Search

The number of short campaigns by hedge funds has dramatically increased over the last two decades. Nearly 80% of campaigns are undertaken by activist hedge funds, particularly those that employ hostile tactics in their long campaigns. Short campaigns are associated with negative abnormal returns of –7%, with aggregate valuation effects similar in magnitude to the gains from long activism campaigns. In contrast to long campaigns, public communication is a critical component of short campaigns. We do not find evidence that such communication is manipulative. Overall, our analysis highlights the importance of short campaigns for understanding the economic impact of activist hedge funds. [ABSTRACT FROM AUTHOR]

We identify Industry-Neutral Self-Financed Informed Trading (INSFIT) as stock trades financed by offsetting, equivalent dollar-denominated stock trades in the same industry. Approximately 37% of short-term mutual fund trading profits can be attributed to these trade pairs. Consistent with informed trading, INSFIT precedes unusually high media coverage for the underlying stocks. The trades underlying INSFIT are also larger as the release of stock-level news becomes more imminent. Both relative valuation and the hedging of industry exposure motivate INSFIT's industry neutrality. While INSFIT positively impacts fund performance, active fund managers who execute INSFIT more aggressively obtain smaller trading profits per execution. [ABSTRACT FROM AUTHOR]

We examine the predictability of stock returns using implied volatility spreads (VS) from individual (nonindex) options. VS can occur under simple no-arbitrage conditions for American options when volatility is time-varying, suggesting that the VS-return predictability could be an artifact of firms' sensitivities to aggregate volatility. Examining this empirically, we find that the predictability changes systematically with aggregate volatility and is positively related to the firms' sensitivities to volatility risk. The alpha generated by VS hedge portfolios can be explained by aggregate volatility risk factors. Our results cannot be explained by firm-specific informed trading, transaction costs, or liquidity. [ABSTRACT FROM AUTHOR]

We develop a dynamic incomplete-markets model of entrepreneurial firms and demonstrate the implications of preferences for liquidity to entrepreneur's interdependent consumption, portfolio allocation, hedging and financing decisions. The numerical results provide several important implications. Preferences for liquidity reduce hedging demand using risky assets. Besides, the existence of preferences for liquidity decreases the implied equity value and encourages the entrepreneur to issue more debt. Especially, the preferences for liquidity can overturn the risk-shifting incentives of a risk-averse entrepreneur. [ABSTRACT FROM AUTHOR]

In this article we will propose a novel, self-financing, dynamic and path dependent portfolio trading strategy which is based on the well known principle "sell high – buy low". Trading strategies are important also for the hedging problem selling/buying an option. The main problem of the writer of an option is how to invest the amount that she has received selling the option therefore the proposed trading strategy play an important role here. We will see that the hedging problem reduces to an optimization one and therefore the portfolio optimization and the hedging problem are closely related. We will also propose a deterministic portfolio selection method (i.e., without making any assumption-guess about the assets) and a notion of a deterministic fair price of an option. [ABSTRACT FROM AUTHOR]

The current research delves into the implications of exchange rate fluctuations on commodity trade between Pakistan and Japan, utilizing the nonlinear autoregressive distributed lag method. While prior studies have predominantly utilized the symmetric cointegration approach, the current study posits that the limited assumption of symmetry between the exchange rate and the trade flows could have hindered the empirical findings. The research investigates both the symmetric and asymmetric effects of exchange rate fluctuations on 102 Pakistani industries that import from Japan and 62 industries that export to Japan at the industry level from 1980 to 2020. The outcomes indicate that in nearly half of the importing and exporting industries that engage in trade with Japan, there is evidence of a significant impact of asymmetric exchange rate fluctuations on trade flows in both the short and long term. The study suggests that policymakers should consider the industry-specific impact of exchange rate fluctuations on trade flows and implement targeted policies accordingly. Particularly, industries that benefit from currency depreciation should be encouraged through export incentives, while industries negatively affected by currency volatility should be provided with hedging mechanisms and other forms of support to mitigate their losses. [ABSTRACT FROM AUTHOR]

