Dynamic Hedging Research Articles

Exploring the sources of systemic risk and trading strategies in energy and stock markets

This study aims to investigate the impact of the energy transition on asset pricing by analyzing the different sources of systemic risk in a comprehensive system of one traditional energy ETF, one clean energy ETF, seven oil-exporting country ETFs, and six oil-importing country ETFs. We find that shocks to the traditional (clean) energy market are the primary source of short-term (long-term) systemic risk, suggesting that oil-dependent countries have started to incorporate the long-term risks associated with clean energy into the pricing of related assets. Meanwhile, we document both homogeneity within and heterogeneity between oil exporters and oil importers in the transmission patterns of systemic risk. These findings can help provide customized trading strategies for investors with diverse profiles. Additionally, we compare the regime-dependent hedging strategy with both the regime-dependent diversification strategy and the dynamic hedging strategy, demonstrating that it is the most effective for investors aiming to minimize portfolio volatility. Collectively, our results provide valuable insights that can assist policymakers, portfolio managers, and investors in adapting to the evolving dynamics of the energy transition.

Beyond the glitter: An empirical assessment of the true risk and hedging role of precious metals

Precious metals are an essential component of asset class within the global financial market offering diverse benefits. Being significant store of value, they are immune to default risk, provide hedge against inflation and are deemed to be safe haven. This highlights the importance of precious metals in the broader context of financial and economic stability. This paper focuses on the S&P Precious Metal Index (PMI) to explore its resilience towards financial market risks and examines if it could offer effective hedge during crises. Using Markov Regime Switching model, we find significant volatility persistence in PMI returns undermining its suitability for dynamic hedging. However, absence of speculative trends makes the PMI a safe asset. Further, financial market stress, as revealed by estimated Component GARCH and Discrete Threshold Models, does not affect the unique and market risk of investing in PMI. Although we could not establish PMI as a strong safe haven or a zero-beta asset, its potential to be included in a risk-avert investor's portfolio cannot be negated. Its consistently low and stress-invariant time-varying beta safeguards wealth against losses. The role of PMI becomes particularly salient while considering portfolio optimization during crises. In recent years, particularly during and after the pandemic the hedging effectiveness of PMI has increased. There is, however, a point of concern. The market risk of PMI is explained mostly by its lagged values rather than exogenous factors like market stress. Given the endogenous nature of market risks, chaotic dynamics might be in operation making forecasting and regulating PMI prices difficult.

Exploring the Effectiveness of Hedging in the Indian Commodity Market: A Comparative Analysis of Constant and Dynamic Hedge Ratios Across Agricultural and Non-Agricultural Commodities

Exploring the Effectiveness of Hedging in the Indian Commodity Market: A Comparative Analysis of Constant and Dynamic Hedge Ratios Across Agricultural and Non-Agricultural Commodities

Research on Hedging Ratio of Stock Index Futures to ETF Fund

Exchange Traded Funds (ETF) can largely avoid non-systematic risk, but investors often cannot avoid systemic risk, and the hedging function of stock index futures can do this. Therefore, hedging ETF with stock index futures has become a good investment strategy. Based on the trading data of the China Securities Index (CSI) 300 stock index futures and CSI 300 Exchange-Traded Fund (ETF), this paper analyzes the optimal hedging ratio of CSI 300 stock index futures by Ordinary Least Squares (OLS) and dynamic Error Correction Model - Generalized Autoregressive Conditional Heteroskedasticity (ECM-GARCH) model and compares the hedging performance predicted by the model to avoid the systematic risk of ETF. The results show that the hedging effect of the dynamic hedging model is better than that of the static model, and the ability to avoid systemic risk is also better. The predictions of both models show that stock index futures hedge ETFs very well. The dynamic model is more able to reduce the heteroscedasticity of the transaction data.

