face problems such as insufficient interpretability and transparency as well as limited performance when dealing with high-dimensional and nonlinear data. To address these issues, this paper introduces a method based on Kolmogorov-Arnold Networks (KANs). KANs is a new type of neural network architecture with learnable activation functions and no linear weights, which has potential advantages in handling complex multi-dimensional data. Specifically, this paper applies KANs to the field of individual credit risk prediction for the first time and constructs the Kolmogorov-Arnold Credit Default Predict (KACDP) model. Experiments show that the KACDP model outperforms mainstream credit default prediction models in performance metrics (ROC_AUC and F1 values). Meanwhile, through methods such as feature attribution scores and visualization of the model structure, the model's decision-making process and the importance of different features are clearly demonstrated, providing transparent and interpretable decision-making basis for financial institutions and meeting the industry's strict requirements for model interpretability. In conclusion, the KACDP model constructed in this paper exhibits excellent predictive performance and satisfactory interpretability in individual credit risk prediction, providing an effective way to address the limitations of existing methods and offering a new and practical credit risk prediction tool for financial institutions. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.17783 |
| By: | Arnone, Massimo; Costantiello, Alberto; Leogrande, Angelo |
| Abstract: | The paper deals only with the identification of the determinants of total risk exposure amount within the European banking system, while the importance of TREA within Basel III regulatory regimes is focused. The research provides the integration of an econometric investigation with high-end machine learning techniques for the identification of the influential financial variables of TREA. The most relevant financial determinants of TREA were identified as LCR, CRWEA, LA, and OREA. These also reflect complex interdependencies-for instance, the negative value of TREA and LCR would suggest that there were trade-offs made between risk-taking and liquidity management. Thus, the positive relationship with CRWEA, and even more so with derivatives over assets, underlines intrinsic risks from credit exposures and related to financial instruments' complexity. The report further iterates that there should be mechanisms for appropriate risk-weighting, adequate liquidity buffers, and proper operational controls so that the financial system can become significantly more stable and resilient. This work will put forward actionable recommendations to policy makers, regulators, and financial institutions on mitigating systemic vulnerabilities and further optimizing their strategies for compliance in view of an increasingly volatile financial landscape, leveraging from traditional econometric modeling insights with machine learning. |
| Date: | 2025–01–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:2u4jb |
| By: | Chaitanya Joshi; Jinming Yang; Sergeja Slapnicar; Ryan K L Ko |
| Abstract: | Protecting against cyber-threats is vital for every organization and can be done by investing in cybersecurity controls and purchasing cyber insurance. However, these are interlinked since insurance premiums could be reduced by investing more in cybersecurity controls. The expected utility theory and the prospect theory are two alternative theories explaining decision-making under risk and uncertainty, which can inform strategies for optimizing resource allocation. While the former is considered a rational approach, research has shown that most people make decisions consistent with the latter, including on insurance uptakes. We compare and contrast these two approaches to provide important insights into how the two approaches could lead to different optimal allocations resulting in differing risk exposure as well as financial costs. We introduce the concept of a risk curve and show that identifying the nature of the risk curve is a key step in deriving the optimal resource allocation. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.18838 |
| By: | Qianli Zhao; Chao Wang; Richard Gerlach; Giuseppe Storti; Lingxiang Zhang |
| Abstract: | Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. In this thesis, aiming to synthesise the impact of various realised volatility measures on volatility forecasting, we propose an extension of the Realised GARCH model that incorporates an autoencoder-generated synthetic realised measure, combining the information from multiple realised measures in a nonlinear manner. Our proposed model extends existing linear methods, such as Principal Component Analysis and Independent Component Analysis, to reduce the dimensionality of realised measures. The empirical evaluation, conducted across four major stock markets from January 2000 to June 2022 and including the period of COVID-19, demonstrates both the feasibility of applying an autoencoder to synthesise volatility measures and the superior effectiveness of the proposed model in one-step-ahead rolling volatility forecasting. The model exhibits enhanced flexibility in parameter estimations across each rolling window, outperforming traditional linear approaches. These findings indicate that nonlinear dimension reduction offers further adaptability and flexibility in improving the synthetic realised measure, with promising implications for future volatility forecasting applications. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.17136 |