Purpose: The objective of the study is to shed light on the notion of quality investing in the Indian stock market. The study also attempts to combine the value and quality metrics to test their ability to generate a higher risk-adjusted return. Design/methodology/approach: The paper employs asset pricing models to examine the excess risk-adjusted returns and panel regression model (random estimates) to determine the price of quality in the cross-section of Bombay Stock Exchange (BSE) listed stocks from 2003 to 2020. Findings: The results indicate that the quality-only strategy failed to produce substantial risk-adjusted returns in the Indian stock market. The returns to long/short hedging strategy quality-minus-junk (QMJ) are significantly positive with the majority of the returns attributable to the short leg of the stock portfolio. The findings further discovered that the explanatory effect of quality on prices is limited. In particular, a strategy that combines value and quality investing generated positive and significant alphas as well as a higher Sharpe ratio. Practical implications: The study provides investors and portfolio managers with valuable insights for navigating undervalued high-quality equities in the Indian stock market. Originality/value: This is the first research of its kind to examine the performance of quality (Q score indicator) combined with value investing in the Indian stock market. As majority of research have concentrated on developed economies, this study offers out-of-sample evidence to validate the strategy's success in an emerging market. [ABSTRACT FROM AUTHOR]

Small stocks' time‐varying spreads predict future return gap between small and large stocks. To optimally exploit such predictability, the investor captures current risk premium by purchasing at large spreads with substantially reduced turnover; uses an aim‐in‐front‐of‐the‐target approach to trade‐off between future risk premium and current transaction costs; and meets hedging demand at low costs. Strong interaction between transaction costs and return predictability leads to large losses from myopic trading. Greater variability of the spread is advantageous for investors who trade optimally but detrimental for investors who trade myopically. The spread‐based return predictability significantly increases the investment value of small stocks. [ABSTRACT FROM AUTHOR]

Motivated by the increasing demand for alternative assets that can contribute to reducing portfolio risk, this paper examines the volatility spillovers between collateralized loan obligations (CLOs) and various in-demand investment instruments, including equities, bonds, crude oil, commodities, gold, bitcoin, shipping and real estate. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) modification of the classical spillover approach, for the period from January 1, 2012, to August 31, 2023. The empirical findings show moderate levels of dynamic connectedness; albeit several external shocks strengthened the interconnection among the assets. Moreover, we compare the ability of CLOs for hedging, during the overall sample period and multiple subperiods, by estimating hedge ratios and optimal portfolio weights, in order to inform investors about feasible portfolio adjustments. Our results indicate that CLOs constitute an effective hedging tool, irrespective of the period covered, as the short position in their volatility provides high hedging effectiveness for investors holding long positions in the volatility of all the remaining assets. [ABSTRACT FROM AUTHOR]

Utilizing firms in the S&P 500, we study whether greater transparency in the reporting of other comprehensive income (OCI) items, as mandated by ASU 2011-05, resulted in a reduction in information asymmetry, a change in the value relevance of this information, or a change in hedging practice. Our results show that while transparent reporting reduced information asymmetry, firms that engage in cash flow hedging do have greater information asymmetry than their counterparts that do not hedge. We find evidence that investors penalize firm value for greater volatility of OCI relative to net income volatility when reported transparently. When permitted, managers were able to mitigate the negative impact by reporting OCI only in the Statement of Shareholders' Equity. We conclude that managers' concerns regarding potential confusion surrounding OCI volatility following more prominent reporting led to changes in hedging behavior. After transparent reporting, we find a reduced likelihood of foreign currency cash flow (FXCF) hedges and a reduced level of FXCF hedging among firms experiencing the greatest volatility of unrealized hedging gains and losses. [ABSTRACT FROM AUTHOR]

Reservoir operations are influenced by hydroclimatic variability, reservoir characteristics (i.e., size and purpose), policy regulation, as well as operators' experiences and justification. Data‐driven reservoir operation models based on long‐term historical records shed light on understanding reservoir operation rules and patterns. This study applies generic data‐driven reservoir operation models (GDROMs) developed for 452 data‐rich reservoirs with diversified operation purposes across the CONUS to explore typical operation rules and patterns. We find that the operating policies of any of these reservoirs can be modeled with a small number (1–8) of typical operation modules. The derived modules applied to different conditions of the 452 reservoirs can be categorized into five basic types, that is, constant release, inflow‐driven piecewise constant release, inflow‐driven linear release, storage‐driven piecewise constant release, and storage‐driven nonlinear (or piecewise linear) release. Additionally, a joint‐driven release module, constructed from these five basic types, has been identified. The analysis further shows the module application transition patterns featuring operation dynamics for reservoirs of different operation purposes, sizes, and locations. The typical module types can be used as "Lego" bricks to build operation models, especially for data‐scarce reservoirs. These module types and their application and transition conditions can inform Standard Operation Policy (SOP) and Hedging Policy (HP) with specific inflow, storage, and/or both conditions. Key Points: Five basic types of operation modules are categorized for 452 reservoirsSeasonal patterns for module application transition are identified for reservoirs with different sizes, operation purposes, and locationsThe basic types of modules and operation patterns inform reservoir operation modeling [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

