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on Risk Management |
| By: | Eden Gross; Ryan Kruger; Francois Toerien |
| Abstract: | In the last five years, expected shortfall (ES) and stressed ES (SES) have become key required regulatory measures of market risk in the banking sector, especially following events such as the global financial crisis. Thus, finding ways to optimize their estimation is of great importance. We extend the application of dynamic Bayesian networks (DBNs) to the estimation of 10-day 97.5% ES and stressed ES, building on prior work applying DBNs to value at risk. Using the S&P 500 index as a proxy for the equities trading desk of a US bank, we compare the performance of three DBN structure-learning algorithms with several traditional market risk models, using either the normal or the skewed Student's t return distributions. Backtesting shows that all models fail to produce statistically accurate ES and SES forecasts at the 2.5% level, reflecting the difficulty of modeling extreme tail behavior. For ES, the EGARCH(1, 1) model (normal) produces the most accurate forecasts, while, for SES, the GARCH(1, 1) model (normal) performs best. All distribution-dependent models deteriorate substantially when using the skewed Student's t distribution. The DBNs perform comparably to the historical simulation model, but their contribution to tail prediction is limited by the small weight assigned to their one-day-ahead forecasts within the return distribution. Future research should examine weighting schemes that enhance the influence of forward-looking DBN forecasts on tail risk estimation. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.12334 |
| By: | Carboni, Giacomo; Fonseca, Luís; Fornari, Fabio; Urrutia, Leonardo |
| Abstract: | We investigate the impact of structural shocks on the joint distribution of future real GDP growth and inflation in the euro area. We model the conditional mean of these variables, along with selected financial indicators, using a VAR and perform quantile regressions on the VAR residuals to estimate their time-varying variance as a function of macroeconomic and financial variables. Through impulse response analysis, we find that demand and financial shocks reduce expected GDP growth and increase its conditional variance, leading to negatively skewed future growth distributions. By enabling this mean-volatility interaction, demand and financial shocks drive significant time variation in downside risk to euro area GDP growth, while supply shocks result in broadly symmetric movements. For inflation, supply shocks drive instead a positive mean-volatility co-movement, where higher inflation is associated with increased uncertainty, causing time variation in upside risk. JEL Classification: C32, C58, E32, G17 |
| Keywords: | downside risk, euro area, mean-variance correlation, quantile regressions, stochastic volatility, structural shocks, tail risk, vector autoregression (VAR) |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263171 |
| By: | Kärnä, Anders (Financial Stability Department, Central Bank of Sweden); Östling Svensson, Karin (Financial Stability Department, Central Bank of Sweden) |
| Abstract: | This paper examines how accurately Swedish banks’ internal probability of default (PD) models under IFRS 9 accounting rules predict changes in the borrowing firms’ credit risk levels. Using a sample of matched bank lending and firm-level data, we find that PDs align well with aggregate transitions to an elevated risk level, but explain little of the variation across individual borrowers. Lending rates, in contrast, provide limited information on moderate distress levels but are more predictive of severe credit events. The findings suggest that PDs capture both risk assessment and accounting conventions in a non-linear and complex pattern, highlighting the importance of combining regulatory and market-based indicators when monitoring credit risk. |
| Keywords: | Probability of Default; Bankruptcy; Financial Distress |
| JEL: | G33 L25 |
| Date: | 2025–12–01 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:rbnkwp:0458 |
| By: | Fantazzini, Dean |
| Abstract: | In this study, we extend research on stablecoin credit risk by introducing a novel rule-of-thumb approach to determine whether a stablecoin is ``dead" or ``alive" based on a simple price threshold. Using a comprehensive dataset of 98 stablecoins, we classify a coin as failed if its price falls below a predefined threshold (e.g., \$0.80), validated through sensitivity analysis against established benchmarks such as CoinMarketCap delistings and \cite{feder2018rise} methodology. We employ a wide range of panel binary models to forecast stablecoins' probabilities of default (PDs), incorporating stablecoin-specific regressors. Our findings indicate that panel Cauchit models with fixed effects outperform other models across different definitions of stablecoin failure, while lagged average monthly market capitalization and lagged stablecoin volatility emerge as the most significant predictors—outweighing macroeconomic and policy-related variables. Random forest models complement our analysis, confirming the robustness of these key drivers. This approach not only enhances the predictive accuracy of stablecoin PDs but also provides a practical, interpretable framework for regulators and investors to assess stablecoin stability based on credit risk dynamics. |
