Optimal Hedging Strategy Research Articles

On asset pricing in a binomial model with fixed and proportional transaction costs, portfolio constraints and dividends

AbstractWe extend the classical binomial model proposed by Cox, Ross, and Rubinstein for derivative security pricing to encompass both fixed and proportional transaction costs, portfolio constraints including margin requirements, and dividend-paying assets. Our focus is on studying option hedging within this enriched framework. Initially, we establish the existence of a hedging strategy in this context. Subsequently, we determine the optimal hedging strategy and its associated initial cost by decomposing the problem into a sequence of hedging problems. To illustrate our approach, we present a numerical example within a 3-period binomial model.

Dynamic optimal hedging with futures in portfolio context

In this paper, we investigate the optimal hedging strategy in a continuous time framework that is more adequate for commodities. We consider the consumption-investment problem where all asset prices follow mean-reverting jump-diffusion processes. The optimal investment and consumption strategies are derived in closed form. The framework is used to address one of the major risk factors faced by commodity producers. We show that a commodity producer will be better off hedging his/her futures contracts by simultaneously investing in foreign exchange products to minimize the adverse impacts of the jump risk prevalent in commodity prices.

Cashflow-driven investment beyond expectations

ABSTRACT This paper presents a computationally tractable optimization model for cashflow-driven investment where the aim is to find asset portfolios whose future payouts cover given liability payments as well as possible. While current industry solutions are largely based on expected future cash flows, we use a stochastic optimization model that seeks portfolios that give the best possible match across time as well as scenarios. Cashflow matching across scenarios is controlled by risk aversion while the timing is driven by illiquidity of money markets. When illiquidity increases, the hedging strategy quickly shifts towards portfolios suggested by deterministic models, but significant uncertainty remains. The model can incorporate hundreds of quoted instruments in the construction of optimal hedging strategies. The hedging strategies are able to employ any statistical connections between the liabilities and publicly quoted assets. The model is solved with simple Monte Carlo approximations and off-the-shelf convex optimization software. Besides optimal hedging strategies, we find the least cost of hedging which provides a market-consistent valuation based on the current quotes and the liquidity factors as well as the views and risk preferences of the investor/regulator. The approach is illustrated by pricing and hedging defined benefit pension liabilities which depend on uncertain longevity developments and the consumer price index. The hedging strategies are constructed from 128 publicly quoted instruments including index-linked bonds and equities. We find that the optimized hedging strategies achieve lower risk at a lower cost than the strategies obtained by matching expected cashflows. The hedging-based liability valuations are robust with respect to model parameters and the additional risk reduction achieved by optimization does not add much to the overall hedging cost.

Employment of the DCC-GARCH Copula Model to explore a link between robotics and artificial intelligence and green crypto investments.

The pursuit of the Sustainable Development Goals (SDGs) requires considerable new green crypto investments. To attract the flow of this investment, it is necessary to develop and apply robotic artificial intelligence (AI) as it has the potential to encourage the adoption of environmental innovation and increase individuals' environmental awareness. Our research employs the DCC-GARCH Copula Model to examine time-varying spillovers and prove interlinkages between the development of AI and green cryptocurrencies in the period from January 1, 2018, to September 8, 2023. Comparing the optimum hedge ratios with the optimal portfolio weights, we demonstrate that the optimal hedge strategy for BOTZ is the most successful one. However, the success of hedging depends on the portfolio's risk profile. Based on our analysis of the cumulative profit profile of different approaches, we continue to believe that the best portfolio weighting strategy is the one that produces positive returns in the middle of 2020 and the first part of 2022 and 2023. This demonstrates that the most profitable diversification approach is not always the most successful one. Our results have important policy implications for investors and governments.

Финансовое заражение российского фондового рынка от европейского в период пандемии COVID-19

