Hedging Performance Research Articles

Evaluating the performance of futures hedging using factors-driven realized volatility

The complexity and uncertainty of the financial market mainly stem from the rich market internal transaction information and a wide range effect of external factors. To this end, this paper proposes the combination factors-driven forecasting method to predict realized volatilities of the CSI 300 index and index futures. Based on the volatilities predicted by the proposed method, we further evaluate the ex-ante hedging performance in comparison to the conventional HAR model as well as GARCH-type models. The empirical results indicate that the factors-driven realized volatility model significantly dominates the other commonly used models in terms of hedging effectiveness. Furthermore, the superiority of the proposed method is robust in different market conditions, including significant rising or falling and abnormal market fluctuations in the COVID-19 pandemic, and in different index markets. Therefore, this paper improves the prediction accuracy of volatility by integrating market internal transaction information and external factor information, and the proposed method in this paper can be used by investors to obtain an excellent hedging effect.

Improving and Extending the Wu-Zhu Static Hedge

Without considering the underlying risk dynamics and jumps, Wu and Zhu (2016) recently proposed an ingenious approach of hedging options statically with an option portfolio. We improve their scheme in three ways. First, we theoretically make the Wu-Zhu approach more accurate by utilizing the Black-Scholes-Merton dual equation. Second, we propose a better error measure, the so-called “true hedge error,” that takes the initial cost of the hedge into consideration. Finally, we suggest two measures of percentage hedge errors to assess hedge performance more precisely. With extensive simulations under both the Black-Scholes-Merton and Heston models, we show that our proposal significantly improves the hedge performance, especially for in-the-money and at-the-money options. Importantly, we extend Wu-Zhu to options with a payoff of homogeneous of degree one.

A regime-switching real-time copula GARCH model for optimal futures hedging

A regime-switching real-time copula GARCH (RSRTCG) model is suggested for optimal futures hedging. The specification of RSRTCG is to model the margins of asset returns with state-dependent real-time GARCH and the dependence structure of asset returns with regime switching copula functions. RSRTCG is faster in adjusting to the new level of volatility under different market regimes which is a regime-switching multivariate generalization of the state-independent univariate real-time GARCH. RSRTCG is applied to cross hedge the price risk of S&P 500 sector indices with crude oil futures. The empirical results show that RSRTCG possesses superior hedging performance compared to its nested non-real-time or state-independent copula GARCH models based on the criterion of percentage variance reduction, utility gain, model confidence set, model combination strategy, risk-adjusted return and reward-to-semivariance ratio.

SENSITIVITIES AND HEDGING OF THE COLLATERAL CHOICE OPTION

The collateral choice option allows a collateral-posting party the opportunity to change the type of security in which the collateral is deposited. Due to nonzero collateral basis spreads, this optionality significantly impacts asset valuation. Because of the complexity of valuing the option, many practitioners resort to deterministic assumptions on the collateral rates. In this paper, we focus on a valuation model of the collateral choice option based on stochastic dynamics. Intrinsic differences in the resulting collateral choice option valuation and its implications for collateral management are presented. We obtain sensitivities of the collateral choice option price under both the deterministic and the stochastic model, and we show that the stochastic model attributes risks to all involved collateral currencies. Besides an inability to capture volatility effects, the deterministic model exhibits a digital structure in which only the cheapest-to-deliver currency influences the valuation at a given time. We further consider hedging an asset with the collateral choice option by a portfolio of domestic and foreign zero-coupon bonds that do not carry the collateral choice option. We propose static hedging strategies based on the crossing times of the deterministic model and based on variance-minimization under the stochastic model. We show how the weights of this model can be explicitly determined with the semi-analytical common factor approach and we show in numerical experiments that this strategy offers good hedging performance under minimized variance.

Improving Interest Rate Risk Hedging Strategies through Regularization

The effectiveness of duration and convexity hedging strategies deteriorates in the presence of non-parallel shifts of the yield curve. In the absence of appropriate constraints, the extension of these strategies accounting for changes in the shape of the yield curve generates unstable weights and extreme leverage, leading to poor out-of-sample hedging performance. To address this conundrum, we recast the bond portfolio immunization problem as a multifactor optimization program with leverage constraints and weight regularization. These regularized immunization strategies offer a robust improvement in hedging performance and are particularly well-suited to secure future cash flow needs such as pension liabilities.

