Tail Risk Research Articles

Do green financial markets offset the risk of cryptocurrencies and carbon markets?

The current controversies raised due to cryptocurrency mining and their hazardous impact on the environment have spurred the need of the current study to investigate whether green markets offset the risk of cryptocurrencies and carbon markets. We took five categories of financial markets, for instance, bonds, stocks, commodities, cryptocurrencies, and carbon markets to measure their tail risk at 5% value-at-risk (VaR). Moreover, we employed the novel technique of conditional autoregressive value-at-risk (CAViaR) along with time-varying parameters vector autoregressions (TVP-VAR) to indicate asymmetries among these financial markets. We found strong intra-class connectedness clusters with little interconnectedness among the markets. Subsequently, the time-varying trends highlighted extreme risk spillovers during the crisis times. Our findings offer valid justifications for policymakers, stakeholders, regulators, investors, and financial market participants.

Less disagreement, better forecasts: Adjusted risk measures in the energy futures market

AbstractThis paper develops a generic adjustment framework to improve in the market risk forecasts of diverse risk forecasting models, which indicates the degree to which risk is under‐ and overestimated. In the context of the energy commodity market, a market in which tail risk management is of crucial importance, the empirical analysis shows that after this adjustment framework is applied, the forecasting performance of various risk models generally improves, as verified by a battery of backtesting methods. Additionally, our method also lessens the risk model disagreement among post‐adjusted risk forecasts.

Extreme Liquidity Risk and the Cross‐Section of Expected Returns: Evidence from China*

AbstractIn this study, we investigate whether extreme liquidity risk is priced in the China A‐shares market. We find that the market extreme liquidity risk significantly and negatively predicts market returns up to 9 months. In addition, the extreme liquidity risk beta of individual stocks commands a positive monthly premium of 0.75%. Moreover, our findings show that the extreme liquidity risk beta can subsume the tail risk beta in predicting stock returns. Furthermore, our findings show that both the potential selling pressures caused by insiders and by institutional investors significantly and positively influence an individual stock's extreme liquidity risk beta. We also find that the potential selling pressure component of the extreme liquidity risk beta significantly and positively predicts stock returns. Taken together, our evidence demonstrates that a stock's extreme liquidity risk beta provides a channel through which the stock's potential selling pressure caused by both insiders and institutional investors influences its expected return in the China A‐shares market.

He Missing Tail Risk in Option Prices

he Missing Tail Risk in Option Prices

Tail risk adjusted clean energy portfolios in P2P transactive markets using Rachev ratio

AbstractRenewable energy sources (RES) and electric vehicles (EVs) pose ‘energy‐risk’ in peer energy commitments due to their temporal and spatial uncertainties. Thus, optimistic commitments in the peer‐to‐peer transactive energy market (P2P TEM) are improbable. This paper proposes a two‐stage master–slave portfolio optimization approach for combining energy‐risk of RES and EVs, and welfare‐risk of peers, in building clean energy portfolios. The master portfolio (MP) refers to the shares of renewable and EVs in P2P market settlement, whereas the slave portfolio (SP) gives the wind‐solar mix within renewables. Here, Rachev Ratio (RR), an index used in financial portfolio selection for tail‐risk management, is adapted and combined with Markowitz Efficient Frontier (EF) to find the optimal slave portfolio. Both the extreme tails are optimized, encouraging energy outputs far above forecast and discouraging those far below forecast. The master portfolio is obtained by maximizing the sum of the average welfare of the peers at the best (right) and worst (left) tails of the welfare distribution curve using Stochastic Weight Trade‐off Particle Swarm Optimization (SWT‐PSO). The proposed portfolio selection approach is better in terms of increased expected energy output, improved utilization of RES and EVs, and better collective peer welfare.