| Keywords: | stablecoins; crypto-assets; cryptocurrencies; credit risk; probability of default; probability of death; panel binary models; fixed effects; cauchit; ZPP |
| JEL: | C32 C35 C51 C53 C58 G12 G17 G32 G33 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126906 |
| By: | Lucas, André; Schwaab, Bernd; Zhang, Xin; D’Innocenzo, Enzo |
| Abstract: | We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for peaks-over-threshold (POT) dynamics. Unlike earlier approaches, our model (i) has unit root-like, i.e., integrated autoregressive dynamics for the GPD tail shape, and (ii) re-scales POTs by their thresholds to obtain a more parsimonious model with only one time-varying parameter to describe the entire tail. We establish parameter regions for stationarity, ergodicity, and invertibility for the integrated time-varying parameter model and its filter, and formulate conditions for consistency and asymptotic normality of the maximum likelihood estimator. Using two cryptocurrency exchange rates, we illustrate how the simple single-parameter model is competitive in capturing the dynamics of VaR and ES, particularly in the extreme tail. JEL Classification: C22, G11 |
| Keywords: | dynamic tail risk, extreme value theory, integrated score-driven models |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263166 |
| By: | Li, Linzhou |
| Abstract: | In this paper, the hedging strategy of China Eastern Airlines (CEA) in 2022, which is affected by the dual crises of COVID-19 pandemic and Russia-Ukraine war will be examined. Meanwhile, how these external shocks affect the effectiveness of the CEA's hedging will be described. Specifically, the results of the study show that the pandemic weakened the value of hedging. Global oil prices have been in position due to the decline in passenger demand. the CEA hedges lead to realized losses and deterioration of the firm's financial performance. In contrast, the Russia-Ukraine war triggered a significant increase in fuel prices. However, low hedging ratios and poor timing limited its effectiveness. Worse economic scenarios followed. Additionally, this article highlights the dual nature of hedging by analyzing the financial, managerial and policy dimensions. The research shows that it is not seen as an insurance policy only. Different scenarios of demand-side and supply-side shocks can also determine its value. The CEA experience consequently emphasizes the need for more flexible and adaptive risk management frameworks combined with operational strategies to better protect against risks with high future uncertainty. It also provides inspiration for global airlines and policymakers who are seeking to increase corporate resilience in an era of heightened volatility. |
| Keywords: | fuel price hedging; airline risk management; crisis impact; corporate resilience |
| JEL: | R14 J01 N0 |
| Date: | 2025–12–27 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:130836 |
| By: | Bonesini, Ofelia; Jacquier, Antoine; Muguruza, Aitor |
| Abstract: | On the one hand, rough volatility has been shown to provide a consistent framework to capture the properties of stock price dynamics both under the historical measure and for pricing purposes. On the other hand, market price of volatility risk is a well-studied object in financial economics, and empirical estimates show it to be stochastic rather than deterministic. Starting from a rough volatility model under the historical measure, we take up this challenge and provide an analysis of the impact of such a non-deterministic risk for pricing purposes. |
| Keywords: | fractional Brownian motion; risk premium; rough volatility |
| JEL: | F3 G3 |
| Date: | 2025–12–11 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:130975 |
| By: | Liang, Pan; Chen, Xuan; Shi, Longzhong |
| Abstract: | China launched its hog futures market on January 8, 2021, yet its impact remains largely unexplored. We assess the hedging effectiveness of China’s hog futures using a GJR-GARCH model with various copula functions. Utilizing hog futures prices from February 2021 to August 2023, along with national and provincial spot prices, we examine hedging effectiveness at both the national and provincial levels. Our findings suggest that hedging with hog futures reduces price volatility and increases mean returns at both the national and provincial levels, while regional heterogeneities in hedging effectiveness are observed. Moreover, the symmetrized Joe-Clayton (SJC) copula and the time-varying SJC copula, which capture asymmetric tail dependence, outperform alternative copula functions in terms of model fit, hedge ratios, and hedging effectiveness. These findings suggest that the establishment of China’s hog futures market has significantly stabilized spot prices, mitigated risks, and enhanced returns, while accounting for regional differences in hedging strategies and improving market liquidity in underperforming areas remain critical for optimizing hedging outcomes. |
| Keywords: | Risk and Uncertainty |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360694 |
| By: | Travon Lucius; Christian Koch Jr; Jacob Starling; Julia Zhu; Miguel Urena; Carrie Hu |
