Hedging Performance Research Articles

Risk Management and Portfolio Optimization for Gas- and Coal-Fired Power Plants in Germany: A Multivariate GARCH Approach

We investigate the hedging effectiveness of energy derivatives traded at the European Energy Exchange (EEX), which can be used for mitigating the risk exposure of gas- and coal-fired power plants in Germany. Our aim is to identify the optimum forward contract for simultaneously hedging power output and fuel input price risk. The optimum hedging contract for each commodity is identified with the help of a multivariate GARCH model (D-BEKK model). We find that it is more prudent to hedge the spot electricity and coal prices with long-term contracts, whereas for natural gas prices there is evidence that short-term futures enable higher hedging effectiveness. Further, with all futures contracts available we construct portfolios and compare the hedging performance with that of the individual futures contracts. More specifically, we first identify the point at which the combination of spot and forward prices gives the maximum reduction of risk exposure and then investigate the impact of time-varying risk premiums on the spot-forward price relationship. Finally, we compute portfolio weights for different risk levels that reveal the optimal mix of spot and futures contracts. The results show that electricity and coal both produce negative returns, whereas for natural gas we are able to construct economically viable portfolios, albeit with low returns.

Static mitigation of volumetric risk

We consider the problem of designing a financial instrument aimed at mitigating the joint exposure of energy-linked commitments to random price and volume delivery fluctuations. We formulate a functional optimization problem over a set of regular payoff functions: one is written on energy price, while the other is issued over any index exhibiting statistical correlation to volumetric load. On theoretical grounds, we derive closed-form expressions for both payoff structures under suitable conditions about the statistical properties of the underlying variables; we pursue analytical computations in the context of a lognormal market model and deliver explicit formulas for the optimal derivative instruments. On practical grounds, we first develop a comparative analysis of model output through simulation experiments; next, we perform an empirical study based on data quoted at EPEX SPOT power market. Our results suggest that combined price-volume hedging performance improves along with an increase of the correlation between load and index values. This outcome paves the way for a new class of effective strategies for managing volumetric risk upon extreme temperature waves.

The Inflation-Hedging Performance and Risk-Return Characteristics of Residential Property Investments in Gombe, Nigeria

Purpose: This study aims at investigating the inflation-hedging performance of residential property investments in Gombe metropolis Nigeria.Design/Methodology/Approach: Questionnaire survey was conducted to obtain primary data on rental and capital values of residential properties from branch managers of Estate Surveying and Valuation Firms in the study area, this was subsequently translated to total returns. Similarly, secondary data with respect to the Nigerian Consumer Price Index (CPI) which was used as a proxy for actual inflation and the 90-day Treasury bill rates (used as proxy for unexpected inflation) for the period between 2003 and 2012 were also collected from the National Bureau of Statistics (NBS) and the Central Bank of Nigeria (CBN) respectively. The unexpected inflation was calculated as the difference between the actual and expected inflation. The 3 groups of data obtained for the study were subjected to Phillip-Perron unit root test as well as the Odinary Least Square Regression analysis.Findings: The study revealed that the inflation-hedging performance of residential property investments in Gombe metropolis vis-a-vis the actual inflation provides a partial hedge (with beta 0.061), vis-a-vis the expected inflation provides a complete hedge (with beta 0.678) and vis-a-vis the unexpected inflation provides a perverse hedge (with beta -0.454).Practical Implications: Inflation-hedging performance of investment asset classes is of particular interest to investors. The results of this study can be useful for investment forecasts as well as investment decisions on asset types to include in portfolios as a measure for protecting investors’ earnings from erosion by inflation.Originality/Value: Research work on the subject of inflation-hedging capabilities in Nigeria majorly focused on commercial real estate and stocks in the Southwestern region of Nigeria. This study expanded the scope of the inflation-hedging literature by empirically investigating the inflation hedging performance of residential real estate investments in Northern Nigeria (Gombe metropolis).

Optimal hedging in carbon emission markets using Markov regime switching models

This paper proposes a Markov regime switching framework for modeling carbon emission (CO2) allowances that combines a regime switching behavior and disequilibrium adjustments in the mean process, along with a state-dependent dynamic volatility process. We find that all regime switching based hedging strategies significantly outperform single regime hedging strategies (both in-sample and out-of-sample), with the newly proposed framework providing the greatest variance reduction and the best hedging performance. Our results indicate that risk managers using state-dependent hedge ratios to manage portfolio risks in carbon emission markets will achieve superior hedging returns.

