Hedging Positions Research Articles

Dynamic DeFi-G7 stock markets interactions and their potential role in diversifying and hedging strategies

AbstractThis study examines the link between stocks and decentralized finance (DeFi) in terms of returns and volatility. Major G7 exchange-traded funds (ETFs) and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment. Specifically, this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity (VAR-GARCH) model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation (DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair. The volatility spillover analysis proves a contagion effect occurred between different geographic markets, and even between markets of different natures and typologies, during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine. Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times. In addition, DeFi assets, except for the Bazaar (BZR) Protocol, can offer diversification gains when included in financial investment portfolios. These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies, especially during periods of financial crisis.

Pricing and Hedging Contingent Claims by Entropy Segmentation and Fenchel Duality

We present a new approach to the problem of characterizing and choosing equivalent martingale pricing measures for a contingent claim, in a finite-state incomplete market. This is the entropy segmentation method achieved by means of convex programming, thanks to which we divide the claim no-arbitrage prices interval into two halves, the buyer’s and the seller’s prices at successive entropy levels. Classical buyer’s and seller’s prices arise when the entropy level approaches 0. Next, we apply Fenchel duality to these primal programs to characterize the hedging positions, unifying in the same expression the cases of super (resp. sub) replication (arising when the entropy approaches 0) and partial replication (when entropy tends to its maximal value). We finally apply linear programming to our hedging problem to find in a price slice of the dual feasible set an optimal partial replicating portfolio with minimal CVaR. We apply our methodology to a cliquet style guarantee, using Heston’s dynamic with parameters calibrated on EUROSTOXX50 index quoted prices of European calls. This way prices and hedging positions take into account the volatility risk.

Chinese Crude Oil Futures and Sectoral Stocks: Copula-Based Dependence Structure and Connectedness

Abstract China launched its first crude oil futures on 18th March 2018, with the primary objective of introducing its own hedging instrument in China. Our study responds to this objective by investigating the dependence structure based on time-variant and time-invariant copula, connectedness in various market conditions, and hedging effectiveness of oil futures with eleven GICS sectors in China using daily returns data from 19th June 2019 to 24th February 2024. Findings suggest that time-varying copula is a best fit for all the GICS sectors in China except for the Energy. Similarly, the results of time-varying (TVP-VAR) connectedness indicate that the oil futures are the net receiver of shocks in total, short (1–5 days), and long-time (5 to infinity) spillover. The oil futures can better offset portfolio losses during the down market than normal and up markets. Lastly, based on TVP-VAR, the hedging ratio, optimum portfolio weights, and hedge effectiveness are calculated. We find a positive hedge ratio across various pairs of assets, which reduces the assets’ volatility. Moreover, hedge ratios and optimum portfolio weights are non-constant over time. Our results significantly contribute to the scant literature on Chinese crude oil futures and have implications for investors and policymakers.

Spatial natural hedging: a general framework with application to the mortality of U.S. states

It is well known that coupling life and death benefits within an insurance portfolio may be a beneficial longevity risk reduction technique, especially when policies are underwritten in the same geographical region. However, though desirable, the lack of available capacity of life insurance instruments in terms of underlying cohorts or duration of products underwritten within a given region can substantially constrain the use of natural hedging strategies for life insurance companies. That is why the primary objective of this paper is to investigate the implementation and effectiveness of natural hedging strategies when considering the geographical or spatial dimension. Starting from a well-known multi-population mortality model, we evaluate the relevance of natural hedging strategies and their susceptibility to basis risk resulting from age, period, and spatial effects. Our novel theoretical findings provide direct insights into specific and often complex positions necessary for optimal real-world hedging. In a practical numerical application predicated on U.S. mortality data, we demonstrate the situation of a U.S.-based insurance company capable of selling policies across different states. Though often unable to curtail product sales, an insurance company using our analytical tool can effectively, through marketing strategies, stimulate or destimulate sales to approach an optimal hedging position of an overall portfolio.