A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models

Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions.

Day‐ahead unit commitment for hydro‐thermal coordination with high participation of wind power

AbstractThe variability and uncertainty of renewable resources impose new challenges in the operational planning related to the unit commitment of generation units. The development of day‐ahead multi‐period optimal power flow, under integration of wind power, requires modelling of multiple scenarios in order to ensure an optimal power flow minimising the generation cost. A progressive hedging approach has been proposed and developed to solve efficiently the unit commitment problem as a two‐stage stochastic programming problem to update each stage in parallel. The performance of progressive hedging is compared with a standard mixed‐integer linear programming problem. The results indicate that the computation time is 50 times faster than standard mixed‐integer linear programming. The test case system is based on a reduced version of the interconnected Colombian system. The comparative results indicate an important reduction in computational time.

Commodity hedging benefits: analyses among different financial assets

PurposeThis study aims to investigate the benefits of commodity hedging in the global stock index, bond and foreign currency (FX) portfolios.Design/methodology/approachThe authors compare various hedging strategies and factor transaction costs. The authors analyze equally weighted, dynamic hedging ratio, risk parity and reward to risk timing strategies. Volatilities are estimated using historical, GARCH(1,1), and APARCH(1,1) methods. In addition, the authors evaluate the portfolio's hedging performance (HP) based on four different dimensions: volatility (annualized standard deviation), Sharpe ratio (SR), HP, and high-low ratio (HL).FindingsThe authors observe different benefits of the commodity hedging strategy among financial assets (stocks, bonds or FX).The authors find that commodity hedging in the stock markets is the best option, if the authors optimize the hedging ratio using dynamic hedging from historical data. The authors also document that for stock portfolio managers, adding commodities will generate a more conservative strategy, whereas for bond and/or FX portfolio managers, adding commodities will generate a more aggressive strategy.Originality/valueThis study contributes to the literature by investigating commodity hedging in the global stock index, bond and FX portfolios. First, the authors provide details on the diversification benefits in the commodities. Second, the authors document the hedging strategy that is the best as a part of the diversification strategy by adding commodities. Third, the authors provide a practical analysis by reporting the financial assets portfolio that is appropriate for commodity hedging following the portfolio managers' objectives (e.g. reducing risks or improving the risk-reward ratio).

Dynamic Conditional Bias-Adjusted Carry Cost Rate Futures Hedge Ratios

This paper proposes new dynamic conditional futures hedge ratios and compares their hedging performances along with those of common benchmark hedge ratios across three broad asset classes. Three of the hedge ratios are based on the upward-biased carry cost rate hedge ratio, where each is augmented in a different bias-mitigating way. The carry cost rate hedge ratio augmented with the dynamic conditional correlation between spot and futures price changes generally: (1) provides the highest hedging effectiveness and (2) has a statistically significantly higher hedging effectiveness than the other hedge ratios across assets, sub-periods, and rolling window sizes.

Performance of Financial Hedging and Earnings Management Under Diverse Corporate Information Quality

Performance of Financial Hedging and Earnings Management Under Diverse Corporate Information Quality

Learning sequential option hedging models from market data

Following a direct data-driven approach, we propose a robust encoder-decoder Gated Recurrent Unit (GRU), GRU δ , for optimal discrete option hedging. The proposed GRU δ utilizes the Black-Scholes model as a pre-trained model and incorporates sequential information and feature selection. Using the S&P 500 index European option market data, we demonstrate that the weekly and monthly hedging performance of the proposed GRU δ significantly surpasses that of the data-driven minimum variance (MV) method, the regularized kernel data-driven model, and the SABR-Bartlett method. In addition, the daily hedging performance of the proposed GRU δ also surpasses that of MV methods based on parametric models, the kernel method, and SABR-Bartlett method.

Basis Forecasting and Performance Analysis on the Selective Long Hedge for Imported Corn

The primary objective of this study is to forecast basis for imported corn and evaluate the hedging performance of a selective long hedge strategy. Six models are used to forecast cost and freight (C&F) basis for imported corn: a moving average model (3 and 5 years respectively), a moving average model with current basis information (3 and 5 years respectively), an ordinary least squares (OLS) regression model, and an autoregressive-moving average (ARMA) time series model. Six forecast horizons from 1 to 6 months are considered. The forecasting accuracy of 6 competing basis forecasts is determined in terms of mean absolute error (MAE) and root mean squared error (RMSE). The results show that the ARMA model performs best overall. Based on the results that the ARMA model is the best overall, basis forecasts from the ARMA model are used to simulate a selective long hedge where a long hedge is placed only when the basis will have weakened over the hedging period. The net buying price (NBP) from the selective long hedge is compared in pairs with the objective price of the traditional long hedge. The simulation results show that the NBP from the selective long hedge is significantly lower than the objective price of the traditional long hedge over the hedging period of 5 and 6 months.