Score-Driven Nelson Siegel: Hedging Long-Term Liabilities.

textabstractDue to its affine structure the Nelson-Siegel model for yield curves can be transformed to a factor model for excess bond returns. Hedging interest rate risk in this framework amounts to eliminating the factor exposure and minimizing the residual risk. Fitting the model directly on excess returns with constant factor loadings leads to large hedging errors caused by substantial and persistent time-variation in the shape parameter of the Nelson-Siegel factor loadings. To capture this variation we develop a Dynamic Conditional Score (DCS) model for the shape parameter. This dynamic model offers superior hedging performance and reduces the hedging error standard deviation by almost 50% during the financial crisis. Much of the improvement is due to the model for the shape parameter with some further reduction achieved by a GARCH model for the residual risk.

Estimation and Hedging Effectiveness of Time‐Varying Hedge Ratio: Nonparametric Approaches

AbstractMany studies have estimated the optimal time‐varying hedge ratio using futures, with most employing a bivariate generalized autoregressive conditional heteroscedasticity (BGARCH) model or a random coefficient model to estimate the time‐varying hedge ratio. However, it has been argued that when the variability of the estimated time‐varying hedge ratio is large, this ratio's hedging performance is not as good as that of the unconditional (constant) hedge ratio. This study proposes a nonparametric estimation approach to estimate and evaluate the optimal conditional hedge ratio. This method produces a time‐varying hedge ratio with less volatility than those obtained from the BGARCH and random coefficient models. We evaluate the hedging performance of the various models using soybean oil, corn, S&P 500, and Hang Seng futures indices. The empirical results support the proposed nonparametric approach in terms of both in‐sample and out‐of‐sample performance. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:968–991, 2016

Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection

During the recent European sovereign debt crisis, returns on EMU government bond portfolios experienced substantial volatility clustering, leptokurtosis and skewed returns as well as correlation spikes. Asset managers invested in European government bonds had to derive new hedging strategies to deal with changing return properties and higher levels of uncertainty. In this environment, conditional time series approaches such as GARCH models might be better suited to achieve a superior hedging performance relative to unconditional hedging approaches such as OLS. The aim of this study is to test innovative hedging strategies for EMU bond portfolios for non-crisis and crisis periods. We analyze single and composite hedges with the German Bund and the Italian BTP futures contracts and evaluate the hedging effectiveness in an out-of-sample setting. The empirical analysis includes OLS, constant conditional correlation (CCC), and dynamic conditional correlation (DCC) multivariate GARCH models. We also introduce a Bayesian composite hedging strategy, attempting to combine the strengths of OLS and GARCH models, thereby endogenizing the dilemma of selecting the best estimation model. Our empirical results demonstrate that the Bayesian composite hedging strategy achieves the highest hedging effectiveness and compares particularly favorable to OLS during the recent sovereign debt crisis. However, capturing these benefits requires low transactions cost and efficiently functioning futures markets.

Evaluating the Hedging Performance of Oil and Oilseeds Futures in India

The risk of commodity price volatility impacts almost everyone from individuals to companies and the economy as a whole. Hedging with derivative instruments is a way to insure against such risks. In the present study, the risk reduction function of commodity futures has been evaluated for commodities in the oil and oilseeds segment, namely, refined soya oil, mustard seed, mentha oil and crude palm oil (CPO). The choice of these commodities is made because in India, oilseeds’ trading has grown phenomenally due to massive increase in its domestic consumption which is being met by imports. To attain self-sufficiency and to enhance domestic production, besides other measures, commodity futures help to improve price realization and thereby encourage farmers to increase the area under cultivation. The minimum variance hedge ratio (MVHR) is calculated using the ordinary least square (OLS) method, OLS with additional variables and error correction model (ECM). Hedging effectiveness is quantified using MVHR. The estimates of hedging effectiveness are satisfactory for more liquid futures contracts of refined soya oil and CPO which gave variance reduction to the extent of 55 and 71 per cent, respectively. On the other hand, risk reduction with mustard seed and refined mentha oil futures is only around 28 per cent. According to market reports, the latter are narrow commodities with a small market size and greater susceptibility to price manipulation and cartelization. Measures, such as, better regulatory framework, wider participation of actual users in the commodity value chain and suitable contract specifications, are most essential for lucrative commodity markets.

Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk

This study examines the risk spillovers between energy futures prices and Europe-based carbon futures contracts. We use a Markov regime-switching dynamic correlation, generalized autoregressive conditional heteroscedasticity (MS-DCC-GARCH) model in order to capture the time variations and structural breaks in the spillovers. We further evaluate the optimal weights, hedging effectiveness, and dynamic hedging strategies for the MS-DCC-GARCH model based on both the regime-dependent and regime-independent optimal hedge ratios. We finally complement our analysis by examining the in- and out-of sample hedging performances for alternative strategies. Our results mainly show significant volatility and time-varying risk transmission from energy markets to carbon market. We also find that spot and futures segments of the emission markets exhibit time-varying correlations and volatile hedging effectiveness. The subsample estimates show significant changes in the hedge effectiveness over the different phases of the European carbon market. These results have important investment and policy implications.