Comparative Analysis of Gold, Bitcoin and Gold-backed Cryptocurrencies as Safe Havens During Global Crises: A Focus on G7 Stock Market and Banking Sector Indices

In this article, we attempt to analyze and compare the safe-haven features of gold, Bitcoin and gold-backed cryptocurrency against the stock and banking indices of G7 countries during the outbreak of adverse events. To do so, we examine dynamic relationships between different assets and we compute optimal hedge ratios for different couples using the corrected Asymmetric Dynamic Conditional Correlation-Exponential Generalized Autoregressive Conditional Heteroscedasticity and corrected Asymmetric Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity models. We clearly show that gold and gold-backed cryptocurrency maintain higher weights in optimal portfolios compared to Bitcoin. We also report that shocks due to unexpected events increasingly affect dynamic correlations, asset weights and hedge ratios. This underscores the need for regular demand for rebalancing the hedge positions and effective risk management. We thereafter show that the relationship between Bitcoin (gold-backed cryptocurrency) and G7 indices is highly affected by the outbreak of COVID-19 pandemic. Such findings highlight the hedging and safe-haven features of different asset classes against stock markets. They could have insightful implications for investors who want to minimize investment risks and policymakers who are worried about the financial consequences of different unexpected events.

Hedging Efficiency of Energy Commodities between Indian and American Commodity Exchanges: Constant and Time-Varying Approaches

This research paper investigates the hedging efficiency between spot and future prices of crude oil and natural gas commodities in the energy category of the commodity market. The study focuses on two major commodity exchanges, the Multi Commodity Exchange of India (MCX) and the New York Mercantile Exchange (NYMEX), which serve as the global benchmarking commodity market. Various econometric models have been incorporated to measure constant and time-varying hedging efficiency, we analyze the potential of futures contracts in mitigating price risks in these markets. The results indicate significant and consistent hedge ratios for both crude oil and natural gas in both the MCX and NYMEX markets. Moreover, the MGARCH model, which assesses hedging that varies over time, demonstrates the ability to adjust hedging positions based on market changes. Johansen's co-integration test endorses the presence of enduring connections between spot and future prices in both exchanges. Traders and investors can effectively use futures contracts to mitigate price risks in energy commodity markets, ensuring more dependable financial outcomes amid price fluctuations. Additionally, policymakers can utilize these research findings to promote the adoption of futures contracts, make well-informed choices, manage risks effectively, and enhance the overall efficiency and stability of energy commodity markets.

Promoting financial stability of oil producers: Operational vs. financial hedging

This paper investigates the effects of operational hedging on commodity price risks. It explores a novel type of operational hedging, i.e., the natural operational hedge position between upstream crude oil production and downstream activities in the supply chain. Using hand-collected data from 293 unique oil-producing firms, we find that operational hedging is sufficiently effective in reducing firms’ exposure to oil-price risk. We also find an inverse relationship between operational and financial hedging, suggesting that they can substitute for each other.

Robust investment and hedging policy with limited commitment

This study examines the impacts of ambiguity aversion on corporate investment and risk management when human capital cannot be alienated. An entrepreneur is concerned about model misspecifications and seeks robust decisions. At optimality, the entrepreneur lowers the maximal debt capacity, invests less and underconsumes in response to ambiguity concerns. When the firm becomes more financially constrained, these distortions are higher. Finally, we quantitatively show that the hedging positions are nonmonotonic in ambiguity aversion because of the interaction between ambiguity and limited commitment frictions.

Cointegration between high base metals prices and backwardation: Getting ready for the metals super-cycle

This paper is devoted to test agents’ behavior in the markets of hard commodities by trying to distinguish between managing future price structures to hedge their positions and speculating in on prices. We do a triple analysis: cointegration on the time series, structural breaks over the full time series and panel data. The analysis of the full series and the identification of structural breaks allows us to discover the connection between high prices and the negative futures price structure (backwardation) in rising prices scenarios of tin, copper, aluminium, and zinc. Moreover, we obtain that the base metals full matrix (price and futures price structure) is cointegrated in our analysis that uses panel data methods. We believe that these results are important for agents in the markets, as commodity traders or brokers, to maximize profits in their hedging positions.

