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The purpose of this paper is to determine and contrast the risk mitigating effectiveness from optimal multiproduct time-varying hedge ratios, applied to the margin of a cattle feedlot operation, over single commodity time-varying and naive hedge ratios.

A parsimonious regime-switching dynamic correlations (RSDC) model is estimated in two-stages, where the dynamic correlations among prices of numerous commodities vary proportionally between two different regimes/levels. This property simplifies estimation methods for a large number of parameters involved.

There is significant evidence that resulting simultaneous correlations among the prices (spot and futures) for each commodity attain different levels along the time-series. Second, for in and out-of-sample data there is a substantial reduction in the operation's margin variance provided from both multiproduct and single time-varying optimal hedge ratios over naive hedge ratios. Lastly, risk mitigation is attained at a lower cost given that average optimal multiproduct and single time-varying hedge ratios obtained for corn, feeder cattle and live cattle are significantly below the naive full hedge ratio.

The application studied is limited in that once a hedge position has been set at a particular period, it is not possible to modify or update at a subsequent period.

Agricultural producers, specifically cattle feeders, may profit from a tool using improved techniques to determine hedge ratios by considering a larger amount of up-to-date information. Moreover, these agents may apply hedge ratios significantly lower than one and thus mitigate risk at lower costs.

Feedlot operators will benefit from the potential implementation of this parsimonious RSDC model for their hedging operations, as it provides average optimal hedge ratios significantly lower than one and sizeable advantages in margin risk mitigation.

In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods.

The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness.

The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader’s seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index.

The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies.

First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic.

We consider the partial hedging of stochastic electricity load pattern with static forward strategies. We assume that the company under consideration maximizes the risk adjusted expected value of its electricity cash flows. First, we calculate an optimal hedge ratio and after that we use this hedge ratio to solve the optimal hedging time. Our results indicate, for instance that agents with high load volatility hedge later than agents that have low load volatility. Moreover, negative correlation between forwards and electricity load pattern postpones the hedging timing.

This paper aims to investigate the efficiency of different hedging strategies for an investor holding a portfolio of foreign currency bonds.

The simplest strategies of no hedge and fully hedged are compared with the more sophisticated strategies of the ordinary least squares (OLS) approach and the optimal hedge ratios found by the dynamic conditional correlation-generalised autoregressive conditional heteroskedasticity approach.

The sophisticated hedging strategies are found to be superior to the simple strategies because they lower the portfolio risk in domestic currency terms and improve the Sharpe ratios for multi-asset portfolios. The analyses also show that both the OLS and dynamic hedging strategies imply holding a limited carry position by being long in high-yielding currencies but short in low-yielding currencies.

The performance of multi-currency portfolios is examined using more realistic assumptions than in the previous literature, including a weekly frequency and a constraint of no short selling. Furthermore, carry trades are shown to be part of an optimal portfolio.

This present work aims at looking into whether or not introducing commodities in international equity portfolios helps reduce the market risk and if hedging is carried out with the same effectiveness across different regional stock markets.

The authors determine the optimal hedge ratios and hedging effectiveness of a number of commodity-hedged emerging and developed equity markets, using three versions of MGARCH model: DCC, ADCC and GO-GARCH. The authors also use a rolling window estimation procedure for the purpose of constructing out-of-sample one-step-ahead forecasts of dynamic conditional correlations and optimal hedge ratios.

Empirical results evince that commodities significantly display effective risk-reducing hedge instruments in short and long runs. The main finding is that commodities do not seem to hedge regional stock markets in the same way. They tend to provide evidence of a rather effective hedging regarding mainly the East European and Latin American stock markets.

The authors study whether commodities can hedge stock markets at regional context and if hedging effectiveness differ from one region to another.

A futures contract may rely upon physical delivery or cash settlement to liquidate open positions at the maturity date. Contract settlement specification has direct impacts on the behavior of the futures price, leading to different effects of liquidity risk on futures hedging. This chapter compares such effects under alternative settlement specifications with a simple analytical model of daily price change. Numerical simulation results demonstrate that capital constraint reduces hedging effectiveness and tends to produce a lower optimal hedge ratio. As the futures contract proceeds toward the maturity date, hedgers will take larger hedge position in order to achieve better hedging effectiveness. Finally, optimal hedge ratios are higher (resp. lower) under cash settlement for the bivariate normal (resp. lognormal) assumptions, whereas hedging effectiveness is almost always greater under cash settlement.

The purpose of this paper is to analyze the optimal hedging strategy of the oil-stock dependence structure.

The methodology consists to model the data over the daily period spanning from January 02, 2002 to May 19, 2016 by a various copula functions to better modeling the dependence between crude oil market and stock markets, and to use dependence coefficients and conditional variance to calculate optimal portfolio weights and optimal hedge ratios, and to suggest the best hedging strategy for oil-stock portfolio.

The findings show that the Gumbel copula is the best model for modeling the conditional dependence structure of the oil and stock markets in most cases. They also indicate that the best hedging strategy for oil price by stock market varies considerably over time, but this variation depends on both the index introduced and the model used. However, the conditional copula method with skewed student more effective than the other models to minimize the risk of oil-stock portfolio.

This research implication can be valuable for portfolio managers and individual investors who seek to make earnings by diversifying their portfolios. The findings of this study provide evidence of the importance of stock assets for making an optimal portfolio consisting of oil in the case of investments in oil and stock markets. This paper attempts to fill the voids in the literature on volatility among oil prices and stock markets in two important areas. First, it uses copulas to investigate the conditional dependence structure of the oil crude and stock markets in the oil exporting and importing countries. Second, it uses the dependence coefficients and conditional variance to calculate dynamic hedge ratios and risk-minimizing optimal portfolio weights for oil–stock.