Static Mitigation of Volumetric Risk

We consider the problem of designing a financial instrument aimed at mitigating the joint exposure of energy-linked commitments to random price and volume delivery fluctuations. We formulate a functional optimization problem over a set of regular payoff functions: one is written on energy price, while the other is issued over any index exhibiting statistical correlation to volumetric load. On theoretical grounds, we derive closed-form expressions for both payoff structures under suitable conditions about the statistical properties of the underlying variables; we pursue analytical computations in the context of a lognormal market model and deliver explicit formulas for the optimal derivative instruments. On practical grounds, we first develop a comparative analysis of model output through simulation experiments; next, we perform an empirical study based on data quoted at EPEX SPOT power market. Our results suggest that combined price-volume hedging performance improves along with an increase of the correlation between load and index values. This outcome paves the way for a new class of effective strategies for managing volumetric risk upon extreme temperature waves.

Evaluation of realized multi-power variations in minimum variance hedging

This study investigated the hedging performance of realized multi-power variations under minimum variance strategy. The minimum variance hedge ratios are estimated by the realized DCC-GARCH model, and the risk and utility metrics are used to evaluate the performances of long and short hedge. The empirical results derived from the S&P 500 index demonstrated that the realized DCC-GARCH model with realized tri-power variation outperforms others in reducing risks, and generates largest economic benefits. While considering transaction costs, the superiority of the realized DCC-GARCH model with realized multi-power variations persists and produced less rebalancing costs than the realized DCC-GARCH model with realized variance.

An optimal combination of risk-return and naive hedging

Taking an approach contrary to the mean–variance portfolio, recent studies have appealed to an older wisdom, “the naive rule provides the best solution,” to improve out-of-sample performance in portfolio selection. Naive diversification, which invests equally across risky assets, is such an example of this simple rule. Previous studies also show that a portfolio combining naive diversification with the mean–variance strategy based on minimizing expected quadratic utility losses may show strong out-of-sample performance. Using the mean squared error, this paper derives an optimal combination of nonstochastic allocation and the mean–variance portfolio. We find that this design is equivalent to the combination of the naive rule and mean–variance strategy based on minimizing the expected utility losses. As an application of this finding, we propose a regression-based combination of maximal risk-return hedging and naive hedging. Our illustration also shows out-of-sample performance of a combined hedging that is superior to that of other methods.

Rand Hedge as an Investment Strategy on the JSE

This study examines the performance of Rand Hedge shares on the Johannesburg Stock Exchange (JSE) and investigates the impact of fluctuations in the dollar-Rand exchange rate (hereafter USDZAR) using a risk-based framework. The exchange rate beta is used as the sole determinant for classifying shares into three portfolios namely, Rand Hedge, Rand Neutral and Rand Tracker. Over the period January 1996 to December 2013, Rand Hedge shares typically underperformed Rand Neutral and Rand Tracker shares. Therefore, from a pure investment perspective, a Rand Hedge investment strategy does not offer significant alpha. However, the results of the time series regressions and vector auto-regression (VAR) analysis indicate that Rand Hedge shares hedge against exchange rate shocks and significant Rand depreciations. As a result, Rand Hedge shares offer investors protection from currency-induced tail-risk, thereby mitigating extreme currency devaluations. This suggests that the pattern of returns realised by the Rand Hedge portfolio are driven by a currency specific risk factor.

Large-Scale Empirical Tests of the Markov Tree Model

The Markov Tree model is a discrete-time option pricing model that accounts for short-term memory of the underlying asset. In this work, we compare the empirical performance of the Markov Tree model against that of the Black-Scholes model and Heston’s stochastic volatility model. Leveraging a total of five years of individual equity and index option data, and using three new methods for fitting the Markov Tree model, we find that the Markov Tree model makes smaller out-of-sample hedging errors than competing models. This comparison includes versions of Markov Tree and Black-Scholes models in which volatilities are strike- and maturity-dependent. Visualizing the errors over time, we find that the Markov Tree model yields more accurate and less risky single instrument hedges than Heston’s stochastic volatility model. A statistical resampling method indicates that the Markov Tree model’s superior hedging performance is due to its robustness with respect to noise in option data.