The effects of skewness on hedging decisions: an application of the skew-normal distribution in WTI and Brent futures

Skewness, as a proxy for extreme risks or losses, deserves more attention from risk management work of portfolio selection and futures hedging. We evaluate the hedging performance of strategies considering the skewness for two major benchmark international crude oil markets, Brent and WTI, with sample period ranging from June 11, 2018, to May 19, 2021. This paper contributes to the literature by accounting for futures basis and the skewness of the hedged portfolio return. Specifically, we first extend the existing literature of Lien (2010), whose study investigated the effect of skewness on optimal production and hedging decisions, to the case of a futures bias existing. Then, we propose minimum-risk hedging models wherein the return of the hedged portfolio return is assumed to follow a skew-normal distribution, which is a generalization of normality assumption. From the empirical results, we find that skewness cannot be ignored, otherwise it will lead to wrong hedging decision. Furthermore, hedging strategies under skew-normal distribution are outperformed than that under the normal distribution assumption. The research results of this paper have important implications for investors and decision makers to hedge the price risk of crude oil in extreme market conditions.

A discrete-time hedging framework with multiple factors and fat tails: On what matters

This article presents a quadratic hedging framework for a general class of discrete-time affine multi-factor models and investigates the extent to which multi-component volatility factors, fat tails, and a non-monotonic pricing kernel can improve the hedging performance. A semi-explicit hedging formula is derived for our general framework which applies to a myriad of the option pricing models proposed in the discrete-time literature. We conduct an extensive empirical study of the impact of modelling features on the hedging effectiveness of S&P 500 options. Overall, we find that fat tails can be credited for half of the hedging improvement observed, while a second volatility factor and a non-monotonic pricing kernel each contribute to a quarter of this improvement. Moreover, our study indicates that the added value of these features for hedging is different than for pricing. A robustness analysis shows that a similar conclusion can be reached when considering the Dow Jones Industrial Average. Finally, the use of a hedging-based loss function in the estimation process is investigated in an additional robustness test, and this choice has a rather marginal impact on hedging performance.

Investigation of trees as natural protective barriers using simulated blast environment

Blast barriers are commonly used for protecting critical infrastructure from blast effects. While rigid blast walls are the most frequently used barrier type, they are often unattractive and obtrusive. It is therefore practical to consider the use of natural barriers. The applicability of trees as natural blast barriers has not been studied widely. Apart from several recent studies investigating the performance of hedges using small explosive charges and large-scale high-explosive forest blowdown studies from the 1950s-1960s, no other published results on the mechanical response of vegetation and its blast wave mitigation potential are found in the open literature. This paper presents the experimental results of the interaction of far-field blast waves with trees aimed to evaluate the potential of trees in protecting infrastructure targets from blast threats. Full-scale trees were exposed to simulated far-field blast loads generated in an Advanced Blast Simulator. The dynamic structural response of the Juniperus Spartan trees subjected to blast wave and their blast sheltering potential were investigated. This paper revealed the potential for trees in attenuating far-field blast waves and shelter target structures. A numerical model based on Computational Fluid Dynamics was developed in ANSYS FLUENT to reveal the blast mitigation mechanism of barriers against far-field blast loads. The model was also used to explain experimental observations.

Research on the Hedge Ratio of China's Crude Oil Futures — Based on DCC-GARCH Model

Crude oil plays an important role in economic development. This paper chooses China’s crude oil futures and crude oil actuals as the research objects, and builds the DCC-GARCH model to study the hedge ratio under the risk minimization standard. The hedge ratios obtained from the DCC-GARCH model will be compared with those obtained from OLS, B-VAR and VECM models. The empirical results prove that: China’s crude oil futures and actuals have a significant reverse “leverage effect”; China’s crude oil futures have a variance reduction of more than 70% under all models; the DCC-GARCH model achieves the best hedging performance in the four models.