PurposeAimDesign/Methodology/ApproachMain findingsPractical implicationsNovelty/ContributionTo ascertain how JSE-listed companies use derivatives to hedge.Determine whether JSE-listed companies apply established rationales for corporate hedging practices.This empirical study uses data from the Johannesburg Stock Exchange (JSE), South Africa, as a proxy for emerging markets.Binomial logistic regression, applied to the 200 largest non-financial firms (by market capitalization) on the JSE from 2005 to 2017, indicates that larger firms, higher leveraged firms, ones with better growth prospects, and less information asymmetry between directors and management are more likely to use derivatives to hedge.The findings suggest that some traditional determinants for corporate hedging practices apply in an emerging market context, but local conditions still remain an important consideration.By confirming the applicability of traditional hedging theories in an emerging market context, the study extends the theoretical understanding of corporate risk management. It supports the notion that established hedging rationales, such as reducing financial distress costs and addressing information asymmetry, are relevant across different economic contexts. [ABSTRACT FROM AUTHOR]

This study focused on the determinant of macroeconomic variables on the non-performing loans of banks in the sub-Saharan African region using the fixed effect panel regression method. Data from twenty-two SSA countries from 2010 to 2021 were used. The results from the study provide a general indication that the macroeconomic environment matters a lot for non-performing loans within the banking sectors of SSA countries. It is therefore recommended that banks in the SSA sub-region learn to develop adaptive mechanisms to address the effects of macroeconomic shocks on their loan policy. These measures may come in the form of well-diversified portfolio systems that will improve banks' hedging. [ABSTRACT FROM AUTHOR]

Central Asia and China enjoy a mutually beneficial, albeit hugely asymmetrical, relationship. Nevertheless, leaders in Central Asia have occasionally and selectively resisted a broadening and deepening of their relations with China. Framed by a 'hedging' foreign policy approach, this article suggests that the practice of hedging arises not just from structural and exogenous conditions but is also facilitated by domestic considerations peculiar to each Central Asian state. The claim here is not that leaders in Central Asia are effective or proficient hedgers; rather, that some are attempting to use hedging as part of their statecraft. The varying ability of leaders in Central Asia to hedge, manage, and negotiate their relations with China is often under-appreciated but deserves to be highlighted as an evolving approach. [ABSTRACT FROM AUTHOR]

Currently, the most liquidly traded options on the crypto underlying are the so-called inverse options. An inverse option contract is quoted and traded in the units of the underlying cryptocurrency. The main economic reason for the popularity of inverse contracts in the crypto exchanges (such as Deribit) is that inverse contracts enable traders to operate without maintaining fiat cash accounts. For the theoretical part, we show that inverse options are just regular vanilla options considered under the martingale measure using the forward of the underlying as the numéraire. This measure requires an adjustment to option delta. For the empirical part, we use Deribit options data of past five years to backtest delta-hedged option strategies. We introduce USD and Coin accounting of trading Profit&Loss (P&L) which is important for designing strategies in crypto options. We show empirically that USD and Coin accounting rules are equivalent when performance is measured is Coin and USD units, respectively. We establish that the risk-premia observed in options on Deribit is negative and significant so that strategies selling volatility are expected to generate positive risk-adjusted performance in the long-term. [ABSTRACT FROM AUTHOR]

In this article, an iterative deterministic policy gradient method for finding optimal strategies in the presence of market impact is introduced. The derivation of the policy gradient sheds light on a proper way of handling the market impact of trades in the context of reinforcement learning. Similar to many machine learning methods, the proposed deterministic policy gradient method is based on mini-batch stochastic gradient descent optimization. The method is demonstrated to consistently perform well for several different objectives and market dynamics when applied to the financial applications of order execution and option hedging. [ABSTRACT FROM AUTHOR]