This paper investigates the transmission of financial contagion from the European to the Russian stock market during the COVID-19 pandemic. Financial contagion refers to the spread of instability and shocks across individual countries, sectors, or markets during a crisis, where the relationship between returns and volatility of different assets goes beyond normal interactions. Using the construction of extended autoregressive models, we test the contagion of the RTSI composite index from the EURO STOXX 50 index, with the US dollar exchange rate and the spot price of Urals crude oil serving as control variables. The calculation of the moving coefficient of variation in assets prices allows us to distinguish the pre-crisis, crisis and post-crisis periods, for which three separate autoregressive models are built. The contagion of the Russian stock market from the European stock market in these models is identified in two ways: 1) based on the growth and significance of estimated coefficient for the tested variable (STOXX 50 index return) during the crisis; 2) through an increase in the contribution of the tested variable to the explained variance of the dependent variable (RTSI return). In addition, we tested contagion based on the method of central co-moments of the distribution of returns, skewness and volatility of the tested and dependent variable. The analysis has convincingly demonstrated the existence of a financial contagion effect from the European stock market to the Russian stock market in the short term — strengthening of the impact of the European STOXX 50 index on the Russian RTSI index in the acute phase of the pandemic. Understanding the factors contributing to the spread of market shocks in the context of financial globalization can help policymakers to implement effective financial regulatory measures and maintain long-term financial stability in line with national interests. For investors, it helps to identify potential risks and opportunities, enabling optimal hedging and diversification response strategies.

Dynamic connectedness, spillover, and optimal hedging strategy among FinTech, Sukuk, and Islamic equity markets

The paper investigates the dynamic connectedness, spillover, and optimal hedging strategy of the FinTech, Sukuk, and Islamic equity markets. The CAViaR approach, TVP-VAR Frequency connectedness, and network connectedness approaches are used. We also apply the MVP, MCP, and MCoP approaches to construct portfolios based on the Fintech, Sukuk, and Islamic equity indices. We then compute hedge effectiveness, portfolio performance, and Sharpe ratios. We find that FinTech markets are predominantly intra-connected in the short and medium term; they are significantly interconnected in the long term. In terms of frequency decomposition, volatility transmission among the variables dominates in the short term in comparison with other examined durations. The Russian-Ukrainian War and COVID-19 pandemic both saw a considerable increase in the intensity of volatility transmission. There is higher information transmission from Islamic equity indices and less effective information transmission from Sukuk and Fintech indices. Our findings suggest that the dynamic portfolio weights for MCP and MCoP seem to be similar, but clearly differs from MVP approach. When delving into the existence of Fintech indices as a part of an international fixed-income Islamic investor's portfolio, our paper's findings – being robust to the portfolio construction method – establish that Fintech indices do have a notable role in fixed-income Islamic investment portfolios. Finally, we conclude by highlighting the fact that the benefits of diversifying a portfolio with FinTech and Islamic assets fluctuate with time and on a regular basis.

Potential Welfare Gains from Optimal Macro Hedging for Oil Exporters

This paper computes the welfare gains from optimal hedging with futures contracts for an oil-exporting country. Unlike previous studies, this paper derives the welfare gains under a more realistic futures hedging model. This is accomplished by considering basis risk and by relaxing the full-hedging assumption. Furthermore, this is the first paper to derive the welfare gains under optimal hedging strategies. We also incorporate the empirical relationship between spot and futures prices within our models, rather than the theoretical relationship which most studies employ. The models were developed under a dynamic stochastic optimization framework and the optimal consumption and value functions were found using the method of Endogenous Gridpoints. The results showed that the choice of the optimal hedging strategy employed led to a slight improvement in the country’s welfare gains relative to full hedging. We also found that the strategies with the highest welfare gains were the most effective at volatility reduction. Finally, this paper provides compelling evidence for the use of optimal macro futures hedging as an effective risk management tool for oil-exporting developing countries.

Safe-Haven Currencies as Defensive Assets in Global Stocks Portfolios: A Reassessment of the Empirical Evidence (1999–2022)

This paper reassessed the hedging properties of four major safe-haven currencies (US dollar, Swiss franc, euro, yen) in international stock portfolios covering most representative world macroeconomic areas. The main contribution to the existing literature is the emphasis on optimal hedging and asset-allocation strategies. A further distinguishing feature is an accurate comparison, inside a multivariate framework, between value-at-risk simulations assuming equal or optimal asset weights in hedged global stock portfolios. The US dollar stands out as the best safe-haven currency, while adding the US currency to single-hedged global stock portfolios including either the Swiss franc or the euro yields smooth risk profiles during major financial crises, and average risk indicators lower than that of a benchmark fully hedged portfolio.

