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Journal articles on the topic 'Hedging models'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

BRODÉN, MATS, and PETER TANKOV. "TRACKING ERRORS FROM DISCRETE HEDGING IN EXPONENTIAL LÉVY MODELS." International Journal of Theoretical and Applied Finance 14, no. 06 (2011): 803–37. http://dx.doi.org/10.1142/s0219024911006760.

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We analyze the errors arising from discrete readjustment of the hedging portfolio when hedging options in exponential Lévy models, and establish the rate at which the expected squared error goes to zero when the readjustment frequency increases. We compare the quadratic hedging strategy with the common market practice of delta hedging, and show that for discontinuous option pay-offs the latter strategy may suffer from very large discretization errors. For options with discontinuous pay-offs, the convergence rate depends on the underlying Lévy process, and we give an explicit relation between the rate and the Blumenthal-Getoor index of the process.

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2

Di Tella, Paolo, Martin Haubold, and Martin Keller-Ressel. "Semi-static variance-optimal hedging in stochastic volatility models with Fourier representation." Journal of Applied Probability 56, no. 3 (2019): 787–809. http://dx.doi.org/10.1017/jpr.2019.41.

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AbstractWe introduce variance-optimal semi-static hedging strategies for a given contingent claim. To obtain a tractable formula for the expected squared hedging error and the optimal hedging strategy we use a Fourier approach in a multidimensional factor model. We apply the theory to set up a variance-optimal semi-static hedging strategy for a variance swap in the Heston model, which is affine, in the 3/2 model, which is not, and in a market model including jumps.

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3

El Euch, Omar, and Mathieu Rosenbaum. "Perfect hedging in rough Heston models." Annals of Applied Probability 28, no. 6 (2018): 3813–56. http://dx.doi.org/10.1214/18-aap1408.

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4

Y. Uppal, Jamshed, and Syeda Rabab Mudakkar. "Mitigating Vulnerability to Oil Price Risk— Applicability of Risk Models to Pakistan’s Energy Problem." Pakistan Development Review 53, no. 3 (2014): 293–308. http://dx.doi.org/10.30541/v53i3pp.293-308.

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The paper examines the prospects of reducing the price risk of Pakistan’s oil imports through hedging in the oil futures market. The paper evaluates the ex-ante cross hedge strategies over the 1990–2013 period using 1–4 months futures NYMEX in order to see how to reduce price risk? Our results indicate that in all cases except one, ex-ante hedging would have been effective in reducing price risk. We provide quantitative estimates of the return/risk tradeoffs from hedging Pakistan’s oil imports, and find that futures hedging offers the country significant risk-reduction potential. Keywords: Risk-return Trade-off, Hedging, Oil Prices JEL Classification: G100, G130

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5

HELL, PHILIPP, THILO MEYER-BRANDIS, and THORSTEN RHEINLÄNDER. "CONSISTENT FACTOR MODELS FOR TEMPERATURE MARKETS." International Journal of Theoretical and Applied Finance 15, no. 04 (2012): 1250027. http://dx.doi.org/10.1142/s0219024912500276.

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We propose an approach for pricing and hedging weather derivatives based on including forward looking information about the temperature available to the market. This is achieved by modeling temperature forecasts by a finite dimensional factor model. Temperature dynamics are then inferred in the short end. In analogy to interest rate theory, we establish conditions which guarantee consistency of a factor model with the martingale dynamics of temperature forecasts. Finally, we consider a specific two-factor model and examine in more detail pricing and hedging of weather derivatives in this context.

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6

Horvath, Blanka, Josef Teichmann, and Žan Žurič. "Deep Hedging under Rough Volatility." Risks 9, no. 7 (2021): 138. http://dx.doi.org/10.3390/risks9070138.

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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.

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7

Liu, Qingfu, Michael T. Chng, and Dongxia Xu. "Hedging Industrial Metals With Stochastic Volatility Models." Journal of Futures Markets 34, no. 8 (2014): 704–30. http://dx.doi.org/10.1002/fut.21671.

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8

Biagini, Francesca, Paolo Guasoni, and Maurizio Pratelli. "Mean-Variance Hedging for Stochastic Volatility Models." Mathematical Finance 10, no. 2 (2000): 109–23. http://dx.doi.org/10.1111/1467-9965.00084.