A Comparative Analysis of Hedging Determination for Three Alternative International Equity Index Futures

This study provides a comparative analysis of hedging determination for three alternative international equity index futures, namely FTSE 100, NIKKEI 225 and S&P 500 futures contracts. Both the conventional regression and the error correction modeling approaches are used to estimate the minimum-variance hedge ratios and to evaluate the hedging effectiveness. Comparisons of out-of-sample hedging performance reveal that the error correction model outperforms the conventional model, suggesting that the error correction model serves as a better hedging model than conventional model in a hedge using stock index. In addition, the effects of temporal aggregation on hedge ratio estimates and hedging effectiveness are evaluated. It is found that temporal aggregation plays an important role on sizing hedging positions and measuring hedge effectiveness.

Housing in the greater Paris area as an inflation hedge?

PurposeOver a long period, even low inflation has an impact on portfolio value and households’ purchasing power. In such a context, inflation hedging should remain an important issue for investors. In particular, long-term investors, who are concerned with the protection of their wealth, seek to hold effective hedging assets. This study aims to demonstrate that residential assets in “Grand Paris” are a hedge against inflation and particularly against its unexpected component.Design/methodology/approachIn this study, the physical residential markets in 127 communes in Paris and the Parisian first-ring suburbs are considered as potential asset classes. We simplified the analysis by clustering the 127 communes into five homogenous groups using ascending hierarchical classification (AHC). Then, we test the hedging ability of these groups within a mixed asset portfolios using both correlation and regression analysis.FindingsThis paper presents an analysis of the “Grand Paris” housing market and its inflation hedging ability with comparison to other financial asset classes. Results show that the five housing groups act as a highly positive hedge against unexpected inflation. Furthermore, cash and bonds seem to provide, respectively, a partial and an over hedge against unexpected inflation. Stocks act as a perverse hedge against unexpected inflation and provide no significant hedge against expected inflation. Also, indirect listed real estate demonstrates little correlation with inflation, which makes us reject its hedging ability contrary to physical residential real estate.Research limitations/implicationsThe inflation topic: although several researches exist that question the hedging property of real estate, very few concentrate on physical residential assets and to the best of the authors’ knowledge, this study is the only one that targets the “Grand Paris” area. Residential assets of the “Grand Paris” communes are confirmed to be a hedge against inflation and particularly against its unexpected component thanks to its capital appreciation rather than income one. Also, we show that the listed real estate in France (Sociétés d’Investissement Immobilier Cotée) does not provide the same hedging properties contrary to the US real estate investment trusts (REITs) who demonstrate this ability. Listed real estate could thus not be used interchangeably with housing to protect from inflation in the French market.Practical implicationsProtection of investors against inflation and in particular in the face of its return to France in 2022. Reassuring promoters and investors of the interest of residential investment projects in “Greater Paris” and of the potential that this holds.Social implicationsInflation takes a chunk out of the purchasing power of money and thereby erodes the real value of people’s finance. Investors and households who seek protection from inflation erosion should invest in direct housing, and in particular within areas that are experiencing an effective metropolization process.Originality/valueThe originality of the study is precisely relative to the geographical area studied. The latter has experienced favorable economic conditions for several years and offers interesting fundamentals to explore and exploit in investment strategies that prove capable of protecting against imminent inflation. The database is specific to this project and has been built through the compilation of several sources and with the support of BNP Paribas Real Estate.

Higher moments in the fundamental specification of electricity forward prices

An extended specification for estimating the risk premia necessary for the forward pricing of wholesale electricity is developed in order to respond to the increasing need for more precise risk management of hedging positions in practice. Using Taylor expansions, we provide new specifications for the electricity forward premium including its dependency on all four moments of the expected wholesale price density as well as the higher moments of the demand density including skewness and kurtosis. Overall we argue that previous models have been underspecified and that the extended formulation proposed in this analysis is robust and worthwhile.

