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on Risk Management |
| By: | Xia Han; Liyuan Lin; Hao Wang; Ruodu Wang |
| Abstract: | A diversification quotient (DQ) quantifies diversification in stochastic portfolio models based on a family of risk measures. We study DQ based on expectiles, offering a useful alternative to conventional risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). The expectile-based DQ admits simple formulas and has a natural connection to the Omega ratio. Moreover, the expectile-based DQ is not affected by small-sample issues faced by VaR-based or ES-based DQ due to the scarcity of tail data. The expectile-based DQ exhibits pseudo-convexity in portfolio weights, allowing gradient descent algorithms for portfolio selection. We show that the corresponding optimization problem can be efficiently solved using linear programming techniques in real-data applications. Explicit formulas for DQ based on expectiles are also derived for elliptical and multivariate regularly varying distribution models. Our findings enhance the understanding of the DQ's role in financial risk management and highlight its potential to improve portfolio construction strategies. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.14646 |
| By: | Limin Wen; Junxue Li; Tong Pu; Yiying Zhang |
| Abstract: | Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk contagion. This paper introduces various types of contribution measures based on the MCoVaR, MCoES, and MMME studied in Ortega-Jim\'enez et al. (2021) and Das & Fasen-Hartmann (2018) to assess both the absolute and relative effects of a single risk when other risks in a group are in distress. The properties of these contribution risk measures are examined, and sufficient conditions for comparing these measures between two sets of random vectors are established using univariate and multivariate stochastic orders and stochastic dependence notions. Numerical examples are presented for validating the conditions. Finally, a real dataset from the cryptocurrency market is also utilized to analyze the contagion effect in terms of our proposed contribution measures. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13384 |
| By: | Tsogt-Ochir Enkhbayar |
| Abstract: | This paper advances the computational efficiency of Deep Hedging frameworks through the novel integration of Kronecker-Factored Approximate Curvature (K-FAC) optimization. While recent literature has established Deep Hedging as a data-driven alternative to traditional risk management strategies, the computational burden of training neural networks with first-order methods remains a significant impediment to practical implementation. The proposed architecture couples Long Short-Term Memory (LSTM) networks with K-FAC second-order optimization, specifically addressing the challenges of sequential financial data and curvature estimation in recurrent networks. Empirical validation using simulated paths from a calibrated Heston stochastic volatility model demonstrates that the K-FAC implementation achieves marked improvements in convergence dynamics and hedging efficacy. The methodology yields a 78.3% reduction in transaction costs ($t = 56.88$, $p |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.15002 |
| By: | Revant Nayar; Raphael Douady |
| Abstract: | Traditional Markowitz portfolio optimization constrains daily portfolio variance to a target value, optimising returns, Sharpe or variance within this constraint. However, this approach overlooks the relationship between variance at different time scales, typically described by $\sigma(\Delta t) \propto (\Delta t)^{H}$ where $H$ is the Hurst exponent, most of the time assumed to be \(\frac{1}{2}\). This paper introduces a multifrequency optimization framework that allows investors to specify target portfolio variance across a range of frequencies, characterized by a target Hurst exponent $H_{target}$, or optimize the portfolio at multiple time scales. By incorporating this scaling behavior, we enable a more nuanced and comprehensive risk management strategy that aligns with investor preferences at various time scales. This approach effectively manages portfolio risk across multiple frequencies and adapts to different market conditions, providing a robust tool for dynamic asset allocation. This overcomes some of the traditional limitations of Markowitz, when it comes to dealing with crashes, regime changes, volatility clustering or multifractality in markets. We illustrate this concept with a toy example and discuss the practical implementation for assets with varying scaling behaviors. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13792 |
| By: | Fan Yang; Yi Zhang |
| Abstract: | We study the tail asymptotics of the sum of two heavy-tailed random variables. The dependence structure is modeled by copulas with the so-called tail order property. Examples are presented to illustrate the approach. Further for each example we apply the main results to obtain the asymptotic expansions for Value-at-Risk of aggregate risk. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09657 |
| By: | Martin Arnaiz Iglesias (UP1 UFR27); Adil Rengim Cetingoz (UP1 UFR27); Noufel Frikha (UP1 UFR27) |