Deep Hedging under Rough Volatility

We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular, we analyse the hedging performance of the original architecture under rough volatility models in view of existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. We also analyse the hedging behaviour in these models in terms of Profit and Loss (P&L) distributions and draw comparisons to jump diffusion models if the rebalancing frequency is realistically small.

Relationships among US S&P500 Stock Index, its Futures and NASDAQ Index Futures with Volatility Spillover and Jump Diffusion: Modeling and Hedging Performance

This study takes the US S&P500 stock index cash, futures and NASDAQ stock index futures as the main research objects, and applies the ARJI (autoregressive jump intensity model) VEC GJR-GARCH model to examine the co-integration, volatility spillover, jump behavior and hedge performance of the three markets. With the rapid circulation of new information, the financial market will often fluctuate under the impact of new information. Investors will have different and timely responses to emergencies, and this event will have an impact on the stock market. When the event is unexpected or abnormal, the financial market will have huge fluctuations, and this kind of fluctuation is a jump. The empirical results found that the three markets have linkages and volatility spillover effects, and there are indeed discontinuous jumps. Two-way volatility spillovers between S&P500 index cash and futures, and only one-way volatility spillovers from S&P500 futures to the Nasdaq futures market. International investors need to consider information from their own-market volatility (risk) as well as information on volatility spillovers (risk) from other markets. The jump frequency is not a fixed constant, that is, the jump frequency (strength) generated by abnormal information changes over time. In addition, the results of this research also found that the ARJI VEC GJR-GARCH model can better capture the risk of fluctuations in price discontinuities after adding jump factors to the hedging performance estimated by the ARJI VEC GJR-GARCH model. The hedging performance can be more effective, which is conducive to investors' risk management decisions. Also, the performance of direct hedging that is better than the performance of cross hedging.

Hedging cryptos with Bitcoin futures

The introduction of derivatives on Bitcoin enables investors to hedge risk exposures in cryptocurrencies. Because of volatility swings and jumps in cryptocurrency prices, the traditional variance-based approach to obtain hedge ratios may not be suitable for hedgers. In this work, we consider two extensions of the traditional approach: first, different dependence structures are modelled by different copulae, such as the Gaussian, Student-t, Normal Inverse Gaussian and Archimedean copulae; second, different risk measures, such as value-at-risk, expected shortfall and spectral risk measures are employed to find the optimal hedge ratio. Extensive out-of-sample tests using the data from the time period December 2017 until May 2021 give insights in the practice of hedging various cryptos and crypto indices, including Bitcoin, Ethereum, Cardano, the CRIX index and a number of crypto-portfolios. Evidence shows that BTC futures can effectively hedge BTC and BTC-involved indices. This promising result is consistent across different risk measures and copulae except for the Frank copula. On the other hand, we observe complex and diverse dependence structures between non-BTC-related cryptocurrencies and the BTC futures. As a consequence, the hedge performance of non-BTC-related cryptocurrencies is mixed and even suitable for some assets.

Delta hedging bitcoin options with a smile

We analyse robust dynamic delta hedging of bitcoin options using a set of smile-implied and other smile-adjusted deltas that are either model-free, in the sense that they are the same for every scale-invariant stochastic and/or local volatility model, or they are based on simple regime-dependent parameterisations of local volatility. These deltas are popular with option market makers in traditional assets because they are very easy to implement. Previous empirical research on dynamic delta hedging is based solely on equity index options, but analysis of our unique data on hourly historical bitcoin option prices reveals that bitcoin implied volatility curves behave very differently from those of equity index options. For call and put options with a wide range of moneyness and with synthetic constant maturities of 10, 20 and 30 days, we compare the dynamic hedging performance of different smile-adjusted deltas over two one-year periods. We also examine the use of the perpetual contract rather than the standard futures as hedging instrument because the basis risk for the perpetual is very much smaller than it is for calendar futures. Results are presented as testable statistics of hedging error variance ratios. In certain periods the use of smile-implied hedge ratios can significantly out-perform the simple Black–Scholes delta hedge, especially when using the perpetual swap as hedging instrument, where efficiency gains can exceed 30% for out-of-the-money puts, and reach an average of 15% when hedging short-term out-of-the money calls during periods when the implied volatility curve slopes upwards. The advantage of using the perpetual contract is especially evident during 2021, for the longer-term contracts for which the basis is still rather large.