Multidimensional risk spillovers among carbon, energy and nonferrous metals markets: Evidence from the quantile VAR network

Carbon market attracts much attention due to its unique statue of promoting the transformation and development of low-carbon economy. In this paper, we investigate the multidimensional risk spillover effects among carbon, energy and nonferrous metals markets, and also examine the portfolio diversification. The quantile VAR network framework and GARCHSK model are applied. We find that: (i)There are significant risk spillover effects among carbon, energy and nonferrous metal market with prominent dynamic characteristics, while the risk spillover under different dimensions shows apparent differences. (ii)According to the findings based on network structure features, the Coal market becomes the core market of carbon-energy-nonferrous system, and the position of carbon trading market is significantly weakened when the market is downturn. (iii) In their portfolios, the nonferrous metals assets are preferred over carbon and energy, investors should adjust the portfolio structure and hedge positions according to market conditions. These findings have important implications for investors to construct diversified portfolios and regulators to formulate risk regulation policies.

State-dependent hedge strategy for crude oil spot and futures markets

Relying on the hidden Markov model improved by the particle swarm optimization algorithm (PSO-HMM), we develop a dual-decision method to address the issue of state-dependent futures hedging. Our approach is attractive in two ways. First, it uses the PSO algorithm to overcome the shortcomings of the traditional algorithm, which can easily fall into the local optima to estimate parameters in a hidden Markov mode. Second, this paper proposes a new hedge position adjustment method based on the identified market states, instead of sticking to the hedge position calculated by the commonly used GARCH-type models to achieve a better trade-off between risk hedging and return acquisition. Specifically, we first improve the accuracy of parameter measurement and employ the PSO-HMM to identify two market states, bear and bull, and fully illustrate the rationality and effectiveness of the proposed model. Based on the market states identified, we then adjust the hedge ratio estimated by GARCH-type models and compare the hedging effects of no hedge, model-driven, and state-dependent strategies. Our empirical results show that the PSO-HMM method can improve the accuracy of state identification over the classical HMM. The market state-dependent hedging strategy has better performance than other strategies when it comes to the trade-off between the return and the risk of a hedged portfolio. Furthermore, robustness checks under different conditions confirm that the state-dependent hedging strategy outperforms the model-driven hedging and no hedge strategies. Thus, our research sheds new light on conventional hedging models.

Delta-Gamma-Like Hedging with Transaction Cost under Reinforcement Learning Technique

Option hedging is critical in financial risk management. The traditional methods to determine the hedging position require assumptions of a frictionless market and continuous hedging. In this article, these two assumptions are removed, and a hedging strategy based on the reinforcement learning technique is proposed. This new strategy maximizes the expectation of the present value of accounting and realized profits of the hedging portfolio while limiting the sensitivity of the hedging position to changes in the underlying asset. The performance of this method is tested on option trading data (from 2004 to 2020) for the Standard and Poor’s (S&P) 500, S&P 100, and Dow Jones Industrial Average.

Fractal Dimension Option Hedging Strategy Implementation During Turbulent Market Conditions in Developing and Developed Countries

A hedging strategy is designed to increase the likelihood of desired financial outcomes. Market speculators hedge investment positions if they are worth protecting against potential negative outcomes of turbulent market conditions and effective hedging implementation can reduce the impact severity on the underlying investment since these negative scenarios cannot be avoided. This paper provides a solution for investors to implement a trading strategy to effectively manage turbulent market conditions (such as the COVID pandemic) by implementing an investment trading approach. The investment strategy includes an index held by the investor (long position) and uses a fractal dimension indicator to warn when liquidity or sentiment changes are imminent within financial markets. When the threshold is breached at a predetermined level, the investor will take this observation as a change in liquidity in the market and a hedging position is undertaken. This sequence of events triggers the implementation of a hedging strategy by entering a buy put option position. The fractal indicator was found to be effective when applied to four of the six tested indices in terms of cumulative returns, but also in effect increased the risk taken by the investor for all six indices. The conclusion was made that where the outcome was similar for each economy type, both had a scenario where two out of the three economies outperformed the underlying index and had one index not outperforming the underlying index. This comparison was done to establish whether the hedging strategy had a more promising application to a developing or developed economy type. The fractal indicator was found to be effective when applied to four of the six tested indices in terms of cumulative returns, but also in effect increased the risk taken by the investor for all six indices.