The effectiveness of weather index insurance in managing mariculture production risk

AbstractMariculture is a high‐risk industry that is susceptible to weather disasters. However, due to moral hazard, adverse selection and high transaction costs, traditional indemnity insurance policies are not available. An emerging alternative is the development of weather index mariculture insurance. This research assesses the effectiveness of weather index mariculture insurance by using the swimming crab precipitation index insurance as an example. The theoretical and empirical results suggest that weather index mariculture insurance is not always effective. It cannot be guaranteed to promote the welfare of mariculturists and reduce the tail risk of income, especially in the case of gross rates. However, in the case of low basis risk, it could be a viable option for the government because of the low subsidy cost required.

Study on international energy market and geopolitical risk contagion based on complex network

At present, the global political and economic situation is changing with geopolitical risks occurring frequently, and the commodity market represented by energy fluctuates violently. In order to deeply investigate the risk contagion status and interactive contagion mechanism between international energy market and geopolitics under the impact of extreme events, this paper selects crude oil spot prices and geopolitical risk index of major international oil-producing countries from January 2006 to December 2021, and analyzes the tail risk cross-contagion effects, dynamic effects and cyclical characteristics between international energy-geopolitics two-layer networks based on complex networks. Empirical research finds that risk spillover effect of traditional energy markets is more significant than that of clean energy markets. Russia acts as the main risk spillover of international crude oil market under extreme shocks, and geopolitical conflicts will exacerbate risk contagion between international energy markets. The analysis of energy-geopolitics two-layer network finds that the international energy market generates net risk spillover effect on geopolitics. In addition, the interconnectedness network study found that the United States, Russia and China are systemically important economies in the energy market under the complex geopolitical landscape. The risk contagion effect among systemically important economies finds that Russia's geopolitical risks have stronger impact on China's crude oil price. The cycle characteristics analysis of international crude oil market and geopolitical dynamic network finds that the short-term risk contagion effect is more obvious. The machine learning analysis found that the international crude oil-geopolitical connectedness can effectively warn the tail risk of the crude oil market, which provides basis for preventing cross-contagion risks from the perspective of system connectedness.

Systemic risk spillovers and the determinants in the stock markets of the Belt and Road countries

We construct time-varying tail risk networks to investigate systemic risk spillovers in the Belt and Road (B&R) stock markets during 2008–2021. Network metrics clearly reflect aggregate risk level and individual risk accumulation for the B&R stock markets under extreme events (e.g., 2008 financial crisis and COVID-19 pandemic). Tail-event driven network quantile regression analysis shows that network impacts of the B&R stock markets under different risk levels are asymmetric and regional heterogeneity. Panel analysis on determinants of systemic risk spillovers shows that cross-border investment and international trade are significant contagion channels while economic freedom is potential driver.

Natural gas and the utility sector nexus in the U.S.: Quantile connectedness and portfolio implications

Given that natural gas is a vital input for the U.S. utility sector, this study empirically investigates the return connectedness between the natural gas and utility stocks in the U.S. market. Using the quantile connectedness approach, we show that the nexus between natural gas and utility stocks is more pronounced at the tails compared to the central of the conditional distribution. The return connectedness indices are time-varying with a net receiver role of natural gas and driven by various macro-variables. Finally, our portfolio implication analyses with alternative tail risk measures suggest that it can be more beneficial for risk-adverse investors to allocate substantial weights into the electricity utility stocks in normal market conditions. However, during the COVID-19- induced recession, it is critical to shift more fund to the natural gas futures to reduce tail risks.

Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach

AbstractA novel forecast combination and weighted quantile‐based tail risk forecasting framework is proposed, aiming to reduce the impact of modeling uncertainty. The proposed approach is based on a two‐step estimation procedure. The first step involves the combination of value‐at‐risk (VaR) forecasts at a grid of quantile levels. A range of parametric and semiparametric models is selected as the model universe in the forecast combination procedure. The quantile forecast combination weights are estimated by optimizing the quantile loss. In the second step, the expected shortfall (ES) is computed as a weighted average of combined quantiles. The quantiles weighting structure for ES forecasting is determined by minimizing a strictly consistent joint VaR and ES loss function of the Fissler–Ziegel class. The proposed framework is applied to six stock market indices and its forecasting performance is compared to each individual model in the universe, a simple average approach and a weighted quantile approach. The forecasting results support the proposed framework.