Integrating Advanced Technologies in Financial Risk Management: A Comprehensive Analysis

This paper delves into the pivotal role of advanced technologies in enhancing financial risk management across various domains, including credit risk, market risk, operational risk, and liquidity risk. It meticulously explores the application of machine learning (ML) algorithms and artificial intelligence (AI) in developing sophisticated risk assessment models, portfolio diversification strategies, and regulatory compliance mechanisms, which collectively surpass traditional methodologies in accuracy, efficiency, and predictive power. Through a detailed examination of enhanced Value at Risk (VaR) models, dynamic hedging strategies, and the impact of geopolitical events on market risk, alongside innovative approaches to operational risk mitigation and liquidity planning, this study underscores the transformative potential of technological advancements in financial risk management. It highlights how these technologies facilitate real-time analysis, predictive modeling, and strategic planning, significantly contributing to the resilience and stability of financial institutions in the face of evolving risks and regulatory requirements.

Integrating Advanced Technologies in Financial Risk Management: A Comprehensive Analysis

This paper delves into the pivotal role of advanced technologies in enhancing financial risk management across various domains, including credit risk, market risk, operational risk, and liquidity risk. It meticulously explores the application of machine learning (ML) algorithms and artificial intelligence (AI) in developing sophisticated risk assessment models, portfolio diversification strategies, and regulatory compliance mechanisms, which collectively surpass traditional methodologies in accuracy, efficiency, and predictive power. Through a detailed examination of enhanced Value at Risk (VaR) models, dynamic hedging strategies, and the impact of geopolitical events on market risk, alongside innovative approaches to operational risk mitigation and liquidity planning, this study underscores the transformative potential of technological advancements in financial risk management. It highlights how these technologies facilitate real-time analysis, predictive modeling, and strategic planning, significantly contributing to the resilience and stability of financial institutions in the face of evolving risks and regulatory requirements.

Exploiting the sentiments: A simple approach for improving cross hedging effectiveness

This paper introduces a novel hedging strategy based on textual information for cross-hedging. The strategy allows the optimal hedge ratio to vary with the sentiment extracted from online oil news, leading to an improvement in cross-hedging effectiveness. We find that the sentiment hedging strategy outperforms traditional time-varying and static hedging strategies in both in-sample and out-of-sample scenarios, with statistical and economic significance. This superior performance benefits from the well-documented predictive power of sentiment on futures volatility. Furthermore, we confirm an asymmetric effect of sentiment on hedging effectiveness, with negative sentiment having a greater impact on hedge ratios. Finally, extreme sentiment can trade off the negative relationship between volatile hedge ratios and hedging performance, potentially leading to superior hedging performance compared to normal sentiment, which is robust when considering transaction costs. Our findings highlight the importance of sentiment for estimating the optimal hedge ratio and should be considered for risk management.

Can gold-backed cryptocurrencies have dynamic hedging and safe-haven abilities against DeFi and NFT assets?

Given that the interconnections of NFT and DeFi digital assets with other stablecoins still not sufficiently studied, this paper is two-fold. We first examine the dynamic conditional correlation between gold-backed cryptocurrencies and NFTs and DeFi assets during the period 02/11/2021-05/01/2023. We thereafter assess the diversification potential of gold-backed cryptocurrencies against NFTand DeFi. To this end, we use the time-varying Student's copula to investigate the cross-markets linkages among different assets in dynamic fashion. We afterwards compute the optimal hedging ratios and effectiveness index to better explore the effectiveness of portfolio risk management. Our findings clearly show that the degree of dependence between gold-backed cryptocurrencies and NFT and DeFi tokens tends to vary over time. Our results also display that gold-backed cryptocurrencies act as suitable hedging (or diversifying) assets during normal times. Nevertheless, such assets can be considered as robust safe havens during the 2022 bear market for the most of NFT and DeFi assets. More specifically, PAXG and PMGT are found to be the best safe haven instruments for both NFT and DeFi tokens. DGX also serves as safe-haven assets for some NFT and DeFi assets, but with a lower risk-mitigation capacity compared to PAXG and PMGT. In most cases, DGX tends to act as a strong diversifier. Its hedging feature is only recorded for the NFT Protocol (xNFT) and the DeFi token Chainlink (LINK). Our results are of particular interest to investors and portfolio managers who search for safe havens to mitigate the risk of their NFT and DeFi portfolios.