Robust Risk-Aware Option Hedging

The objectives of option hedging/trading extend beyond mere protection against downside risks, with a desire to seek gains also driving agent's strategies. In this study, we showcase the potential of robust risk-aware reinforcement learning (RL) in mitigating the risks associated with path-dependent financial derivatives. We accomplish this by leveraging a policy gradient approach that optimizes robust risk-aware performance criteria. We specifically apply this methodology to the hedging of barrier options, and highlight how the optimal hedging strategy undergoes distortions as the agent moves from being risk-averse to risk-seeking. As well as how the agent robustifies their strategy. We further investigate the performance of the hedge when the data generating process (DGP) varies from the training DGP, and demonstrate that the robust strategies outperform the non-robust ones.

A Deep Learning Approach to Data-Driven Model-Free Pricing and to Martingale Optimal Transport

We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds. In particular, our methodology allows to train a single neural network offline and then to use it online for the fast determination of model-free price bounds of a whole class of financial derivatives with current market data. We show the applicability of this approach and highlight its accuracy in several examples involving real market data. Further, we show how a neural network can be trained to solve martingale optimal transport problems involving fixed marginal distributions instead of financial market data.

No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging

Deep hedging is a versatile framework for computing the optimal hedging strategy of derivatives in incomplete markets. However, it is subject to the action-dependence problem impeding efficient training because the appropriate hedging action at the next step depends on the current action. To overcome this issue, the authors leverage a no-transaction band strategy, an existing technique that provides optimal hedging strategies for European options and exponential utility. The authors theoretically argue this strategy to be optimal for a wider class of utilities and derivatives, including exotics. Based on the result, the authors propose a no-transaction band network, namely, a neural network architecture that facilitates fast training and precise evaluation of the optimal hedging strategy. Moreover, the authors experimentally demonstrate that, for European and lookback options, their architecture rapidly attains a better hedging strategy compared with a standard feed-forward network. The findings thus have important implications for the practical applications of deep hedging.

A Review on Derivative Hedging Using Reinforcement Learning

Hedging is a common trading activity to manage the risk of engaging in transactions that involve derivatives such as options. Perfect and timely hedging, however, is an impossible task in the real market that characterizes discrete-time transactions with costs. Recent years have witnessed reinforcement learning (RL) in formulating optimal hedging strategies. Specifically, different RL algorithms have been applied to learn the optimal offsetting position based on market conditions, offering an automatic risk management solution that proposes optimal hedging strategies while catering to both market dynamics and restrictions. In this article, the author provides a comprehensive review of the use of RL techniques in hedging derivatives. In addition to highlighting the main streams of research, the author provides potential research directions on this exciting and emerging field.

Gamma and vega hedging using deep distributional reinforcement learning.

We show how reinforcement learning can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta-neutral at the end of each day by taking a position in the underlying asset. We focus on how trades in options can be used to manage gamma and vega. The option trades are subject to transaction costs. We consider three different objective functions. We reach conclusions on how the optimal hedging strategy depends on the trader's objective function, the level of transaction costs, and the maturity of the options used for hedging. We also investigate the robustness of the hedging strategy to the process assumed for the underlying asset.

Hedging with automatic liquidation and leverage selection on bitcoin futures

Bitcoin derivatives positions are maintained with a self-selected margin, which is often too low to avoid automatic liquidation by the exchange, without notice, especially during periods of excessive volatility. Indeed, according to CryptoQuant, almost $80 billion of positions on centralised exchanges were liquidated during 2021, that is an average of over $200 million per day. So hedgers of bitcoin price risk should account for the possibility of automatic liquidation when taking positions on bitcoin futures. We derive a semi-closed form for an optimal hedging strategy with dual objectives – to minimize both the variance of the hedged portfolio and the probability of liquidation due to insufficient collateral. The solution depends on the statistical characteristics of the spot and futures extreme returns, and other parameters that characterize the hedger by choice of leverage, loss aversion and collateral management. An empirical analysis based on minute-level data compares the performance of the major direct and inverse bitcoin hedging instruments traded on five major exchanges.

Dynamic connectedness and optimal hedging strategy among commodities and financial indices

This study looks at the best portfolio strategy for mitigating the risk associated with the MSCI ACWI & Frontier Markets Index, as well as the volatility spillovers between commodity markets and certain financial markets. Therefore, we empirically explore the connectedness among three financial indicators and five product groups using the framework of Diebold and Yilmaz (2012), which is based on a vector autoregressive process and variance decomposition of prediction errors, between 31 May 2002 and 30 July 2021. We also investigate the best hedging instrument(s) for the MSCI ACWI & Frontier Markets Global Index by combining the Asymmetric Dynamic Conditional Correlation (ADCC) model with the risk reduction index and the hedging ratios. Our empirical findings highlight the importance of volatility spillover effects across financial markets, which is not the case for commodity markets with low volatility externalities. Furthermore, the first markets appear to be net transmitters of volatility, whereas the second markets appear to be net receivers. Using the approach of Kroner and Sultan (1993), we show that the least risk portfolio is a portfolio that combines the MSCI ACWI & Frontier Markets Global Index with financial indices related to socially responsible and irresponsible investing.