This study investigates the extreme connectedness network among Green bonds, stocks and commodities and its implications for portfolio diversification during the turbulent periods. To this end, we implement the novel QVAR methodology by Ando, Greenwood-Nimmo, and Shin to study the connectedness at the median, extreme lower, and extreme upper quantiles. The results reveal dynamic spillovers among markets. Interestingly, static spillover effects are stronger for the extreme upper and lower quantiles than for the intermediate quantiles. Commodities are the major net transmitters, whereas Shanghai Stock Exchange green bond is the net receiver of shocks in the system. These results are supported by the findings of portfolio management analyses, which suggest that Green bonds and gold could function as potential diversification assets, serving as safe havens, particularly for the stock and Brent markets. Our study provides valuable insights for investors aiming to optimize their hedging strategies, guiding risk management, and portfolio diversification. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to estimating the minimum variance hedge ratio (MVHR) based on the wild bootstrap and evaluates the approach using a spectrum of conservative to aggressive alternative hedging strategies associated with the percentiles of the MVHR's bootstrap distribution. This approach is suggested to be more informative and effective relative to the conventional method of hedging solely based on a single-point estimate. Furthermore, the percentile-based MVHRs are robust to influential outliers, non-normality, and unknown forms of heteroskedasticity. The bootstrap percentile-based hedging strategies' effectiveness is compared with those from the naïve method and the asymmetric DCC-GARCH model for a range of financial assets and commodities. The bootstrap percentile-based hedging technique is identified to outperform its alternatives in terms of hedging effectiveness, downside risk, and return variability, suggesting its superiority to other methods in both the literature and in practice. [ABSTRACT FROM AUTHOR]

The accuracy and efficiency for computing option prices play very important in the financial risk management and hedging for the investors. In this paper, we for the first time develop a fast and accurate numerical method that combines Laplace transform for time variable and compact difference for spatial discretization, for computing option prices governed by the partial integro-differential equation system under the regime-switching jump-diffusion models. We then invert the Laplace transform through the numerical contour integral to recover the option prices. Furthermore, we prove that the method is convergent in the discrete L 2 and L ∞ norms with fourth-order in space and exponential-order with respect to the quadrature nodes for the numerical Laplace inversion. Finally, several numerical examples are reported to illustrate the convergence theory and show the advantages of the method over the benchmarks in regards to the accuracy and efficiency. [ABSTRACT FROM AUTHOR]

If a financial asset's price movement impacts a firm's product demand, the firm can respond to the impact by adjusting its operational decisions. For example, in the automotive industry, automakers decrease the selling prices of fuel-inefficient cars when the oil price rises. Meanwhile, the firm can implement a risk-hedging strategy using the financial asset jointly with its operational decisions. Motivated by this, we develop and solve a general risk-management model integrating risk hedging into a price-setting newsvendor. The optimal hedging strategy is calculated analytically, which leads to an explicit objective function for optimizing price and "virtual production quantity" (VPQ). (The latter determines the service level—that is, the demand-fulfillment probability.) We find that hedging generally reduces the optimal price when the firm sets the target mean return as its production-only maximum expected profit. With the same condition on the target mean return, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative. We construct the return-risk efficient frontier that characterizes the optimal return-risk trade-off. Our numerical study using data from a prominent automotive manufacturer shows that the markdowns in price and reduction in VPQ are small under our model and that the hedging strategy substantially reduces risk without materially reducing operational profit. This paper was accepted by Kay Giesecke, finance. Funding: Financial support from the Hong Kong Research Grants Council [General Research Fund 17200521] is gratefully acknowledged. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2023.4942. [ABSTRACT FROM AUTHOR]

We present an equilibrium model of hedging for commodity processing firms. We show the optimal hedge ratio depends on the convexity of the firm's cost function and the elasticity of the supply of the input and the demand for the output. Our calibrated model suggests that hedging tends to be ineffective. When uncertainty comes exclusively from either the supply or from the demand side, updating the hedge dynamically, and using nonlinear contracts improves hedging effectiveness. However, with both supply and demand uncertainty, hedging effectiveness can be low even with option-based and dynamic hedging strategies. [ABSTRACT FROM AUTHOR]