| Abstract: | We present a reinforcement-learning (RL) framework for dynamic hedging of equity index option exposures under realistic transaction costs and position limits. We hedge a normalized option-implied equity exposure (one unit of underlying delta, offset via SPY) by trading the underlying index ETF, using the option surface and macro variables only as state information and not as a direct pricing engine. Building on the "deep hedging" paradigm of Buehler et al. (2019), we design a leak-free environment, a cost-aware reward function, and a lightweight stochastic actor-critic agent trained on daily end-of-day panel data constructed from SPX/SPY implied volatility term structure, skew, realized volatility, and macro rate context. On a fixed train/validation/test split, the learned policy improves risk-adjusted performance versus no-hedge, momentum, and volatility-targeting baselines (higher point-estimate Sharpe); only the GAE policy's test-sample Sharpe is statistically distinguishable from zero, although confidence intervals overlap with a long-SPY benchmark so we stop short of claiming formal dominance. Turnover remains controlled and the policy is robust to doubled transaction costs. The modular codebase, comprising a data pipeline, simulator, and training scripts, is engineered for extensibility to multi-asset overlays, alternative objectives (e.g., drawdown or CVaR), and intraday data. From a portfolio management perspective, the learned overlay is designed to sit on top of an existing SPX or SPY allocation, improving the portfolio's mean-variance trade-off with controlled turnover and drawdowns. We discuss practical implications for portfolio overlays and outline avenues for future work. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.12420 |
| By: | Alexis Direr (LEO - Laboratoire d'Économie d'Orleans [UMR7322] - UO - Université d'Orléans - UT - Université de Tours - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | I study the allocation problem of investors who hold their portfolio until reaching a target wealth. The strategy suppresses final wealth uncertainty but creates a time horizon risk. I begin with a classical mean variance model transposed in the duration domain, then study a dynamic portfolio choice problem with Generalized Expected Discounted Utility preferences. Using long-term US return data, I show in the mean variance model that a large amount of time horizon risk can be diversified away by investing a significant share of equities. In the dynamic model, more impatient investors are also more averse to timing risk and invest less in equities. The optimal equity share is downward trending as accumulated wealth approaches its target. |
| Keywords: | portfolio choice risk aversion timing risk, portfolio choice, risk aversion, timing risk |
| Date: | 2023–12–29 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05384201 |
| By: | Roudari, Soheil; Ahmadian- Yazdi, Farzaneh; Chenarani, Hasan; Mensi, Walid |
| Abstract: | Middle Eastern countries, due to their natural and financial resources, occupy a strategic position in the global economy. Despite this, portfolio management of their financial markets remains largely unexplored amid political and geopolitical crises. This study investigates return spillovers among eight selected currencies, analyzing total connectedness (TCI), net transmitters and receivers of risk, dynamic optimal weights, hedge effectiveness, cumulative returns, and Sharpe ratios using MVP, MCP, and MCOP approaches. Findings based on Broadstock et al. (2022) approach, show that the UAE and Saudi Arabia currencies are the main risk transmitters, while Lebanon is the primary receiver. The Israeli shekel exhibits the lowest network connection, making it a suitable asset for portfolio diversification. TCI surged to 65% during the Russia-Ukraine war, reducing diversification opportunities, then rose again during the Israel-Hamas conflict and the 12-day Israel-Iran war, ultimately reaching 50% by the study’s end. Optimal weights and hedge effectiveness indicate that currency selection depends on market conditions and the applied approach; for example, the Qatari stock market offers significant risk management potential, while the MCP approach achieves the highest cumulative returns and Sharpe ratios. Overall, the study highlights that effective risk management in the Middle East requires attention to geopolitical dynamics and structural market changes, providing practical insights for investors and policymakers to optimize asset allocation and enhance financial stability in high-risk environments. |
| Keywords: | Risk spillovers, Portfolio management, Geopolitical risk, Middle East Stock Markets |
| JEL: | G14 |
| Date: | 2025–10–16 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126960 |
| By: | Einmahl, John (Tilburg University, School of Economics and Management); Peng, Liang |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:a37f0243-44eb-4f2c-8e49-40e8fd450e92 |
| By: | Suparna Biswas; Rituparna Sen |