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9

Kallsen, Jan, and Richard Vierthauer. "Quadratic hedging in affine stochastic volatility models." Review of Derivatives Research 12, no. 1 (2009): 3–27. http://dx.doi.org/10.1007/s11147-009-9034-5.

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10

Augustyniak, Maciej, Alexandru Badescu, and Zhiyu Guo. "Lattice-based hedging schemes under GARCH models." Quantitative Finance 21, no. 5 (2021): 697–710. http://dx.doi.org/10.1080/14697688.2020.1865559.

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11

Kallsen, Jan, and Arnd Pauwels. "Variance-Optimal Hedging in General Affine Stochastic Volatility Models." Advances in Applied Probability 42, no. 01 (2010): 83–105. http://dx.doi.org/10.1017/s000186780000392x.

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We consider variance-optimal hedging in general continuous-time affine stochastic volatility models. The optimal hedge and the associated hedging error are determined semiexplicitly in the case that the stock price follows a martingale. The integral representation of the solution opens the door to efficient numerical computation. The setup includes models with jumps in the stock price and in the activity process. It also allows for correlation between volatility and stock price movements. Concrete parametric models will be illustrated in a forthcoming paper.

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12

Kallsen, Jan, and Arnd Pauwels. "Variance-Optimal Hedging in General Affine Stochastic Volatility Models." Advances in Applied Probability 42, no. 1 (2010): 83–105. http://dx.doi.org/10.1239/aap/1269611145.

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We consider variance-optimal hedging in general continuous-time affine stochastic volatility models. The optimal hedge and the associated hedging error are determined semiexplicitly in the case that the stock price follows a martingale. The integral representation of the solution opens the door to efficient numerical computation. The setup includes models with jumps in the stock price and in the activity process. It also allows for correlation between volatility and stock price movements. Concrete parametric models will be illustrated in a forthcoming paper.

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13

ROUX, ALET. "PRICING AND HEDGING GAME OPTIONS IN CURRENCY MODELS WITH PROPORTIONAL TRANSACTION COSTS." International Journal of Theoretical and Applied Finance 19, no. 07 (2016): 1650043. http://dx.doi.org/10.1142/s0219024916500436.

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The pricing, hedging, optimal exercise and optimal cancellation of game or Israeli options are considered in a multi-currency model with proportional transaction costs. Efficient constructions for optimal hedging, cancellation and exercise strategies are presented, together with numerical examples, as well as probabilistic dual representations for the bid and ask price of a game option.

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14

LIU, WEN-QIONG, and WEN-LI HUANG. "HEDGING OF SYNTHETIC CDO TRANCHES WITH SPREAD AND DEFAULT RISK BASED ON A COMBINED FORECASTING APPROACH." International Journal of Theoretical and Applied Finance 22, no. 02 (2019): 1850057. http://dx.doi.org/10.1142/s0219024918500577.

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Hedging of credit derivatives, especially the Collateralized Debt Obligations (CDOs), is the prerequisite of risk management in financial market. Since both spread risk and default risk exist, the models in existing literature resort to the incomplete-market theory to derive the hedging strategies. From another point of view, the construction of hedging strategies of CDO might be regarded as the process of forecasting the changes in value of CDO by the changes in value of hedging instruments. Based on this idea, this paper proposes an alternative hedging approach via the combined forecasting and regression techniques, where the two individual forecasting models are Gaussian copula model and local intensity model, used to hedge against spread risk and default risk, respectively. Finally, the dynamic hedge ratios of CDO tranches with CDS index are derived. A numerical analysis is carried out and the hedge ratios obtained by the new models are compared with those from actual market spreads. It is shown that the model derived in this paper not only provides hedging strategies which agree with the market hedge ratios but that can be effectively implemented as well.

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15

Matsumoto, Koichi. "Mean–variance hedging with model risk." International Journal of Financial Engineering 04, no. 04 (2017): 1750042. http://dx.doi.org/10.1142/s2424786317500426.

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This paper studies a hedging problem of a derivative security in a one-period model when there is the model risk. The hedging error is measured by a quadratic criterion. The model risk means that the true model is uncertain and there are many candidates for the true model. The true model is assumed to be in a set of models. We study an optimal strategy which minimizes the worst-case hedging error over all models in the set. We show how to calculate an optimal strategy and the minimum hedging error effectively. Finally we give some numerical examples to demonstrate the usefulness of our method.