We use the staggered enactment of anti-recharacterization laws as a plausibly exogenous shock to the value of securitizing collateral through special purpose vehicles (SPVs) and test how collateral values impact corporate risk management. Following the laws' enactment, we find increases in commodity, foreign exchange, and interest rate hedging, especially for firms with exposure to these risks and that rely on SPVs. Supporting the collateral constraints literature, the effect is weaker for firms that likely need the collateral for external financing, such as financially constrained firms. Our findings highlight fluctuations in collateral values as an important consideration in risk management decisions. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the impact of global crises, including the Global Financial Crisis (GFC), the COVID-19 pandemic, and the subsequent inflation crisis, on the fluctuations of the Real Effective Exchange Rate (REER). The cyclicality of REER has been examined as influenced by economic shocks, highlighting how pandemic-induced economic disruptions have reshaped REER dynamics differently from the more financially triggered fluctuations of the Global Financial Crisis. Design/methodology/approach: The methodology employs a comparative spectral analysis approach, utilizing time-series data to track REER movements across various countries. Findings: The findings indicate that the GFC and the COVID-19 pandemic led to shortening periods of cyclicality. Moreover, there has been a noticeable improvement in the synchronization of REER movements post-GFC, suggesting that economies may converge in their responses to global economic shocks. This convergence implies a potential stabilization of exchange rate movements in the face of future crises, underlining the importance of coordinated monetary policies. Research limitations/implications: To model the REER index, one can use also classical analytical methods such as VAR models, where macroeconomic factors can serve as variables. In spectral analysis itself, other filters like the Hodrick-Prescott filter or applying a spectral window can be used. Practical implications: The conducted study implies an improvement in synchronization among financial systems, with this knowledge, more appropriate and rational monetary decisions can be made, and private entities can better employ hedging strategies against currency exchange rate fluctuations. The study also aids in identifying inflationary pressures and the impact of the global financial system on the inflation index in individual countries. Originality/value: The methods used in the article represent an innovative approach to modeling the REER index, providing new insights into the cyclicality of the index and the shortening periods of its occurrence. The article is addressed both to researchers in this field and to individuals involved with the monetary system and those working in the financial industry. [ABSTRACT FROM AUTHOR]

Copyright of Australian Accounting Review is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the dynamic sphere of financial markets, hedge funds have emerged as a critical force, navigating through volatility with advanced risk management techniques yet grappling with the challenges posed by illiquid assets. This study aims to transcend traditional option pricing models, which struggle under the complexities of hedge fund investments, by exploring the applicability of machine learning in financial risk management. Leveraging Deep Neural Networks (DNNs) and Long Short-Term Memory (LSTM) cells, the research introduces a model-free, data-driven approach for discrete-time hedging problems. Through a comparative analysis of simulated data and the implementation of LSTM architectures, the paper elucidates the potential of these machine learning techniques to enhance the precision of risk assessments and decision-making processes in hedge fund investments. The findings reveal that DNNs and LSTMs offer significant advancements over conventional models, effectively capturing long-term dependencies and complex patterns within financial time series data. Consequently, the study underscores the transformative impact of machine learning on the methodologies employed in financial risk management, proposing a novel paradigm that promises to mitigate the intricacies of hedging illiquid assets. This research not only contributes to the academic discourse but also paves the way for the development of more adaptive and resilient investment strategies in the face of market uncertainties. [ABSTRACT FROM AUTHOR]

The development of deep learning technique has granted firms with new opportunities to substantially improve their risk management strategies for sustainable growth. This paper introduces a novel deep learning‐based financial hedging (DL‐HE) strategy to leverage the salient ability of deep learning in extracting nonlinear features from complex high dimensional data, thus boosting the management of inventory risks arising from erratic commodity prices. Using real‐world data, we find that the average annualized economic benefit of the proposed strategy is at least 1.21 million CNY for a typical aluminum firm carrying an average level of inventory in China, as compared with those of the traditional hedging strategies. Further analysis reveals that such an economic benefit can largely be explained by the efficacy of the proposed DL‐HE strategy in terms of significantly improving return while still effectively controlling risk. Moreover, the superior of this strategy remains robust when extending to copper and zinc. [ABSTRACT FROM AUTHOR]