| Abstract: | Historically, financial risk management has mostly addressed risk factors that arise from the financial environment. Climate risks present a novel and significant challenge for companies and financial markets. Investors aiming for avoidance of firms with high carbon footprints require suitable risk measures and portfolio management strategies. This paper presents the construction of decarbonized indices for tracking the S \& P-500 index of the U.S. stock market, as well as the Indian index NIFTY-50, employing two distinct methodologies and study their performances. These decarbonized indices optimize the portfolio weights by minimizing the mean-VaR and mean-ES and seek to reduce the risk of significant financial losses while still pursuing decarbonization goals. Investors can thereby find a balance between financial performance and environmental responsibilities. Ensuring transparency in the development of these indices will encourage the excluded and under-weighted asset companies to lower their carbon footprints through appropriate action plans. For long-term passive investors, these indices may present a more favourable option than green stocks. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.21092 |
| By: | Jherek Healy |
| Abstract: | The Heston stochastic volatility model is arguably, the most popular stochastic volatility model used to price and risk manage exotic derivatives. In spite of this, it is not necessarily easy to calibrate to the market and obtain stable exotic option prices with this model. This paper focuses on the vol-of-vol parameter and its relation with the volatility of volatility index (VVIX) level. Four different approaches to estimate the VVIX in the Heston model are presented: two based on the known transition density of the variance, one analytical approximation, and one based on the Heston PDE which computes the value directly out of the underlying SPX500. Finally we explore their use to improve calibration stability. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.19611 |
| By: | Ihlas Sovbetov |
| Abstract: | Decentralized finance (DeFi) lacks centralized oversight, often resulting in heightened volatility. In contrast, centralized finance (CeFi) offers a more stable environment with institutional safeguards. Institutional backing can play a stabilizing role in a hybrid structure (HyFi), enhancing transparency, governance, and market discipline. This study investigates whether HyFi-like cryptocurrencies, those backed by institutions, exhibit lower price risk than fully decentralized counterparts. Using daily data for 18 major cryptocurrencies from January 2020 to November 2024, we estimate panel EGLS models with fixed, random, and dynamic specifications. Results show that HyFi-like assets consistently experience lower price risk, with this effect intensifying during periods of elevated market volatility. The negative interaction between HyFi status and market-wide volatility confirms their stabilizing role. Conversely, greater decentralization is strongly associated with increased volatility, particularly during periods of market stress. Robustness checks using quantile regressions and pre-/post-Terra Luna subsamples reinforce these findings, with stronger effects observed in high-volatility quantiles and post-crisis conditions. These results highlight the importance of institutional architecture in enhancing the resilience of digital asset markets. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.19251 |
| By: | Hamza Bennani (Nantes Université, LEMNA, 44300 Nantes, France); Noémi Berlin (CNRS, Université Paris Nanterre, EconomiX, F-92000 Nanterre, France); Pauline Gandré (CNRS, Université Lumière Lyon 2, Université Jean-Monnet Saint-Etienne, emlyon business school, GATE, 69007, Lyon, France) |
| Abstract: | Following the Covid-19 crisis, extreme macroeconomic risks in terms of both GDP and inflation have returned to the spotlight in Europe. Against this backdrop, we conducted a large-scale online survey experiment in five large European countries (France, Germany, Italy, Spain and the UK) to measure household expectations of future extreme macroeconomic risks and their transmission to personal expectations and planned financial decisions. Exploiting both a between and within-subject design, we provided half of the participants with information about past extreme macroeconomic events in Europe. Our findings indicate that European households have high expectations of future tail macroeconomic events shaped by personal experiences, and that the causal effect of information provision on expectations varies greatly depending on the country and the type of risk. We then find suggestive evidence that expectations of extreme macroeconomic disasters are causally transmitted to personal expectations about one’s future standard of living. However, small variations in expectations of extreme macroeconomic risk do not appear to have a systematic independent impact on planned saving, portfolio, and borrowing decisions. |
| Keywords: | Extreme macroeconomic risk; expectations; information experiment; household finance |
| JEL: | E70 D83 G11 G51 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:gat:wpaper:2601 |
| By: | Minh Chau Nguyen; Tony S. Wirjanto; Fan Yang |
| Abstract: | Catastrophe risk has long been recognized to pose a serious threat to the insurance sector. Although natural disasters such as flooding, hurricane or severe drought are rare events, they generally lead to devastating damages that traditional insurance schemes may not be able to efficiently cover. Catastrophe risk pooling is an effective way to diversify the losses from such risks. In this paper, we improve the catastrophe risk pool by Pareto-optimally allocating the diversification benefits among participants. Finding the practical Pareto-optimal pool entails solving a high-dimensional optimization problem, for which analytical solutions are typically unavailable and numerical methods can be computationally intensive and potentially unreliable. We propose evaluating the diversification benefits at the limit case and using it to approximate the optimal pool by deriving an asymptotic optimal pool. Simulation studies are undertaken to explore the implications of the results and an empirical analysis from the U.S. National Flood Insurance Program is also carried out to illustrate how this framework can be applied in practice. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.18790 |