| Abstract: | This paper introduces and examines numerical approximation schemes for computing risk budgeting portfolios associated to positive homogeneous and sub-additive risk measures. We employ Mirror Descent algorithms to determine the optimal risk budgeting weights in both deterministic and stochastic settings, establishing convergence along with an explicit non-asymptotic quantitative rate for the averaged algorithm. A comprehensive numerical analysis follows, illustrating our theoretical findings across various risk measures -- including standard deviation, Expected Shortfall, deviation measures, and Variantiles -- and comparing the performance with that of the standard stochastic gradient descent method recently proposed in the literature. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12323 |
| By: | Jonathan Ansari; Eva L\"utkebohmert |
| Abstract: | This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing with a common risk factor. We provide simple and interpretable conditions for conditional default probabilities that imply a comparison of credit portfolio losses in convex order. In the case of threshold models, the ranking of portfolio losses is based on a pointwise comparison of the underlying copulas. Our setting includes as special case the well-known Gaussian copula model but allows for general tail dependencies, which are crucial for modeling credit portfolio risks. Moreover, our results extend the classical parameterized models, such as the industry models CreditMetrics and KMV Portfolio Manager, to a robust setting where individual parameters or the copula modeling the dependence structure can be ambiguous. A simulation study and a real data example under model uncertainty offer evidence supporting the effectiveness of our approach. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11522 |
| By: | Peter Cotton |
| Abstract: | Despite many attempts to make optimization-based portfolio construction in the spirit of Markowitz robust and approachable, it is far from universally adopted. Meanwhile, the collection of more heuristic divide-and-conquer approaches was revitalized by Lopez de Prado where Hierarchical Risk Parity (HRP) was introduced. This paper reveals the hidden connection between these seemingly disparate approaches. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05807 |
| By: | Wang, Shu |
| Abstract: | This paper presents a high-frequency structural VAR framework for identifying oil price shocks and examining their uncertainty transmission in the U.S. macroeconomy and financial markets. Leveraging the stylized features of financial data - specifically, volatility clustering effectively captured by a GARCH model - this approach achieves global identification of shocks while allowing for volatility spillovers across them. Findings reveal that increased variance in aggregate demand shocks increases the oil-equity price covariance, while precautionary demand shocks, triggering heightened investor risk aversion, significantly diminish this covariance. A real-time forecast error variance decomposition further highlights that oil supply uncertainty was the primary source of oil price forecast uncertainty from late March to early May 2020, yet it contributed minimally during the 2022 Russian invasion of Ukraine. |
| Keywords: | Oil price, uncertainty, impulse response functions, structural VAR, forecast error variance decomposition, GARCH |
| JEL: | Q43 Q47 C32 C58 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cegedp:307602 |
| By: | Issam Samiri |
| Abstract: | I propose a general equilibrium model with endogenous defaults among producers and a Value-at-Risk rule designed to stabilise insolvency risk in the banking sector. Bank equity fluctuates with aggregate default rates, affecting banks' lending capacity. The Value-at-Risk constraint induces procyclical leverage, amplifying the impact of bank equity fluctuations on credit supply. This mechanism generates countercyclical risk premia in lending rates, thus intensifying economic shocks. Analytical exploration identifies three channels driving the dynamics of bank leverage and credit spreads: (a) the credit demand channel, (b) the bank equity channel, and (c) a risk channel that captures the interaction between default expectations and the Value-at-Risk constraint. The model is calibrated to quantitatively replicate fluctuations in banks' balance sheets, credit spreads, and real business cycle variables. |
| Keywords: | RBC, Value-at-Risk, bank leverage, Credit Spreads, Financial Frictions |
| JEL: | E13 E32 E44 G21 G32 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nsr:niesrd:562 |
| By: | Pierdomenico Duttilo |