This study develops a regime‐switching Riemannian‐geometric covariance framework for futures hedging. The covariance of conventional regime‐switching BEKK (Baba, Engle, Kraft and Kroner) (RSBEKK) evolves on flat spaces that exclude a prior the possibility of inherent geometric covariance dynamic. A Riemannian‐geometric regime‐switching BEKK (RG‐RSBEKK) is proposed such that the covariance moves along a trajectory on Riemannian manifolds. RG‐RSBEKK is applied to China Securities Index 300 futures for hedging the stock sector exposures. Empirical results reveal that specifying covariance dynamic on curved spaces enhances hedging effectiveness based on the model confidence set with loss measures of variance, utility, value‐at‐risk, and Frobenius distance. [ABSTRACT FROM AUTHOR]

In this research, we empirically evaluate whether Indian bullion market instruments glitter as a hedge or a safe haven against oil volatility shock before and during the Russia-Ukraine war. The wavelet coherence mechanism is used to encapsulate the hedge or a safe haven potential over the time-frequency horizon. We find evidence that gold and silver act as a medium horizon investment hedge for crude oil during pre - and invasion periods. However, the results unveil that gold and silver act as a weak safe haven for crude oil during the turmoil period, partly consistent with the prior research findings. The results further show that there is a positive relationship between gold and oil at the beginning of the war and at intervals, 29/06/2022 to 15/07/2022, illustrating that gold acts as a diversifier for crude oil. [ABSTRACT FROM AUTHOR]

We study how a CFO's risk‐taking incentives affect corporate hedging by utilising hand‐collected data from 2009 to 2019 on corporate hedging and managerial compensation for a sample of US oil and gas firms. The relative convexity of CFO equity compensation negatively affects the likelihood and extent of hedging. When the CFO and CEO have diverging risk‐taking incentives, the relative convexity of the CFO's equity payoff prevails over that of the CEO. This evidence underscores the primary role of the CFO in steering a firm's hedging strategy. [ABSTRACT FROM AUTHOR]

This paper examines the production and hedging decisions of the competitive firm under price uncertainty when the firm is not only risk averse but also regret averse. Regret-averse preferences are characterized by a modified utility function that includes disutility from having chosen ex post suboptimal alternatives. The extent of regret depends on the difference between the actual profit and the maximum profit attained by making the optimal production, and hedging decisions had the firm observed the true realization of the random output price. While the separation theorem holds under regret aversion, the prevalence of hedging opportunities may have perverse effect on the firm's optimal output level, particularly when the firm is sufficiently regret averse. The full-hedging theorem, however, does not hold. We derive sufficient conditions under which the regret-averse firm's optimal futures position is an under-hedge (over-hedge). We further show that the firm optimally increases (decreases) its futures position when the price risk possesses more positive (negative) skewness. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to test for volatility spillovers among Islamic stock markets with the exogenous impact of geopolitical risk (GPR) to check the risk transmission among Saudi Arabia, Malaysia, Indonesia and Turkey. Researchers test for both the symmetric and asymmetric risk transmission. Design/methodology/approach: For the symmetric response of volatility, the study uses simple generalized autoregressive conditional heteroscedastic (GARCH) and for the asymmetric response of volatility with the exogenous impact of GPR, the exponential GARCH models have been adopted. Findings: The results suggest spillover effects exist from Turkey to Saudi Arabia, Indonesia to Malaysia and Saudi Arabia and Malaysia to Indonesia. The findings of volatility spillover from GPR to sample countries suggest that only Malaysia and Indonesia experience volatility spillovers from GPR. Research limitations/implications: The present study is limited to the context of four countries and Islamic equities; the study contributes to the literature on volatility spillover, Islamic finance, GPR and asset pricing. Practical implications: This study contributes to individual, institutional investors' policymakers' knowledge in determining security prices, trading plans, investment hedging and policy regulation. Social implications: The extant literature disregards the GPR index to examine the volatility spillover effects among Islamic stock markets, which allow researchers to justify the mechanism of risk transmission due to GPR across the Islamic stock market. Originality/value: To the best of the authors' knowledge, this is the first research of its type to look at volatility spillover and GPR transmission in Islamic stock markets. [ABSTRACT FROM AUTHOR]