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16

ANKIRCHNER, STEFAN, CHRISTIAN PIGORSCH, and NIKOLAUS SCHWEIZER. "ESTIMATING RESIDUAL HEDGING RISK WITH LEAST-SQUARES MONTE CARLO." International Journal of Theoretical and Applied Finance 17, no. 07 (2014): 1450042. http://dx.doi.org/10.1142/s0219024914500423.

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Frequently, dynamic hedging strategies minimizing risk exposure are not given in closed form, but need to be approximated numerically. This makes it difficult to estimate residual hedging risk, also called basis risk, when only imperfect hedging instruments are at hand. We propose an easy to implement and computationally efficient least-squares Monte Carlo algorithm to estimate residual hedging risk. The algorithm approximates the variance minimal hedging strategy within general diffusion models. Moreover, the algorithm produces both high-biased and low-biased estimators for the residual hedging error variance, thus providing an intrinsic criterion for the quality of the approximation. In a number of examples we show that the algorithm delivers accurate hedging error characteristics within seconds.

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17

De Jong, Abe, Frans De Roon, and Chris Veld. "Out-of-sample hedging effectiveness of currency futures for alternative models and hedging strategies." Journal of Futures Markets 17, no. 7 (1997): 817–37. http://dx.doi.org/10.1002/(sici)1096-9934(199710)17:7<817::aid-fut5>3.0.co;2-q.

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18

Lee, Cheng-Few, Kehluh Wang, and Yan Long Chen. "Hedging and Optimal Hedge Ratios for International Index Futures Markets." Review of Pacific Basin Financial Markets and Policies 12, no. 04 (2009): 593–610. http://dx.doi.org/10.1142/s0219091509001769.

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This empirical study utilizes four static hedging models (OLS Minimum Variance Hedge Ratio, Mean-Variance Hedge Ratio, Sharpe Hedge Ratio, and MEG Hedge Ratio) and one dynamic hedging model (bivariate GARCH Minimum Variance Hedge Ratio) to find the optimal hedge ratios for Taiwan Stock Index Futures, S&P 500 Stock Index Futures, Nikkei 225 Stock Index Futures, Hang Seng Index Futures, Singapore Straits Times Index Futures, and Korean KOSPI 200 Index Futures. The effectiveness of these ratios is also evaluated. The results indicate that the methods of conducting optimal hedging in different markets are not identical. However, the empirical results confirm that stock index futures are effective direct hedging instruments, regardless of hedging schemes or hedging horizons.

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19

Kuen Siu, Tak, Roy Nawar, and Christian-Oliver Ewald. "Hedging crude oil derivatives in GARCH-type models." Journal of Energy Markets 7, no. 1 (2014): 3–26. http://dx.doi.org/10.21314/jem.2014.105.

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20

Albrecher, Hansjörg, Jan Dhaene, Marc Goovaerts, and Wim Schoutens. "Static Hedging of Asian Options under Lévy Models." Journal of Derivatives 12, no. 3 (2005): 63–72. http://dx.doi.org/10.3905/jod.2005.479381.

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21

Badescu, Alexandru, Robert J. Elliott, and Juan-Pablo Ortega. "Quadratic hedging schemes for non-Gaussian GARCH models." Journal of Economic Dynamics and Control 42 (May 2014): 13–32. http://dx.doi.org/10.1016/j.jedc.2014.03.001.

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22

Lai, YiHao, Cathy W. S. Chen, and Richard Gerlach. "Optimal dynamic hedging via copula-threshold-GARCH models." Mathematics and Computers in Simulation 79, no. 8 (2009): 2609–24. http://dx.doi.org/10.1016/j.matcom.2008.12.010.

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23

Adviti, Hyungsok Ahn, and Glen Swindle. "Misspecified asset price models and robust hedging strategies." Applied Mathematical Finance 4, no. 1 (1997): 21–36. http://dx.doi.org/10.1080/135048697334818.

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24

Ahn, Hyungsok, Adviti Muni, and Glen Swindle. "Optimal hedging strategies for misspecified asset price models." Applied Mathematical Finance 6, no. 3 (1999): 197–208. http://dx.doi.org/10.1080/135048699334537.