| Abstract: | This PhD Thesis presents an investigation into the analysis of financial returns using mixture models, focusing on mixtures of generalized normal distributions (MGND) and their extensions. The study addresses several critical issues encountered in the estimation process and proposes innovative solutions to enhance accuracy and efficiency. In Chapter 2, the focus lies on the MGND model and its estimation via expectation conditional maximization (ECM) and generalized expectation maximization (GEM) algorithms. A thorough exploration reveals a degeneracy issue when estimating the shape parameter. Several algorithms are proposed to overcome this critical issue. Chapter 3 extends the theoretical perspective by applying the MGND model on several stock market indices. A two-step approach is proposed for identifying turmoil days and estimating returns and volatility. Chapter 4 introduces constrained mixture of generalized normal distributions (CMGND), enhancing interpretability and efficiency by imposing constraints on parameters. Simulation results highlight the benefits of constrained parameter estimation. Finally, Chapter 5 introduces generalized normal distribution-hidden Markov models (GND-HMMs) able to capture the dynamic nature of financial returns. This manuscript contributes to the statistical modelling of financial returns by offering flexible, parsimonious, and interpretable frameworks. The proposed mixture models capture complex patterns in financial data, thereby facilitating more informed decision-making in financial analysis and risk management. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11847 |
| By: | Liang Hou; Hao Yu; Guosong Xu |
| Abstract: | In this paper, we introduce a novel pricing model for Uniswap V3, built upon stochastic processes and the Martingale Stopping Theorem. This model innovatively frames the valuation of positions within Uniswap V3. We further conduct a numerical analysis and examine the sensitivities through Greek risk measures to elucidate the model's implications. The results underscore the model's significant academic contribution and its practical applicability for Uniswap liquidity providers, particularly in assessing risk exposure and guiding hedging strategies. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12375 |
| By: | Josué Thélissaint (Univ Rennes, CNRS, CREM – UMR6211, F-35000 Rennes, France) |
| Abstract: | This paper aims at exploring risk factors which are driving forces behind the global cryptomarket behaviour. Its purpose is to enhance understanding of the transmission mechanisms of aggregated fluctuations. Identification of such factors will contribute to laying foundation for anchoring expectations from a forward-looking perspective. We use the Factor-Augmented Autoregression (FAVAR) framework to estimate the latent factors. Nevertheless due to asymmetries, assessments are performed through a Threshold-VAR which helps to highlight predictive power of the factors. Six leading risk factors are identified. Each of them represents distinct market risks and exerts asymmetric effects on cryptomarket dynamics. Especially, the first factor (F1) encapsulates global market risks associated with volatility and collapse uncertainty. It orchestrates regime transitions among low−, medium−, and high − risk states. The sixth factor (F6) reflects market optimism toward cryptos. It shows a notable negative correlation (− 37%) with F1 over 20 business days. While F1 demonstrates high persistence, other factors exhibit mean-reverting behaviour. Furthermore, our findings are complemented by insights into the structure of shock transmission across different time horizons, highlighting the joint influence of macroeconomic and emotional shocks on market trajectories. Overall, this paper contributes to the existing literature as it offers a novel perspective on risk factors in cryptomarkets and it underscores specific issues for further research. |
| Keywords: | cryptomarkets, common risk factor, factor-augmented vector auto-regression, nonlinear impulse response function, time-varying frequency connectednes |
| JEL: | C58 G12 G17 G41 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:tut:cremwp:2024-14 |
| By: | Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | Recent global economic and political events have made clear that shortages are a key factor driving macroeconomic and financial market developments. Against this backdrop, we studied the forecasting value of shortages for monthly U.S. stock market realized variance (RV) at the aggregate and sectoral level using data spanning the period 1900-2024 and 1926-2023 (for most sectors), respectively. To this end, we considered linear and non-linear statistical learning estimators. When we used linear estimators (OLS and shrinkage estimators), we did not find evidence that aggregate and disaggregate shortage indexes have predictive value for subsequent market or sectoral RVs. In contrast, when we used random forests, a nonlin- ear nonparametric estimator, we detected that aggregate and disaggregate shortage indexes improve forecast accuracy of market and sectoral RVs after controlling for realized moments (realized leverage, realized skewness, realized kurtosis, realized tail risks). We then decomposed RV into a high, medium, and low frequency component and found that the shortages indexes are correlated mainly with the medium and low frequencies of RV. |
| Keywords: | Shortages, Stock market, Realized volatility, Statistical learning, Forecasting |
| JEL: | C22 C53 E23 G10 G17 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202450 |
| By: | De Oliveira Amado, Cristina Alexandra; Garrón Vedia, Ignacio; Lopes Moreira da Veiga, María Helena |