We establish the asset pricing and hedging principle in a financial market model, which is a specific case of the von Neumann-Gale dynamical system, with both fixed and proportional transaction costs and trading constraints. The main results are hedging criteria stated in terms of consistent valuation systems, generalizing the notion of an equivalent martingale measure. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic return connectedness among Decentralized Finance (DeFi) assets [Chainlink (LINK), Maker (MAKER), Basic Attention Token (BAT), and Synthetix (SNX)] and Centralized Financial assets (CeFi), specifically MSCI World, MSCI US, MSCI Europe, MSCI East Europe, MSCI Asia, and MSCI Far East. The Time-Varying Parameter Vector Autoregressions (TVP-VAR) framework is adopted. Our research findings demonstrate that economic events create variations in the dynamic connectedness of a system. There were peaks throughout the COVID-19 pandemic. Furthermore, the results indicate that DeFi assets like BAT and LINK are the most common shock transmitters, while MAKER and SNX are the most common shock receivers. Nonetheless, the pairwise connectedness for LINK and MSCI Far EAST (FarEAST) shows that may not only influence other markets but are also equally receptive to innovations that occur in the majority of these markets due to their high pairwise links. The retrieved evidence has major implications for investors in terms of the creation of strategies for the optimization of portfolios and asset allocation, the minimizing of downside risk, and hedging techniques. [ABSTRACT FROM AUTHOR]

We examine the association of Bitcoin, and other cryptocurrency, returns with changes in inflation expectations, and form a comparison with gold, a traditional inflation hedge. We control for uncertainty in economic policy, cryptocurrency, and financial markets, and show that cryptocurrency returns are positively related to changes in US inflation expectations only for a limited set of circumstances. Unlike with gold, the identified relationship is only significant for short‐term inflation expectations, and when inflation or market‐implied inflation expectations are below 2% (the Fed's inflation target). Moreover, cryptocurrency returns tend to be lower on days with monthly consumer price index (CPI) announcements and respond negatively to CPI surprises. Our results suggest that cryptocurrencies do not currently offer investors a viable alternative to gold for hedging inflation. [ABSTRACT FROM AUTHOR]

The article examines the topical issue of risk management in foreign economic and logistics activities, which is closely related to the enterprise’s chosen strategy and ensures its sustainable development. The study considers the issue of developing a hedging strategy using statistical methods since adequate forecasting allows predicting the impact of external environment factors on the exchange rate, which will allow the enterprise to timely predict and mitigate the risks in foreign economic and logistics activities. The authors have used general scientific and special methods of systemic and structural analysis to clarify the directions of sustainable performance and risk management tools; formulate the goals and steps in choosing a strategy of hedging; compare the methods of foreign exchange rate risk hedging and perform correlation and regression analysis of factors that impact exchange rate under crisis conditions. The purpose of the paper is to study the specifics of ensuring the sustainable performance of an enterprise using statistical methods in risk management for planning foreign economic and logistics activities. The authors have proposed a definition of a hedging strategy based on the concepts, approaches, and ideas of asset and/or investment management with the aim of reducing loss through hedging instruments, whose feasibility can be substantiated by statistical methods. It has been found that the formation and successful implementation of a hedging strategy requires the use of statistical analysis in order to timely predict fluctuations in exchange rates. The findings of the research were tested based on the performance of PrJSC "Linde Gas Ukraine". In the strategy of hedging the exchange rate risks, it has been proposed to simultaneously open a foreign currency deposit and take a loan in the national currency to replenish the company's working capital. The perspective of further research is the implementation of the proposed hedging strategy and assessment of its effectiveness. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models. Design/methodology/approach: DCC-GARCH and ADCC-GARCH models. Findings: The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks. Originality/value: Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war. [ABSTRACT FROM AUTHOR]

The paper investigates quadratic hedging in a semimartingale market that does not necessarily contain a risk-free asset. An equivalence result for hedging with and without numeraire change is established. This permits direct computation of the optimal strategy without choosing a reference asset and/or performing a numeraire change. New explicit expressions for optimal strategies are obtained, featuring the use of oblique projections that provide unified treatment of the case with and without a risk-free asset. The analysis yields a streamlined computation of the efficient frontier for the pure investment problem in terms of three easily interpreted processes. The main result advances our understanding of the efficient frontier formation in the most general case in which a risk-free asset may not be present. Several illustrations of the numeraire-invariant approach are given. [ABSTRACT FROM AUTHOR]