The commodities/equities beta term-structure

This paper document that the commodities/equities beta is positive and hump shaped over shorter holding periods, but declines and becomes negative for longer, business cycle duration, holding periods. The shape of the beta can be understood as a result of common commodities/equities dependence on aggregate economic activity combined with the unique mean reverting component of commodities. Storable commodities behave like financial assets in the short-run, but investment induces mean-reversion that reduces the beta over longer holding periods. The beta is reconstructed theoretically using a novel combined storage and investment partial equilibrium model, and the hypothesis is explored empirically using a structural VAR model for the crude oil market. The findings suggest that a component of the positive hedging benefit of commodities in equity portfolios is due to commodity mean reversion, and that commodity wealth diversification is more effective over longer investment periods.

Searching for hedging and safe haven assets for Indian equity market – a comparison between gold, cryptocurrency and commodities

PurposeThis paper aims to evaluate the hedging and safe haven properties of gold, cryptocurrency and commodities against the Indian equity market.Design/methodology/approachFirst, the authors estimate the hedging and safe haven abilities of gold, cryptocurrency and commodities for the Indian stock market and further verify whether such properties vary across the broad stock market indices and over the different degrees of market volatility. Second, the authors use the multivariate GARCH framework to calculate the dynamic hedge ratios and hedging efficiencies to compare the hedging properties of the alternative asset classes. Third, the authors verify the robustness of the general findings during the recent crisis emanating from the outbreak of the COVID-19 pandemic.FindingsGold, cryptocurrency and most commodities have significant hedging abilities. Only natural gas, crude oil and aluminum, on the other hand, have safe haven property. Neither gold nor cryptocurrency qualifies as a safe haven asset. On the other hand, the financialization of the Indian commodities market provides a significant dividend to investors in terms of hedging and safe haven capabilities. The authors find the least negative hedge ratio and the highest positive hedging effectiveness for the stock-crude oil and stock-natural gas portfolios. The central observations of the paper remain immune to the COVID crisis.Originality/valueFocusing on the Indian equity market, the paper compares the diversification abilities of traditional assets like gold with those of the modern class of assets, including cryptocurrency and other commodities.

Cross-Hedging Portfolios in Emerging Stock Markets: Evidence for the LATIBEX Index

We consider alternative possibilities for hedging spot positions on the FTSE LATIBEX Index, the index of the only international market exclusively for Latin American firms that is denominated by the euro. Since there is not a futures market on the index, it is unclear whether a relatively successful hedge can be found. We explore the plausibility of employing futures on four stock market indices: EUROSTOXX 50, S&P500, BOVESPA, and IPC, and simulate the results that could be obtained by a hedge position based on either unconditional or conditional second order moments estimated from different asymmetric GARCH models. Several criteria for hedging effectiveness suggest that futures contracts on BOVESPA should be preferred, and that a salient reduction in risk can be achieved over the unhedged LATIBEX portfolio. The evidence in favor of a better performance of conditional moments is very clear, without significant differences among the alternative GARCH specifications.

Efficient valuation of variable annuity portfolios with dynamic programming

AbstractThe valuation of variable annuity portfolios presents major challenges for US life insurers. Recent studies propose machine learning and metamodeling techniques based on selecting a few representative guarantees. However, these methods face a critical trade‐off between speed and accuracy. In contrast, I propose a recursive dynamic programming approach and demonstrate its ability to value a large and highly heterogeneous variable annuity portfolio with a high degree of accuracy and within a few seconds—even under stochastic interest rates and volatility—since the heavy computational burden can be fully front‐loaded (in a one‐time effort at the guarantee's pricing stage). This makes the dynamic programming approach ideally suited for all variable annuity applications, including the computation of reserves and capital requirements and to determine the insurer's hedging position. Moreover, dynamic programming can naturally incorporate optimal policyholder behavior into the insurer's valuation.