This paper assesses the co-movement between Pax gold and six fiat currencies. It also investigates the optimal time-varying hedge ratios in order to examine the properties of Pax gold as a diversifier and hedge asset.

This paper examines the volatility spillover between Pax gold and fiat currencies using the framework of wavelet analysis, BEKK-GARCH models and Range DCC-GARCH. Moreover, this paper proposes to use the covariance and variance structure obtained from the new range DCC-GARCH framework to estimate the time-varying optimal hedge ratios, the optimal weighs and the hedging effectiveness.

Wavelet coherence method reveals that, at low frequency, large zone of co-movements appears for the pairs Pax gold/EUR, Pax gold/JPY and Pax gold/RUB. Further, the BEKK results show unidirectional (bidirectional) transmission effects between Pax gold and EUR, GBP, JPY and CNY (INR, RUB) fiat currencies. Moreover, the Range DCC results show that the Pax gold and the fiat currency returns are weakly correlated with low coefficients close to zero. Thus, Pax gold seems to serve as a safe haven asset against the systematic risk of fiat currency markets. In addition, the results of optimal weights show that rational investor should invest more in Pax gold and less in fiat currencies. Concerning the hedge ratios results, the findings reveal that the INR (JPY) fiat currency appears to be the most expensive (cheapest) hedge for the Pax-gold market. However, the JPY’s fiat currency appears to be the cheapest one. As for hedging effectiveness results, the authors found that hedging strategies including fiat currencies–Pax gold pairs are most likely to sharply decrease the portfolio’s risk.

A comprehensive understanding of the relationship between Pax Gold and fiat currencies is crucial for refining portfolio strategies involving cryptocurrencies. This research underscores the significance of grasping volatility transmissions between these currencies, providing valuable insights to guide investors in their decision-making processes. Moreover, it encourages further exploration into the interdependencies of digital currencies. Additionally, this study sheds light on effective contagion risk management, particularly during crises such as Covid-19 and the Russia–Ukraine conflict. It underscores the role of Pax Gold as a safe-haven asset and offers practical guidance for adjusting portfolios across various economic conditions. Ultimately, this research advances our comprehension of Pax Gold’s risk-return profile, positioning it as a potential hedge during periods of uncertainty, thereby contributing to the evolving literature on cryptocurrencies.

This study’s primary value lies in its pioneering empirical examination of the time-varying correlations and scale dependence between Pax Gold and fiat currencies. It goes beyond by determining optimal time-varying hedge ratios through the innovative Range-DCC-GARCH model, originally introduced by Molnár (2016) and distinguished by its incorporation of both low and high prices. Significantly, this analysis unfolds within the unique context of the Covid-19 pandemic and the Russian–Ukrainian conflict, marking a novel contribution to the field.

This study seeks to examine the effectiveness of controlling the currency risk for international diversified mixed‐asset portfolios via two different hedge instruments, currency forwards and currency options. So far, currency forward has been the most common hedge tool, which will be compared here with currency options to control the foreign currency exposure risk. In this regard, several hedging strategies are evaluated and compared with one another.

Owing to the highly skewed return distributions of options, the application of the traditional mean‐variance framework for portfolio optimization is doubtful. To account for this problem, a mean lower partial moment model is employed. An in‐the‐sample as well as an out‐of‐the sample context is used. With in‐sample analyses, a block bootstrap test has been used to statistically test the existence of any significant performance improvement. Following that, to investigate the consistency of the results, the out‐of‐sample evaluation has been checked. In addition, currency trends are also taken into account to test the time‐trend dependence of currency movements and, therefore, the relative potential gains of risk‐controlling strategies.

Results show that European put‐in‐the‐money options have the potential to substitute the optimally forward‐hedged portfolios. Considering the composition of the portfolio in using in‐the‐money options and forwards shows that using any of these hedge tools brings a much more diversified selection of stock and bond markets than no hedging strategy. The optimal option weights imply that a put‐in‐the‐money option strategy is more active than at‐the‐money or out‐of‐the‐money put options, which implies the dependency of put strategies on the level of strike price. A very interesting point is that, just by dedicating a very small part of the investment in options, the same amount of currency risk exposure can be hedged as when one uses the optimal forward hedging. In the out‐of‐sample study, the optimally forward‐hedged strategy generally presents a much better performance than any types of put policies.

The research shows the risk and return implications of different currency hedging strategies. The finding could be of interest for asset managers of internationally diversified portfolios.

Considering the findings in the out‐of‐sample perspective, the optimally forward‐hedged minimum risk portfolio dominates all other strategies, while, in the depreciation of the local currency, this, together with the forward‐hedged tangency portfolio selection, would characterize the dominant portfolio strategies.

Systematic mortality risk, i.e. the risk of unexpected changes in mortality and survival rates, can substantially impact a life insurers' risk and solvency situation. By using the “natural hedge” between life insurance and annuities, insurance companies have an effective tool for reducing their net‐exposure. The purpose of this paper is to analyze this risk management tool and to quantify its effectiveness in hedging against changes in mortality with respect to default risk measures.

To achieve this goal, the paper models the insurance company as a whole and takes into account the interaction between assets and liabilities. Systematic mortality risk is considered in two ways. First, systematic mortality risk is modeled using scenario analyses and, second, empirically observed changes in mortality rates for the last 10‐15 years are used.

The paper demonstrates that the consideration of both the asset and liability side is vital to obtain deeper insight into the impact of natural hedging on an insurer's risk situation and shows how to reach a desired safety level while simultaneously immunizing the portfolio against changes in mortality rates.

The paper contributes to the literature by considering the insurance company as a whole in a multi‐period setting and taking into account both, assets and liabilities, as well as their interaction. Furthermore, the paper shows how to obtain a desired safety level while simultaneously immunizing a portfolio against changes in default risk.