The establishment of a long-term, cross-border market in which forward market coupling and bilateral contracts are developed in an integrated approach is instrumental for the European internal electricity market. We propose the joint energy and transmission right auction (JETRA) mechanism, developed by O'Neill et al., as a solution for long-term cross-border markets in Europe. The main contribution of this research lies in its examination of the underlying market structures for effective JETRA implementation. We compare the institutional setting, market rules, and grid modeling under nodal and zonal pricing systems, adapting JETRA to the flow-based market coupling (FBMC) mechanism that is currently implemented in the European day-ahead market. This adaptation reveals the inherent limitations of FBMC in supporting JETRA, in particular in the long-term auction. We also identify constraints posed by existing European market rules, particularly those that affect the application of multi-settlement rules and the effective timeframe of hedging instruments. In conclusion, our research suggests that transitioning from zonal to nodal pricing is essential for JETRA's effective implementation. Furthermore, a comprehensive market reform is required to seamlessly integrate long- and short-term markets. [ABSTRACT FROM AUTHOR]

The object of this study is the process of risk management at enterprises in the metallurgical industry as a subject of foreign economic activity. Under the conditions of an export-oriented economy, it is important to have tools for early response to crises and challenges caused by various factors. Volatility of the market and global and domestic prices for key products and raw materials add uncertainty and encourage management to look for tools to protect their financial income. The construction of a risk map and the use of mathematical tools for forecasting and minimizing the negative consequences of the occurrence of risky events could contribute to making optimal management decisions in operational activities and in the pricing process. Interpretation of the risk management cycle for the needs of enterprises in the metallurgical industry will make it possible to adequately identify the risks inherent in the industry and devise measures for flexible response to crises and challenges. In the course of the study, it was found out that exporting companies need protection, first of all, against price risk, which increases significantly under the conditions of inflation and due to jumps in the consumer price index in a particular country. It is suggested to use hedging as a protection tool. Determining the perimeter of risk hedging by means of a risk-oriented selection of indicators (external and internal, commercial, and non-commercial risks) depending on a certain market situation will make it possible to establish a correlation between key influencing factors. The implementation of the risk-hedging cycle in the practice of the enterprises in the metallurgical industry will make it possible to ensure flexible management of the pricing procedure for metal products both in the domestic market and during foreign trade. [ABSTRACT FROM AUTHOR]

In response to the intensifying US-China rivalry, Singapore ostensibly “hedges”, a strategy that avoids choosing between Washington and Beijing and maximizes gains from cooperating with both powers while avoiding confrontation. Hedging also extenuates Singapore’s central location in Asia and its role as an established commercial and financial hub. As such, it appears to reflect the imperative of any small state: survival. However, in contrast to the argument that domestic politics does not matter in Singapore’s foreign policy, this article demonstrates how the domestic imperative of legitimizing the political dominance of the ruling People’s Action Party (PAP) shapes the government’s hedging strategy. [ABSTRACT FROM AUTHOR]

Using Indonesia as a case study, this article aims to contribute to the existing literature on why weaker states engage in hedging by examining how Indonesia’s domestic factors influence its foreign policy decisions regarding the United States and China. The article argues that domestic and foreign policies are interconnected as domestic agendas, including the interests and aspirations of Indonesian politicians as well as public opinion, have led to variations in the country’s hedging behaviours towards the two great powers. On one hand, domestic political and economic considerations drive Indonesia to engage with the United States and China. On the other hand, the same factors can also act as hindrances that limit Indonesia’s engagement with these powers. Consequently, despite having strong defence ties with the United States, Indonesia now sees China as a major and essential economic partner that helps the country and its leaders achieve their development goals. [ABSTRACT FROM AUTHOR]

We investigate the impact of COVID-19 on commodity return volatility. We find that the impact of COVID-19 on return volatility is different across different markets. Unlike S&P 500 sector indices, commodity return volatility is less sensitive to the impact of COVID-19. The impact of vaccination programs on return volatility is weak for both commodity and financial markets. We employ Fama-French 3 Factor Model and APARCH (1,1) for return volatility estimation. The variation in COVID-19's impact across different markets has an important implication for return volatility hedging. [ABSTRACT FROM AUTHOR]