| By: | Fabien Le Floc'h |
| Abstract: | Based on the existing literature, this article presents the different ways of choosing the parameters of stochastic volatility models in general, in the context of pricing financial derivative contracts. This includes the use of stochastic volatility inside stochastic local volatility models. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.19821 |
| By: | Sandeep Neela |
| Abstract: | Financial crises emerge when structural vulnerabilities accumulate across sectors, markets, and investor behavior. Predicting these systemic transitions is challenging because they arise from evolving interactions between market participants, not isolated price movements alone. We present Systemic Risk Radar (SRR), a framework that models financial markets as multi-layer graphs to detect early signs of systemic fragility and crash-regime transitions. We evaluate SRR across three major crises: the Dot-com crash, the Global Financial Crisis, and the COVID-19 shock. Our experiments compare snapshot GNNs, a simplified temporal GNN prototype, and standard baselines (logistic regression and Random Forest). Results show that structural network information provides useful early-warning signals compared to feature-based models alone. This correlation-based instantiation of SRR demonstrates that graph-derived features capture meaningful changes in market structure during stress events. The findings motivate extending SRR with additional graph layers (sector/factor exposure, sentiment) and more expressive temporal architectures (LSTM/GRU or Transformer encoders) to better handle diverse crisis types. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.17185 |
| By: | Perdichizzi, Salvatore; Reghezza, Alessio; Spaggiari, Martina; Koufopoulos, Kostas; McGowan, Danny |
| Abstract: | We use a novel data set containing all corporate loans throughout the Eurozone to document a series of novel stylized facts on the relationship between collateral and the probability of default. First, we show that the pervasive empirical finding that riskier borrowers pledge collateral is driven by economists’ informational disadvantage relative to banks. Accounting for time-varying bank- and firm-specific risk factors produces negative correlations consistent with theory. Second, the relationship between pledging collateral and the probability of default is non-linear. Increasing the ex-ante collateral-to-loan ratio initially lowers the default likelihood but increases it as loans become overcollateralized. Third, this is driven by the riskiness of collateral. We estimate that an increase in the ex-ante collateral-to-loan ratio correlates with greater variance in the underlying collateral’s market value after loan origination. We develop a model featuring risk-neutral agents and risky collateral that provides intuition for these empirical patterns. Pledging risky collateral lowers lenders’ expected returns in case of default, leading them to demand more collateral to originate a loan but this diminishes a borrower’s return when a project is successful leading to less effort and a higher probability of default. JEL Classification: D82, G21 |
| Keywords: | collateral, default, moral hazard |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263167 |
| By: | Rong, Rong; Crago, Christine L.; Wang, Rui |
| Abstract: | Individuals making a decision about whether to adopt rooftop solar PV technology face significant financial risk, including technology underperformance, unexpected maintenance and repairs, and changes in policy regarding solar PV incentives. The presence of risk as a factor in the adoption decision suggests that individuals with a higher level of risk tolerance are more likely to adopt the technology than those who are risk averse. In addition, early adopters are also more likely to be risk-tolerant than late adopters. In this paper, we use a lab-in-the-field experiment to elicit individual risk preferences from a subject pool of solar PV adopters and non-adopters, and use this data to examine the effect of risk preference on the decision to adopt solar PV. Our findings confirm our hypothesis that risk preference plays a crucial role in determining solar PV adoption status and the timing of adoption. Our findings suggest that reducing risk in the solar market through policy or through risk-mitigating insurance products can help to broaden solar PV adoption among households. |
| Keywords: | Research and Development/Tech Change/Emerging Technologies |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:361167 |
| By: | Hilscher, Jens; Raviv, Alon; Reis, Ricardo |