| By: | Mr. Augusto Azael Pérez Azcárraga; José M García-Sanjinés; Rossana A San Juan; Selvin A Lemus; Philip R Wood; Mr. Robert Kokoli |
| Abstract: | This technical note offers practical guidance to senior managers and technical staff in Customs administrations for developing a Compliance Improvement Plan (CIP) using an Integrated Risk Management (IRM) approach. It clearly outlines the components of a CIP based on IRM, explains what it entails for a Customs administration, and how to develop it step-by-step. Additionally, it underscores the importance of identifying and implementing tailored treatment measures for various trader segments, which is crucial for enhancing compliance levels. Furthermore, it emphasizes the need to identify key vulnerabilities within processes that may lead to the realization of risks, proposing appropriate strategies to address them. The note also highlights other critical factors that must be considered to ensure the effective implementation of a CIP. |
| Keywords: | Customs Administrations; risk management; integrated risk management; transactional risk management; customs compliance; compliance improvement plan; trade facilitation; segmentation; treatment measures; compliance indicators; compliance risks; institutional risk; governance; core customs processes; global risk index |
| Date: | 2025–01–24 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imftnm:2025/002 |
| By: | Stella C. Dong; James R. Finlay |
| Abstract: | Reinsurance optimization is critical for insurers to manage risk exposure, ensure financial stability, and maintain solvency. Traditional approaches often struggle with dynamic claim distributions, high-dimensional constraints, and evolving market conditions. This paper introduces a novel hybrid framework that integrates {Generative Models}, specifically Variational Autoencoders (VAEs), with {Reinforcement Learning (RL)} using Proximal Policy Optimization (PPO). The framework enables dynamic and scalable optimization of reinsurance strategies by combining the generative modeling of complex claim distributions with the adaptive decision-making capabilities of reinforcement learning. The VAE component generates synthetic claims, including rare and catastrophic events, addressing data scarcity and variability, while the PPO algorithm dynamically adjusts reinsurance parameters to maximize surplus and minimize ruin probability. The framework's performance is validated through extensive experiments, including out-of-sample testing, stress-testing scenarios (e.g., pandemic impacts, catastrophic events), and scalability analysis across portfolio sizes. Results demonstrate its superior adaptability, scalability, and robustness compared to traditional optimization techniques, achieving higher final surpluses and computational efficiency. Key contributions include the development of a hybrid approach for high-dimensional optimization, dynamic reinsurance parameterization, and validation against stochastic claim distributions. The proposed framework offers a transformative solution for modern reinsurance challenges, with potential applications in multi-line insurance operations, catastrophe modeling, and risk-sharing strategy design. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.06404 |
| By: | Jialun Cao; David \v{S}i\v{s}ka |
| Abstract: | We address the liquidation problem arising from the credit risk management in decentralised finance (DeFi) by formulating it as an ergodic optimal control problem. In decentralised derivatives exchanges, liquidation is triggered whenever the parties fail to maintain sufficient collateral for their open positions. Consequently, effectively managing and liquidating disposal of positions accrued through liquidations is a critical concern for decentralised derivatives exchanges. By simplifying the model (linear temporary and permanent price impacts, simplified cash balance dynamics), we derive the closed-form solutions for the optimal liquidation strategies, which balance immediate executions with the temporary and permanent price impacts, and the optimal long-term average reward. Numerical simulations further highlight the effectiveness of the proposed optimal strategy and demonstrate that the simplified model closely approximates the original market environment. Finally, we provide the method for calibrating the parameters in the model from the available data. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19637 |
| By: | Abraham Atsiwo; Andrey Sarantsev |