Interplay of crises: Unpacking intraday spillovers in oil and European equities in the shadow of the COVID-19 and the Ukraine-Russia war

This study investigates the time-varying frequency of spillovers between European stock markets and oil during the COVID-19 pandemic and the Russia-Ukraine war. Using the spillover index by Diebold & Yilmaz, 2012 and Baruník & Křehlík, 2018, we analyze high-frequency data at a 5-min interval to analyze the interplay between crude oil market returns and the Stoxx 600 index returns, including sectors such as auto, basic material, banks, chemicals, food and beverage, health, industrials, insurance, oil and gas, retail, real estate, technology (tech), telecommunication (telecom), and utilities. The sample period is January 3, 2022, to March 25, 2022. Our findings reveal a substantial degree of connectedness within this financial network, with specific sectors—auto, chemicals, food, industrials, insurance, real estate, retail, tech, and telecom—acting as net transmitters of shocks, while other sectors assume the role of net receivers. The Russia-Ukraine war is a significant driver of these interconnected dynamics. Spillovers are most prevalent in the medium-term horizon, with the time dimension affecting the role of sectors and oil. Furthermore, our research highlights the potential benefits of adding oil assets to portfolios, enhancing risk-adjusted performance. However, ongoing risk management remains crucial due to the dynamic hedge ratios. This study offers practical insights for investors and policy makers, aiding risk management and investment strategies in turbulent markets.

Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic?

Our study analyzes the dynamic spillover effects between the NFT market, Bitcoin market, oil market, gold futures market, S&P 500 stock index, bond market, and US dollar index over three time periods: the full sample period (2017/6/23–2022/9/2), before the COVID-19 outbreak (2017/6/23–2020/1/12), and after the COVID-19 outbreak (2020/1/13–2022/9/2) by employing the DCC-GARCH-based connectedness model. Furthermore, we apply the DCC-GARCH-t-Copula model, Risk Parity Portfolio (RPP) model, and Minimum Connectedness Portfolio (MCoP) model to evaluate the bilateral dynamic hedge ratios, portfolio weights, and the multivariate portfolio performance of these financial assets, respectively. Our findings suggest that NFTs are a source of volatility spillovers during both periods and that the COVID-19 outbreak accelerates the speed and intensity of volatility spillovers from NFTs to traditional financial markets. Moreover, most of the volatility spillovers from NFTs are caused by own shocks, which implies that NFTs are not susceptible to financial risk contagion. The results of bilateral portfolio analysis show that NFTs can effectively hedge other traditional financial assets when used as long positions. The results of multivariate portfolio analysis demonstrate that NFTs can also reduce investment risk despite their smaller weighting. In the two months after the COVID-19 outbreak, MCoP slightly outperforms RPP, although RPP outperforms MCoP in general.

Optimal hedging efficiency in global freight markets: Comparing FFAs and time charter strategies

The paper aims to mitigate financial risk in highly volatile shipping freight markets by employing a dynamic hedging model. The primary criterion for evaluating the effectiveness of various methods for estimating optimal hedge ratios through Forward Freight Agreements (FFAs) is the minimum variance hedging rule. Four different methods are utilized to estimate two types of hedge ratios. The first type, a static hedge ratio, is calculated using the OLS and ECM methods. The second type, a time-varying hedge ratio, is determined through a bivariate GARCH model and a Rolling Window OLS method. Additionally, the hedging effectiveness of the traditional method of hedging, time chartering, is compared to the more modern and sophisticated shipping derivatives methods using the coefficient of variation. Highlights Estimating optimal hedge ratios through Forward Freight Agreements (FFAs) with the minimum variance hedging rule Four different methods are utilized to estimate two types of hedge ratios for static and dynamic hedging Assess the hedging effectiveness of the different methods of hedge ration estimation Compare the hedging effectiveness of the traditional method of hedging, time chartering, to the more modern and sophisticated shipping derivatives methods using the coefficient of variation