Forecasting tail risk measures for financial time series: An extreme value approach with covariates

Forecasting tail risk measures for financial time series: An extreme value approach with covariates

Changes to the Fund’s Financing Assurances Policy in the Context Of Fund Upper Credit Tranche (UCT) Financing Under Exceptionally High Uncertainty

The Executive Board of the International Monetary Fund (IMF) approved changes to the Fund’s financing assurances policy. The changes apply in situations of exceptionally high uncertainty, involving exogenous shocks that are beyond the control of country authorities and the reach of their economic policies, and which generate larger than usual tail risks. The changes adopted could enable the design of a Fund Upper Credit Tranche (UCT) program in situations of exceptionally high uncertainty, in particular by modifying the Fund’s financing assurances policies in two ways. The first change allows official bilateral creditors to provide an upfront credible assurance about delivering debt relief and/or financing with the delivery of a contingent second-stage element of debt relief and/or financing once the exceptionally high uncertainty has been resolved. This would help establish that medium-term viability is being restored. The second change extends the use of a capacity-to-repay assurances from official bilateral creditors/donors from emergency financing to a UCT arrangement context. This would help establish adequate safeguards. These changes and their application to any specific country case in a situation of exceptionally high uncertainty would require the Fund to weigh whether it is prepared to accept the enterprise risks that such arrangement would entail.

Empirical tail risk management with model-based annealing random search

Tail risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) are popularly accepted criteria for financial risk management, but are usually difficult to optimize. Especially for VaR, it generally leads to a non-convex NP-hard problem which is computationally challenging. In this paper we propose the use of model-based annealing random search (MARS) method in tail risk optimization problems. The MARS, which is a gradient-free and flexible method, can widely be applied to solving many financial and insurance problems under mild mathematical conditions. We use a weather index insurance design problem with tail risk measures including VaR, CVaR and Entropic Value at Risk (EVaR) as the objective function to demonstrate the viability and effectiveness of MARS. We conduct an empirical analysis in which we use a set of weather variables to hedge against corn production losses in Illinois. Numerical results show that the proposed optimization scheme effectively helps corn producers to manage their tail risk.

Spillovers and connectedness between Chinese and ASEAN stock markets during bearish and bullish market statuses

PurposeThis study aims to examine the tail connectedness between the Chinese and Association of Southeast Asian Nations (ASEAN) stock markets. More specifically, the authors measure the return spillovers at three quantile levels: median (t = 0.5), lower extreme (t = 0.05) and upper extreme (t = 0.95). The connectedness at extreme upper and lower quantiles provides insightful information to investors regarding tail risk propagation, which ultimately suggests that investors adjust their portfolios according to the extreme bullish and bearish market conditions.Design/methodology/approachThe authors employ the quantile connectedness approach of Ando et al. (2022) to examine the quantile transmission mechanism among the ASEAN and Chinese stock markets.FindingsThe results show significant evidence of a higher level of connectedness between Chinese and ASEAN stock markets at extreme upper and lower quantiles compared to the median quantiles, which suggests the use of a quantile-based connectedness approach instead of an average-measure-based one. Furthermore, the time-varying connectedness analysis shows that the total spillovers reach the highest peaks during the global financial crisis, the Chinese stock market crash and the COVID-19 pandemic at the upper, lower and median quantiles. Finally, the static and dynamic pairwise spillovers between the Chinese and ASEAN markets vary over quantiles as well.Originality/valueThis study is the first attempt to examine quantile vector autoregression (VAR)-based return spillovers between China and ASEAN stock markets during different market statuses. Besides, the COVID-19 has intensified the uncertainty in Asian countries, mainly China and ASEAN economies.