| Abstract: | The Capital Asset Pricing Model (CAPM) relates a well-diversified stock portfolio to a benchmark portfolio. We insert size effect in CAPM, capturing the observation that small stocks have higher risk and return than large stocks, on average. Dividing stock index returns by the Volatility Index makes them independent and normal. In this article, we combine these ideas to create a new discrete-time model, which includes volatility, relative size, and CAPM. We fit this model using real-world data, prove the long-term stability, and connect this research to Stochastic Portfolio Theory. We fill important gaps in our previous article on CAPM with the size factor. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19444 |
| By: | Antony Millner |
| Abstract: | This paper investigates a duality between ambiguity averse preferences and the valuation of long run risky assets or public projects. The variational ambiguity model represents preferences over ambiguous acts via a minimization problem, and is fundamentally nonprobabilistic. In contrast, long run risky assets are ranked via a large maturity limit of expected discounted returns. Despite their apparent differences, we show that each variational ambiguity preference is a long run risk preference, and (under natural conditions) vice versa. We explore three implications: a notion of long run stochastic dominance that resolves differences between stochastic processes considered identical by standard risk measures, a typology of stochastic processes that pinpoints when a non-probabilistic description of long run risk is required, and an evolutionary foundation for variational ambiguity preferences that offers a novel explanation for ambiguity aversion. |
| JEL: | C73 D81 G12 H43 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33291 |
| By: | Pérez Velilla, Alejandro (University of California, Merced); Beheim, Bret; Smaldino, Paul E. |
| Abstract: | We use cultural evolutionary models to examine how individual experiences and culturally-inherited information jointly shape risk attitudes under environmental uncertainty. We find that learning processes not only generate plausible variation in risk attitudes, but also that conservative learning strategies---emphasizing the preservation of generational knowledge---excel in high-risk environments, promoting stable wealth accumulation and long-term survival but limiting asset growth as conditions improve. In contrast, exploratory learning strategies---leveraging risk-free juvenile exploration and peer influence---foster risk-tolerant attitudes that thrive in affluent, low-risk settings where wealth buffers and social safety nets reduce the costs of miscalculations. Introducing economic stratification to the model reveals how wealth disparities and inter-class interactions reinforce these patterns, exacerbating differences in learning strategies and risk-taking behaviors, and perpetuating socioeconomic inequalities through the cultural inertia of excessive risk aversion. By uniting developmental, social, and evolutionary perspectives, our framework provides a novel lens on the cultural evolution of risk attitudes and their broader societal implications. |
| Date: | 2025–01–24 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:9yjes |
| By: | Bernhard K Meister |
| Abstract: | Betting markets are gaining in popularity. Mean beliefs generally differ from prices in prediction markets. Logarithmic utility is employed to study the risk and return adjustments to prices. Some consequences are described. A modified payout structure is proposed. A simple asset price model based on flipping biased coins is investigated. It is shown using the Kullback-Leibler divergence how the misjudgment of the bias and the miscalculation of the investment fraction influence the portfolio growth rate. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.14144 |
| By: | de Castro, Luciano; Frischtak, Claudio; Rodrigues, Arthur |
| Abstract: | Most developing economies rely on foreign capital to finance their infrastructure needs. These projects are usually structured as long-term (25–35 years) franchises that pay in local currency. If investors evaluate their returns in terms of foreign currency, exchange rate volatility introduces risk that may reduce the level of investment below what would be socially optimal. In this article, we propose a mechanism with very general features that hedges exchange rate fluctuation by adjusting the concession period. Such mechanism does not imply additional costs to the government and could be offered as a zero-cost option to lenders and investors exposed to currency fluctuations. We illustrate the general mechanism with three alternative specifications and use data from a 25-year highway franchise to simulate how they would play out in eight different emerging economies that exhibit diverse exchange rate trajectories. Results show relatively small length adjustments, and suggest the mechanism offers a powerful policy tool to cost-effectively attract vital foreign infrastructure investment for developing countries. |