Hedging Barrier Options in GARCH Models with Transaction Costs

This study proposes a modified strike‐spread method for hedging barrier options in generalized autoregressive conditional heteroskedasticity (GARCH) models with transaction costs. A simulation study was conducted to investigate the hedging performance of the proposed method in comparison with several well‐known static methods for hedging barrier options. An accurate, easy‐to‐implement and fast scheme for generating the first passage time under the GARCH framework which enhances the accuracy and efficiency of the simulation is also proposed. Simulation results and an empirical study using real data indicate that the proposed approach has a promising performance for hedging barrier options in GARCH models when transaction costs are taken into consideration.

An Empirical Study on Hedge Performance, Lead-lag and the Effectiveness of Trading Volume of US dollar Futures Market

An Empirical Study on Hedge Performance, Lead-lag and the Effectiveness of Trading Volume of US dollar Futures Market

Performance of utility based hedges

Hedgers as investors are concerned with both risk and return. However when measuring hedging performance, the role of returns and investor risk aversion has generally been neglected in the literature, by its focus on minimum variance hedging. In this paper we address this by using utility based performance metrics to evaluate the hedging effectiveness of utility based hedges for hedgers with both moderate and high risk aversion together with the more traditional minimum variance approach. To examine this for an energy hedger, we apply our approach to WTI Crude Oil, for three different hedging horizons, daily, weekly and monthly. We find significant differences between the minimum variance and utility based hedging strategies in-sample for all frequencies. However performance differentials between the different strategies are small and not economically significant. Out-of-sample results support these findings across all frequencies.

Specification tests of calibrated option pricing models

In spite of the popularity of model calibration in finance, empirical researchers have put more emphasis on model estimation than on the equally important goodness-of-fit problem. This is due partly to the ignorance of modelers, and more to the ability of existing statistical tests to detect specification errors. In practice, models are often calibrated by minimizing a loss function of the differences between the modeled and actual observations. Under this approach, it is challenging to disentangle model error from estimation error in the residual series. To circumvent the difficulty, we study an alternative way of estimating the model by exact calibration. Unlike the error minimization approach, all information about dynamic misspecifications is channeled to the parameter estimation residuals under exact calibration. In the context of option pricing, we illustrate that standard time series tests are powerful in detecting various kinds of dynamic misspecifications. Compared to the error minimization approach, exact calibration yields more reasonable model comparison result, and delivers more accurate hedging performance that is robust to both gradual and abrupt structural shifts of state variables.

Hedging Price Risk in the Presence of Quantity Risk

This research looks into hedge strategies to resolve foreign exchange-related risks, generated when investing in overseas financial assets, as an example of quantity risk. If an investor has information with no uncertainty over the volume and there is only a price risk he want to hedge, an investor will be able to reduce or eliminate risks by using relative derivative securities such as forwards or futures contracts. However, if there are uncertainties over the volume of hedging targets that is called quantity risk, it is impossible to set the optimal hedge ratio with the traditional method without considering the presence of quantity risk. In this paper, we theoretically draw an optimal hedge ratio which is estimated via minimal variance criterion under static hedge structure. We also analyze its hedge performance and the impact of change in covariance on the optimal hedge ratio and variance of investment return denominated as its own country currency. For theoretical approach, we review the impact that overseas financial assets’ yield and exchanges rates distribution will have on optimal hedge ratio through simple numerical analysis. Empirical analysis is carried out by using the stock indices of the U.S., Europe and Asian countries, and the results indicate that hedge strategies taken with quantity risk for all markets produced better hedging performance than the strategies taken without quantity risk. Since there is a need for systematic research on risks involving foreign exchanges that occur in the event of foreign investments aimed to develop the domestic financial industry, we hope that our research serve as a stepping-stone for further research.

Robust hedging performance and volatility risk in option markets: Application to Standard and Poor's 500 and Taiwan index options

We investigate daily robust hedging performance with trading costs for markets of Standard and Poor's (S&P) 500 Index options (SPX) and Taiwan index options (TXO). In addition, we conduct a theoretical analysis to cope with the price limit constraint in TXO. Robust hedging refers to minimal model dependence on a risky asset price. Two hedging categories, including “Model-Free” and “Volatility-Model-Free,” are investigated, and nonparametric methods for volatility estimation are considered in our empirical study. In particular, instantaneous volatility is estimated by a proposed nonlinear correction scheme of the Fourier transform method (Malliavin & Mancino, 2009), justified by a simulation study for a local volatility model. We found an asymmetric phenomenon in hedging performance. Hedging portfolios constructed from the “Volatility-Model-Free” category were found to induce much higher Sharpe ratios than those from the “Model-Free” category on SPX, while they were found to perform comparably on TXO. Motivated from the price limit regulation in Taiwan, we further develop a time-scale change method to explain this phenomenon. Asymptotic moment estimates of differences in some hedging portfolios are consistent with our empirical findings.