Hedging investment-grade and high-yield bonds with credit VIX

The existing literature lacks evidence of the ability of newly introduced credit VIXs to hedge investment-grade and high-yield corporate bond indices. A dynamic hedging analysis shows that credit VIX is an effective hedge for its corresponding bond index, but the hedging ability of high-yield credit VIX for high-yield bonds is more stable and effective than that of investment-grade credit VIX for investment-grade bonds. A regression analysis indicates that conditional correlations decreased during the Fed tightening cycle, resulting in more hedging effectiveness for both credit VIXs; whereas, the hedging effectiveness of high-yield bonds improved around the peak of the COVID-19 outbreak.

Are Chinese live hog futures useful hedging tools?

ABSTRACT China’s newly listed live hog futures provide a standardized risk management tool for the hog market. Our paper provides the first systematic and quantitative evaluation of live hog futures’ hedging ability based on various traditional static and dynamic hedging models and an innovative quantile hedging model. Consistent results confirm that live hog futures are useful hedging tools. Specifically, the in-sample results of multiple traditional models show that the hedging ratios are around 0.4 and hedging effectiveness is 0.13. Out-of-sample hedging effectiveness increases significantly to 0.22. Quantile hedging results show more significant risk-management ability in bearish and average futures markets. We further compare live hog futures’ hedging effectiveness and market performance to other commodities. The comparison shows that the live hog futures market has modest hedging effectiveness and underperforms in liquidity, volatility, and basis. Following the empirical results, it is recommended to encourage hedging transactions, simplify trading processes, and enable night trading to enhance liquidity, lower volatility, and eventually improve hedging ability.

Risk management through financial hedging in inventory systems with stochastic price processes

We consider the financial hedging problem of a firm whose operational cash flow from its inventory operation is affected by both price and demand uncertainties. We assume that selling prices and demand arrival process are governed by an exogenous continuous stochastic price process which is assumed to be correlated with prices of various products in financial markets. During the selling horizon, the firm dynamically invests in a financial portfolio of these products to manage its exposure to price and demand risks by observing the current inventory and price levels. We explore the problem in a minimum-variance framework where we look for the variance-minimizing financial hedge for a given operational policy and a martingale price process. The framework leads to explicit results for the optimal static and dynamic financial hedges in single-period problems with complicated within-period dynamics. We also obtain characterizations of optimal dynamic hedges for multi-period problems using dynamic programming. We explore the risk reduction effects of minimum-variance financial hedges through numerical examples and show that significant risk reductions may be possible by using the right hedge.

Static hedging of vanilla and exotic options in a South African context

In this paper, we test the performance of a static hedging strategy for a long-dated European call option and European spread call option in South Africa. The stochastic volatility double jump (SVJJ) model is calibrated to historical FTSE/JSE Top40 returns to generate real-world FTSE/JSE Top40 prices at future dates. The SVJJ model is also calibrated to the FTSE/JSE (Top40) implied volatility surface in order to value the options under the risk-neutral measure. Two static hedging programs are then implemented to test their effectiveness when replicating a long-dated European call option and European spread call option. Our results indicate that static hedging is a simple, yet effective, solution when hedging non-exchange-traded options with vanilla exchange-traded options.