| Abstract: | Long-dated inflation swap contracts provide widely used estimates of expected inflation. We develop methods to estimate complementary tail probabilities for persistently very high or low inflation using inflation options prices. We show that three new adjustments to conventional methods are crucial: inflation, horizon, and risk. We find that: (a) U.S. deflation risk in 2011–2014 has been overstated, (b) ECB unconventional policies lowered deflation disaster probabilities, (c) inflation expectations deanchored in 2021–2022, (d) reanchored as policy tightened, (e) but the 2021–2024 disaster left scars, and (f) U.S. expectations are less sensitive to inflation realizations than in the eurozone. |
| Keywords: | option prices; inflation derivatives; Arrow-Debreu securities |
| JEL: | E31 E44 E52 G13 |
| Date: | 2025–11–16 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127063 |
| By: | Roudari, Soheil; Ahmadian- Yazdi, Farzaneh; Homayounifar, Masoud; Mensi, Walid; Al-Yahyaee, Khamis Hamed |
| Abstract: | Abstract Purpose – This study examines the predictability of comparable bivariate sectors in the U.S. and Chinese stock markets, including industries such as healthcare, utilities, telecom, energy, and real estate, during periods of high market turbulence. Additionally, it analyzes the spillover effects between U.S. and Chinese sectors across varying investment time horizons, ranging from short-term to long-term. To provide deeper insights, the study also investigates the dependence structure between the two countries' sectoral stock markets. Design/methodology/approach– This study employs two methodologies to examine both static and dynamic connectedness across short-, medium-, and long-term financial cycles. These methods are the time-varying parameter vector autoregressive frequency connectedness (TVP-VAR-BK) approach proposed by Baruník and Křehlík (2018) and the Cross Quantilogram (CQ) technique. Findings – The results show that the interrelationship among stock sector returns is sensitive to major events, particularly in the short term. Moreover, China’s energy sector is the main contributor to volatility in US industry returns across all time horizons. The US industry sector consistently acts as a net transmitter of shocks to the network regardless of the investment horizon. Interestingly, US sector returns tend to transmit volatilities, while Chinese sector returns are mostly net recipients of shocks in the long term. Finally, according to the cross-quantilogram results, the optimal opportunity for portfolio diversification arises when an investor selects a similar sector from both US and Chinese markets, and the two markets are in opposite return phases (i.e., one bullish, the other bearish). Practical implications – Our findings provide valuable insights for speculators, institutional investors, and policymakers. For equity investors, the results offer practical guidance on portfolio diversification and effective hedging strategies across different market horizons. Additionally, they help investors identify the dependence structure during bearish and bullish market conditions, enabling the classification of assets as diversifiers, hedgers, or safe havens. For policymakers, the findings shed light on the sources of asset contagion, offering critical information to design strategies and reforms aimed at reducing the vulnerability of assets that serve as net shock receivers. Originality/value –Using the methodology developed by Baruník and Křehlík (2018), we examine the size and direction of connectedness across different time horizons (short, medium, and long terms). For robustness, we employ the Cross Quantilogram technique to evaluate the upper and lower dependence between US and Chinese sectors, considering various market conditions (bearish, bullish, and normal scenarios) by analyzing different quantiles. |
| Keywords: | China and US, stock sectoral index, TVP-VAR-BK model, cross-quantilogram approach. |
| JEL: | C58 G14 |
| Date: | 2024–10–14 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126963 |
| By: | Wiersema, Garbrand; Kemp, Esti; Farmer, J. Doyne |
| Abstract: | The financial crisis of 2007-2008 highlighted the risks that liquidity spirals pose to financial stability. We introduce a novel method for studying liquidity spirals and use this method to identify spirals before stock prices plummet and funding markets lock up. We show that liquidity spirals may be underestimated or completely overlooked when interactions between different types of contagion channels or institutions are ignored. We also find that financial stability is greatly affected by how institutions choose to respond to liquidity shocks, with some strategies yielding a “robust-yet-fragile" system. To demonstrate the method, we apply it to a highly granular data set on the South African banking sector and investment fund sector. We find that the risk of a liquidity spiral emerging increases when the pool of institutions' most liquid assets is reduced, while a liquidity injection by the central bank can dampen the spiral. We further show that a liquidity spiral may be due to the banking and fund sectors' collective dynamics, but can also be driven by an individual sector under some market conditions. The approach developed here canbe used to formulate interventions that specifically target the sector(s) causing the liquidity spiral. JEL Classification: G01, G17, G21, G23, G28 |
| Keywords: | financial contagion, liquidity risk, non-banks financial institutions (NBFIs), system-wide stress test, systemic risk |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263169 |