Hedging IMO2020 compliant fuel price exposure using futures contracts

The IMO 2020 sulphur cap regulation, requiring shipping companies to reduce air pollution of their vessels, poses significant challenges for these companies in terms of bunker risk management. This paper examines for the first time the cross-hedging performance of several petroleum futures contracts which may be used to manage the new low sulphur bunker spot price exposure in the three major bunker hub ports of the world supplying such fuels. Using copula-GARCH models, among others we show that the most effective hedge for the price risk of the low sulphur marine gasoil, through the use of the corresponding futures contracts, is achieved in the port of Singapore in comparison to that achieved in the ports of Rotterdam and Houston. Petroleum futures in the middle distillates family (ultra-low sulphur diesel, and low sulphur gasoil) are shown to be more effective than other petroleum products in reducing return variability. The copula-based strategy performs only marginally better out-of-sample than the rest of the methods employed. The findings provide significant implications and can guide shipping companies' bunker risk management regarding the hedging decisions on bunkering port, the hedging instrument, and the optimal hedging strategy.

A STOCHASTIC OIL PRICE MODEL FOR OPTIMAL HEDGING AND RISK MANAGEMENT

In this paper, we develop a stochastic model for future monthly spot prices of the most important crude oils and refined products. The model is easy to calibrate to both historical data and views of a user even in the presence of negative prices which have been observed recently. This makes it particularly useful for risk management and design of optimal hedging strategies in incomplete market situations where perfect hedging may be impossible or prohibitively expensive to implement. We illustrate the model with optimization of hedging strategies for refinery margins in illiquid markets using a portfolio of 12 most liquid derivative contracts with 12 maturities traded on New York Mercantile Exchange (NYMEX) and Intercontinental Exchange (ICE).

Financial hedging in two-stage sustainable commodity supply chains

This paper studies the optimal financial hedging strategy and sustainable development in four supply chain structures. To help manufacturers reasonably hedge the risks caused by the fluctuation of input commodity prices, we use an index-based price contract by which manufacturers can transfer part of their risk to downstream retailers and realize risk-sharing in the supply chain. We discuss the optimal hedging strategy and the product's green degree in four structures: monopoly (manufacturer sets contract), monopoly with Nash bargaining, retailer competition, and manufacturer competition. Our model follows two-stage Stackelberg game, which delivers us the results that the hedge ratio is positively related to the consumer's preference for green products but is inversely related to the green product research and development costs. Interestingly, in the manufacturer competition supply chain, the profits of both manufacturers increase significantly when the input commodity price fluctuates slightly, but as that fluctuation continually increases, the profit of one manufacturer declines, while the profit of the other rises slowly. Considering another perspective, green degree competition harms the profits of the manufacturers and the retailer, as well as decreasing the green degree.

On RVaR-based optimal partial hedging

AbstractThe main aim of this paper is to develop an optimal partial hedging strategy that minimises an investor’s shortfall subject to an initial wealth constraint. The risk criterion we employ is a robust tail risk measure called Range Value-at-Risk (RVaR) which belongs to a wider class of distortion risk measures and contains the well-known measures VaR and CVaR as important limiting cases. Explicit forms of such RVaR-based optimal hedging strategies are derived. In addition, we provide a numerical example to demonstrate how to apply this more comprehensive methodology of partial hedging in the area of mixed finance/insurance contracts in the market with long-range dependence.

FARMER’S INCOME RISK AND RISK MANAGEMENT BY CROSS-HEDGING: A NOTE

The purpose of this study is to provide theoretical insights into the optimal hedging strategies in farmers contracts usage. We study the hedging decisions of a risk-averse farmer. The farmer faces multiple sources of price uncertainty. Cross-hedging is plausible in that one of these two commodities has a futures market. We show that the farmer’s optimal futures market position is a fullhedge, an over-hedge, or an under-hedge, depending on whether the two random prices are strongly positively correlated, uncorrelated, or negatively correlated, respectively.