| Keywords: | bidding for public projects; concession periods; exchange rate risk; government protection; infrastructure projects; insurance for exchange rate risk; investors risk aversion |
| JEL: | H40 F30 H80 |
| Date: | 2025–01–07 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126881 |
| By: | Florig, Michael; Gossner, Olivier |
| Abstract: | We examine a general equilibrium investment model in which agents incur management costs for holding assets. We characterize the influence of these costs on equilibrium prices as a weighted average of these costs for market participants. We then propose a correction method for this influence in valuation procedures used under regulatory frameworks, such as Solvency II. For insurers subject to Solvency II, the accounting correction amounts to approximately €130 billion, the equivalent of 1.8% of investments or 14% of own funds. These results not only contribute to the understanding of management costs in market equilibrium, but also highlight a distortion in current practices which discourages the holding of assets that are expensive to manage and typically inaccessible directly by policyholders. |
| Keywords: | general equilibrium; insurance; Solvency II; management costs; valuation |
| JEL: | D53 G22 L51 M41 |
| Date: | 2024–12–13 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:125396 |
| By: | Igor Martins; Hedibert Freitas Lopes |
| Abstract: | This paper expands on stochastic volatility models by proposing a data-driven method to select the macroeconomic events most likely to impact volatility. The paper identifies and quantifies the effects of macroeconomic events across multiple countries on exchange rate volatility using high-frequency currency returns, while accounting for persistent stochastic volatility effects and seasonal components capturing time-of-day patterns. Given the hundreds of macroeconomic announcements and their lags, we rely on sparsity-based methods to select relevant events for the model. We contribute to the exchange rate literature in four ways: First, we identify the macroeconomic events that drive currency volatility, estimate their effects and connect them to macroeconomic fundamentals. Second, we find a link between intraday seasonality, trading volume, and the opening hours of major markets across the globe. We provide a simple labor-based explanation for this observed pattern. Third, we show that including macroeconomic events and seasonal components is crucial for forecasting exchange rate volatility. Fourth, our proposed model yields the lowest volatility and highest Sharpe ratio in portfolio allocations when compared to standard SV and GARCH models. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.16244 |
| By: | Antonello Cirulli; Gianluca De Nard; Joshua Traut; Patrick Walker |
| Abstract: | The low-risk anomaly challenges traditional financial theory by stating that less volatile stocks generate higher risk-adjusted returns. This paper explores how various portfolio construction choices influence the performance of low-risk portfolios. We show that methodological decisions critically influence portfolio outcomes, causing substantial dispersion in performance metrics across weighting schemes and risk estimators. This can only be marginally mitigated by incorporating constraints such as short-sale restrictions and size or price filters. Our analysis reveals that volatility-based estimators yield the most favorable performance distribution, outperforming beta-based approaches. Transaction costs are found to significantly affect performance and are vitally important in identifying the most attractive portfolios, highlighting the importance of realistic implementation constraints. Through rigorous empirical analysis, this study bridges the gap between theoretical insights and practical applications, offering actionable guidance to investors. The findings advocate for a cautious approach to nonstandard errors in portfolio modeling and emphasize the necessity of robust strategies in low-risk investing. |
| Keywords: | Low-risk investing, methodology, nonstandard errors, portfolio construction |
| JEL: | C52 G11 G12 G15 G17 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:463 |
| By: | Samantha Ajovalasit; Andrea Consiglio; Giovanni Pagliardi; Stavros Zenios |
| Abstract: | In this paper, we ask whether the level of political risk in a country threatens its debt sustainability |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:bre:wpaper:node_10592 |
| By: | Ken-ichi Hashimoto; Ryonghun Im; Takuma Kunieda; Akihisa Shibata |