Risk Management in Derivatives Markets: Integrating Advanced Hedging Strategies with Empirical Analysis

This study provides a comprehensive analysis of hedging strategies in the Chinese 50ETF options market, emphasizing dynamic hedging, Value at Risk (VaR), and machine learning-based approaches. Each strategy is meticulously evaluated against various market conditions, revealing distinct strengths and weaknesses. Dynamic hedging proves cost-effective in stable markets but struggles in high volatility scenarios, limiting its risk reduction capacity. Conversely, the VaR model, while reducing risk effectively under extreme conditions, may lead to over-hedging in calmer markets due to high costs and dependence on historical data. Machine learning strategies excel in adapting to complex and nonlinear market dynamics, though their effectiveness is contingent on data accuracy and model robustness. The study highlights the criticality of aligning strategies with market specifics and investor risk profiles, advocating for adaptable risk management in the fluctuating realm of financial derivatives. It provides empirical insights into modern hedging methods, offering practical guidance for their application in dynamic financial markets.

Portfolio diversification and dynamic hedging among BRICS stock markets and cryptocurrencies

Portfolio diversification and dynamic hedging among BRICS stock markets and cryptocurrencies

Testing the Hedging Effectiveness of Gold Derivative Market in India

The purpose of this article is to estimate the hedging effectiveness of the Indian Gold Derivative Market from both the statistics of Static hedge methodology of Vector Error Correction Method and Time-Varying hedge methodology of DVEC-GARCH, covering the time span of 1st April 2005 till 31st March 2019, to account for any difference existence in both. The study is conducted to judge the performance of gold futures as a tool of risk management. The result reveals the fact that hedging effectiveness from both the methodologies are found to be 35%. The reason behind low hedging efficiency might be due to the fact that gold itself is used as a hedge instrument and always in demand by the Indian investors for varied reasons as well as its accessibility in various forms (Gold ETFs and Gold Bonds issued by Government). The hedgers’ participation may be less as compared to speculators with respect gold future hedging. The results of phase-wise analysis, that is, before crisis (2005–2007), the hedging efficiency comes out to be 26%, during the crisis (2008–2012) 38.7% and after the crisis (2013–2018) 40% from both the static and dynamic hedge ratio models. The results showed that with the outbreak of Global Financial crisis in 2007–2009 and European debt crisis in 2010–2012 and thereafter, gold futures are increasing use as a risk management tool, but still at a minimal level.

Optimal Static Hedging of Variable Annuities with Volatility-Dependent Fees

Variable annuities (VAs) and other long-term equity-linked insurance products are typically difficult to hedge in the incomplete markets. A state-dependent fee tied with market volatility for VAs is designed to contribute the risk-sharing mechanism between policyholders and insurers. Different from prior research, we discuss several aspects on a fair valuation, fee-rate determination and hedging with volatility-dependent fees from the perspective of a VA hedger. A method of efficient hedging strategy as a benchmark compared to other strategies is developed in the stochastic volatility setting. We illustrate this method in guaranteed minimum maturity benefits (GMMBs), but it is also applicable to other equity-linked insurance contracts.

Stochastic method of dynamic hedging applied to the high liquid asset markets

In this study, the problem of hedging the risk of price slumping for a given volume of high-liquid assets is considered. One such possible hedging strategy may be the management of a portfolio constituted by the futures contracts holding a short position with respect to the basic asset. Besides that, the amount of having been sold futures is to satisfy the restrictions on the admissible minimal and maximum volume at each instant of time. The goal of such portfolio management is the increase of the weighted average price of futures sales through the speculative transactions realized in the real-time regime. Moreover, as feedback in the process of such management realization, one may use the prices of being executed market bargains only. The efficiency of the proposed hedging scheme is demonstrated by its comparison with the financial results achieved by the static hedging in the process of their both practical realizations based on the historical market prices. In this paper by the “dynamic hedging” concept, one means the execution of speculative transactions with futures taking a short position to provide the additional value of basic asset taking a long position. This notion is introduced in counterweight to the “static hedging” concept when the futures quantity does not vary in time and which goal is to preserve the basic asset value only.