The equity-oil hedge: A comparison between volatility and alternative risk frameworks

This paper investigates alternatives to the conventional minimum-variance framework for hedging equity risks with crude oil. Specifically, the optimal hedge ratios are calculated for various risk objectives such as volatility, semivariance, and tail risk. The hedging efficacy is assessed for BRIC stocks and aggregated market indices. The results show that the optimal hedge ratios differ by strategy, and hedging efficacy varies across risk metrics, equity indices, and time periods. Semivariance and tail risk hedging are more effective than volatility risk hedging, but more expensive. Oil exporters (Brazil and Russia) outperform importers (India and China) in terms of hedging effectiveness. Hedging costs are lower during COVID-19 than during the global financial crisis. It is critical to capture volatility dynamics for hedging volatility, downside, and tail risk. Over-hedging could significantly increase the risk of an equity-oil-hedged portfolio. The findings contribute to a better understanding of oil’s risk hedging capacity for equity indices, which will be of interest to financial market participants, corporations, and regulators.

What Can Volatility Smiles Tell Us About the Too Big to Fail Problem?

AbstractWe exploit the information content of option prices to construct a novel measure of bank tail risk. We document a persistent increase in tail risk for the U.S. banking industry following the global financial crisis, except for banks designated as systemically important by the Dodd–Frank Act. We show that this post-crisis difference in tail risk for large and small banks is consistent with the too-big-to-fail (TBTF) status of large banks being reinforced by the Dodd–Frank designation: Naming the banks whose failure could threaten the financial stability of the U.S. gave investors a list of banks the government deemed as TBTF.

The probability conflation: A reply to Tetlock et al.

We respond to Tetlock et al. (2022) showing (1) how expert judgment fails to reflect tail risk and (2) the lack of compatibility between forecasting tournaments and tail-risk assessment methods (such as extreme value theory). More importantly, we communicate a new result showing a greater gap between the properties of tail expectation and those of the corresponding probability.

Hedging cryptocurrency options

The cryptocurrency market is volatile, non-stationary and non-continuous. Together with liquid derivatives markets, this poses a unique opportunity to study risk management, especially the hedging of options, in a turbulent market. We study the hedge behaviour and effectiveness for the class of affine jump diffusion models and infinite activity Lévy processes. First, market data is calibrated to stochastic volatility inspired-implied volatility surfaces to price options. To cover a wide range of market dynamics, we generate Monte Carlo price paths using an stochastic volatility with correlated jumps model, a close-to-actual-market GARCH-filtered kernel density estimation as well as a historical backtest. In all three settings, options are dynamically hedged with Delta, Delta–Gamma, Delta–Vega and Minimum Variance strategies. Including a wide range of market models allows to understand the trade-off in the hedge performance between complete, but overly parsimonious models, and more complex, but incomplete models. The calibration results reveal a strong indication for stochastic volatility, low jump frequency and evidence of infinite activity. Short-dated options are less sensitive to volatility or Gamma hedges. For longer-dated options, tail risk is consistently reduced by multiple-instrument hedges, in particular by employing complete market models with stochastic volatility.

Left-tail momentum and tail properties of return distributions: A case of Korea

This study documents the importance of considering the cross-sectional differences in the tail properties of stocks' return distributions when analyzing the left-tail momentum (LTM) phenomenon. This phenomenon is verified in the Korean stock markets, which shows that stocks showing large losses in the past tend to continue to perform poorly in the future. However, when tail fatness (TF), measured using standardized return distributions, is considered, the LTM phenomenon is significant only in the low-TF stock group. This means that investors underestimate the persistence of left-tail risk only for stocks with a low frequency of large losses, and not for all stocks that show large losses. The results of the measurement of tail risk (TR) reaffirm the positive relationship with expected returns, which shows that the existence of LTM is verified only in the low-TR stock group, suggesting a need for caution in interpreting the LTM phenomenon with low TR as a market anomaly.

Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk

We study how foreign financial developments influence the conditional distribution of domestic GDP growth. We propose a method to account for foreign vulnerabilities using bilateral-exposure weights when assessing downside macroeconomic risks within quantile regressions. For an advanced-economy panel, we show that tighter foreign financial conditions and faster foreign credit-to-GDP growth are associated with a more severe left-tail of domestic GDP growth, even controlling for domestic indicators. Incorporating foreign variables improves estimates of domestic GDP-at-Risk, both in and out of sample. Decomposing GDP-at-Risk into domestic and foreign origins, we show that foreign shocks are a key driver of domestic macroeconomic tail risks.