| Abstract: | By applying a simple dynamic general equilibrium model without exogenous shocks inhabited by infinitely lived capitalists and workers, we show that a higher degree of relative risk aversion can destabilize an economy. In traditional real business cycle (RBC) theory, a higher degree of relative risk aversion dampens the amplitude of the consumption fluctuations caused by exogenous shocks through consumption smoothing. However, a higher degree of relative risk aversion combined with a high degree of elasticity of the marginal product of capital can also lead to the emergence of a nonlinear mechanism that causes endogenous business fluctuations. The nontrivial steady state loses stability due to the higher degree of relative risk aversion; thus, endogenous business fluctuations can occur. This result suggests that for a deeper understanding of boom-bust cycles, researchers should merge exogenous and endogenous business fluctuations when investigating economies. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:dpr:wpaper:1272 |
| By: | Ms. Elena D'Agosto; Michael A Hardy; Stefano Pisani; Anthony Siouclis |
| Abstract: | Traditional top-down tax gap assessments identify the size of a tax gap, but not its origins. By extracting more granular information from top-down tax gap assessments, and combining this information with compliance risk management (CRM) techniques, it is possible to: improve the accuracy of CRM techniques; improve the consistency of the likelihood and consequence dimensions of compliance risk assessments; identify emerging areas of tax compliance risk and; better disaggregate the direct and indirect revenue effects of compliance interventions, including the “behavioral component” within the indirect effects. Finally, it is also possible to determine the optimal revenue recovery from each segment of the taxpayer population. |
| Keywords: | tax administration; tax compliance; tax gap; compliance gap; Compliance Risk Management; CRM; direct revenue effect; indirect revenue effect; taxpayer behavioral component |
| Date: | 2025–01–24 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imftnm:2025/003 |
| By: | Olaniran, Abeeb; Akanni, Lateef; Salisu, Afees |
| Abstract: | We explore the role of fear associated with migration in predicting exchange rate volatility of Germany, France, and the United Kingdom within the context of the generalized autoregressive conditional heteroscedastic (GARCH) mixed-data-sampling (MIDAS) framework using United States dollar (USD) as the reference currency. While we adopt the quarterly Migration Fear Index and daily exchange rate of Euro (for France and Germany) and GBP (for the UK) to USD for the nexus between migration anxiety and exchange rate volatility, we equally augment our model with Migration Policy Uncertainty (MPU) to examine the joint predictability of the two migration fears proxies on exchange rate volatility. We conduct an empirical analysis that covers the full sample period which is further partitioned into pre- and post-GFC periods to see if the nexus is sensitive to crises periods. We find evidence of migration fears predicting exchange rate volatility of the G-3 country considered, given the statistical significance of our model’s slope coefficient. Although the influence of migration fears on the strengths of the euro and pounds relative to the USD differ, as migration fear causes the former to depreciate and the latter to appreciate, both currencies exhibit high volatility persistence during the period under scrutiny. Our findings have implications for policy-makers on whose shoulders the responsibility of exchange rate management falls. |
| Keywords: | Exchange rate, Migration, Fear, GARCH-MIDAS |
| JEL: | J6 |
| Date: | 2024–11–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123196 |
| By: | José Andrée Camarena; Juan Pablo Medina; Daniel Riera-Crichton; Carlos A. Vegh; Guillermo Vuletin |
| Abstract: | In the aftermath of the 1997-98 Asian crises, many emerging markets self-insured by accumulating international reserves (i.e., non-contingent assets) in excess of what many models predicted, while relying relatively little on state-contingent assets. This apparent over-reliance on self-insurance has been viewed as a puzzle in search of an explanation. A related, and still outstanding, puzzle is why the benefits of financial liberalization appear to be quite small and, yet, financial globalization has been unprecedented in recent decades. We show that these two puzzles can be solved by incorporating rare macroeconomic disasters in income risk. To this effect, we first fit a fat-tailed distribution to long output time series for 156 countries. We then develop a theoretical framework to quantify (i) the increase in welfare gains of financial integration and (ii) how the composition of official reserves (non-contingent and contingent) responds to bigger shocks. Our results show that fat tails lead to a sharp increase in both the gains of financial integration and self-insurance for standard values of the coefficient of risk aversion. |
| JEL: | E20 E44 F36 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33366 |
| By: | Jingchu Zhang |