| Abstract: | This paper addresses the efect of country geopolitical risk on energy inflation by employing a methodology that combines fixed efects panel quantile regressions and local projections. The panel covers country-level indicators for 16 OECD countries from January 1990 to December 2022. Results show that adverse country-specific geopolitical events are associated with upside risks on energy inflation whereas the analysis reveals insignificant efects for both the median and left tail of energy inflation distribution. |
| Keywords: | Geopolitical risk; Energy inflation; Fixed efects; Panel data; Quantile regression |
| JEL: | C22 C23 E31 Q40 |
| Date: | 2024–11–29 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:45113 |
| By: | Edward Honda; Keh-Kuan Sun |
| Abstract: | We provide and axiomatize a representation of preferences over lotteries that generalizes the expected utility model. Our representation is consistent with the violations of the independence axiom that we observe in the laboratory experiment that we conduct. The violations differ from the Allais Paradox in that they are incompatible with some of the most prominent non-expected utility models. Our representation can be interpreted as a decision-maker with context-dependent attitudes to risks and allows us to generate various types of realistic behavior. We analyze some properties of our model, including specifications that ensure preferences for first-order stochastic dominance. We test whether subjects in our experiment exhibit the type of context-dependent risk attitudes that arise in our model. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13823 |
| By: | Bublu Thakur-Weigold; Sébastien Miroudot |
| Abstract: | This working paper contributes to the debate on effective solutions for assuring the resilience of critical global supply chains by undertaking a review of both the supply chain management literature and recent actions by firms and governments. The report highlights that when pursuing the resilience of global supply chains, policy should focus on the performance of the system as a whole and not target a single objective, such as security of supply. In addition, resilience strategies should be segmented to address two distinct categories of risks: business-as-usual disruptions that can be mitigated by standard risk management practices of firms and unforeseen extreme disruptions where the role of governments is crucial as facilitators and providers of emergency resources. Effective interventions include reducing logistics frictions, regulatory co-operation and flexibility, and fostering an industrial commons for emergency preparedness. Regular preparedness conferences would enable public-private stakeholders to co-ordinate responses to future crises. |
| Keywords: | Global supply chains, Risk management |
| JEL: | D81 F23 F63 H12 L23 |
| Date: | 2024–11–28 |
| URL: | https://d.repec.org/n?u=RePEc:oec:traaab:286-en |
| By: | Jose Barrales-Ruiz (Center of Economics for Sustainable Development (CEDES), Faculty of Economics and Government, Universidad San Sebastian and Universidad Catolica de la Santısima Concepcion.); Ivan Mendieta-Muñoz (Department of Economics, University of Utah) |
| Abstract: | This paper examines the changes in the dynamic interactions between aggregate demand and income distribution in the USA. We focus on two periods that capture the relevant characteristics before and after contemporary neoliberal capitalism. We study the interactions between aggregate demand and income distribution in both periods using structural quantile vector autoregression models. This allows us to assess the informational content of the dynamic interactions at all parts of the relevant distributions, including the potential tail risks. The results show evidence of important reductions in the profit-led effect across the whole distribution of aggregate demand during neoliberalism; while profit squeeze dynamics have decreased at most parts of the distribution of income but have increased its downside risk, thus becoming more heterogeneous across the distribution of income. Notwithstanding the underlying transmission mechanisms have remained unaltered across the two periods, our results highlight that the interactions between aggregate demand and income distribution have become a more complex phenomenon to study since the mid-1980s. |
| Keywords: | Aggregate demand, income distribution, tail risks, quantile vector autoregression, neoliberalism |
| JEL: | D33 E11 E12 E32 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:new:wpaper:2414 |
| By: | Bernhard Mayr |
| Abstract: | An increased frequency and intensity of climate-related natural catastrophes has created significant challenges for both the private and the public sector. Existing risk-sharing approaches are reaching their efficacy limits, pushing governments to take on an increasing share of the burden as private-sector solutions become less affordable or available. This paper outlines how adding a European loan-based backstop facility to the risk-sharing hierarchy can contribute to a more efficient solution and why it may enhance private insurers’ risk-taking capacity. We elaborate on the mechanics of such an approach and show how it could increase private sector insurance capacity without additionally burdening the public. |
| Date: | 2024–11–27 |
| URL: | https://d.repec.org/n?u=RePEc:stm:dpaper:24 |