| Abstract: | This paper mainly utilizes the ARDL model and principal component analysis to investigate the relationship between the volatility of China's Shanghai Composite Index returns and the variables of exchange rate and domestic and foreign bond yields in an internationally integrated stock market. This paper uses a daily data set for the period from July 1, 2010 to April 30, 2024, in which the dependent variable is the Shanghai Composite Index return, and the main independent variables are the spot exchange rate of the RMB against the US dollar, the 10-year treasury bond yields in China and the United States and their lagged variables, with the effect of the time factor added. Firstly, the development of the stock, foreign exchange and bond markets and the basic theories are reviewed, and then each variable is analyzed by descriptive statistics, the correlation between the independent variables and the dependent variable is expanded theoretically, and the corresponding empirical analyses are briefly introduced, and then the empirical analyses and modeling of the relationship between the independent variables and the dependent variable are carried out on the basis of the theoretical foundations mentioned above with the support of the daily data, and the model conclusions are analyzed economically through a large number of tests, then the model conclusions are analyzed economically. economic analysis of the model conclusions, and finally, the author proposes three suggestions to enhance the stability and return of the Chinese stock market, respectively. Key Words: Chinese Stock Market, Volatility, GARCH, ARDL Model |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.08668 |
| By: | Huy N. Chau; Miklos Rasonyi |
| Abstract: | In this paper, a new approach for solving the problems of pricing and hedging derivatives is introduced in a general frictionless market setting. The method is applicable even in cases where an equivalent local martingale measure fails to exist. Our main results include a new superhedging duality for American options when wealth processes can be negative and trading strategies are subject to a cone constraint. This answers one of the questions raised by Fernholz, Karatzas and Kardaras. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19206 |
| By: | Laura Recuero Virto (PULV - Pôle Universitaire Léonard de Vinci); Adrien Comte (CIRED - Centre International de Recherche sur l'Environnement et le Développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École nationale des ponts et chaussées - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique, AMURE - Aménagement des Usages des Ressources et des Espaces marins et littoraux - Centre de droit et d'économie de la mer - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - UBO - Université de Brest - IUEM - Institut Universitaire Européen de la Mer - IRD - Institut de Recherche pour le Développement - INSU - CNRS - Institut national des sciences de l'Univers - UBO - Université de Brest - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique); Linwood Pendleton (UBO - Université de Brest, WWF - World Wide Fund, Global Change Institute (Australia)) |
| Abstract: | Although they provide essential services to local populations, coral reefs are threatened by the impacts of a multitude of human activities. Through the notions of social and ecological vulnerability of socio-ecosystems dependent on coral reefs, it is possible to develop a risk management framework that enables us to identify and prioritize the issues at stake. In other words, the human, economic and environmental value of elements exposed to the risks of adverse events. This framework makes it possible to assess the possibilities for actions that lead either to reducing vulnerability by reducing hazard or exposure, or that lead to reinforcing response or adaptation capacity. In this policy brief, we explain the concepts of social and ecological vulnerability, and share examples of indicators for assessing and monitoring them, as well as examples of their use in identifying action plans. |
| Abstract: | Alors qu'ils fournissent des services essentiels aux populations locales, les récifs coralliens sont menacés par les impacts d'une multitude d'activités humaines. À travers les notions de vulnérabilité sociale et écologique des socio-écosystèmes dépendant des récifs coralliens, il est possible de développer un cadre de gestion de risques qui permet d'identifier et de hiérarchiser les enjeux. Autrement dit la valeur humaine, économique et environnementale des éléments exposés aux risques d'événements défavorables. Ce cadre permet d'évaluer les possibilités d'action qui conduisent soit à réduire la vulnérabilité en réduisant l'aléa ou l'exposition, soit à renforcer la capacité de réponse ou d'adaptation. Dans ce policy brief, nous expliquons les concepts de vulnérabilité sociale et écologique, nous partageons des exemples d'indicateurs pour assurer leur évaluation et leur suivi ainsi que des exemples d'usage de ces derniers pour la caractérisation de plans d'action. |
| Keywords: | Récif corallien, Socio-écosystèmes marins, Recommandations politiques, Vulnérabilité écologique, Conservation marine, Environnement marin et côtier, Activités anthropiques, Pressions, DPSIR, Directive-cadre sur le milieu marin |
| Date: | 2024–11–15 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04851832 |