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25

Lai, Yu-Sheng. "Evaluating the hedging performance of multivariate GARCH models." Asia Pacific Management Review 24, no. 1 (2019): 86–95. http://dx.doi.org/10.1016/j.apmrv.2018.07.003.

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26

Topaloglou, Nikolas, Hercules Vladimirou, and Stavros A. Zenios. "Integrated dynamic models for hedging international portfolio risks." European Journal of Operational Research 285, no. 1 (2020): 48–65. http://dx.doi.org/10.1016/j.ejor.2019.01.027.

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27

Kavak, Yasin, Erkut Erdem, and Aykut Erdem. "Hedging static saliency models to predict dynamic saliency." Signal Processing: Image Communication 81 (February 2020): 115694. http://dx.doi.org/10.1016/j.image.2019.115694.

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28

Nian, Ke, Thomas F. Coleman, and Yuying Li. "Learning sequential option hedging models from market data." Journal of Banking & Finance 133 (December 2021): 106277. http://dx.doi.org/10.1016/j.jbankfin.2021.106277.

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29

Bhat, Aparna Prasad. "An empirical exploration of the performance of alternative option pricing models." Journal of Indian Business Research 11, no. 1 (2019): 23–49. http://dx.doi.org/10.1108/jibr-04-2018-0114.

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PurposeThe purpose of this paper is to ascertain the effectiveness of major deterministic and stochastic volatility-based option pricing models in pricing and hedging exchange-traded dollar–rupee options over a five-year period since the launch of these options in India.Design/methodology/approachThe paper examines the pricing and hedging performance of five different models, namely, the Black–Scholes–Merton model (BSM), skewness- and kurtosis-adjusted BSM, NGARCH model of Duan, Heston’s stochastic volatility model and anad hocBlack–Scholes (AHBS) model. Risk-neutral structural parameters are extracted by calibrating each model to the prices of traded dollar–rupee call options. These parameters are used to generate out-of-sample model option prices and to construct a delta-neutral hedge for a short option position. Out-of-sample pricing errors and hedging errors are compared to identify the best-performing model. Robustness is tested by comparing the performance of all models separately over turbulent and tranquil periods.FindingsThe study finds that relatively simpler models fare better than more mathematically complex models in pricing and hedging dollar–rupee options during the sample period. This superior performance is observed to persist even when comparisons are made separately over volatile periods and tranquil periods. However the more sophisticated models reveal a lower moneyness-maturity bias as compared to the BSM model.Practical implicationsThe study concludes that incorporation of skewness and kurtosis in the BSM model as well as the practitioners’ approach of using a moneyness-maturity-based volatility within the BSM model (AHBS model) results in better pricing and hedging effectiveness for dollar–rupee options. This conclusion has strong practical implications for market practitioners, hedgers and regulators in the light of increased volatility in the dollar–rupee pair.Originality/valueExisting literature on this topic has largely centered around either US equity index options or options on major liquid currencies. While many studies have solely focused on the pricing performance of option pricing models, this paper examines both the pricing and hedging performance of competing models in the context of Indian currency options. Robustness of findings is tested by comparing model performance across periods of stress and tranquility. To the best of the author’s knowledge, this paper is one of the first comprehensive studies to focus on an emerging market currency pair such as the dollar–rupee.

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30

Lahouel, Noureddine, and Slaheddine Hellara. "Improving the option pricing performance of GARCH models in inefficient market." Investment Management and Financial Innovations 17, no. 2 (2020): 14–25. http://dx.doi.org/10.21511/imfi.17(2).2020.02.

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Understanding the relation between option pricing and market efficiency is important. Indeed, emphasizing this relation generates new insights that are appropriate in practice. These insights give a better understanding of the current limitations of the option pricing and hedging methods. This article thus aims to improve the performance of the option pricing approach. To start, the relation between the option pricing methodology and the informational market efficiency was discussed. It is, therefore, useful, before proceeding to apply the standard risk-neutral approach, to check the efficiency assumption. New modified GARCH processes were used to model the dynamics of the asset returns in the option pricing framework. The new considered approaches allow describing the dynamic of returns when the market is inefficient. Using real data on CAC 40 index, the performance of different models as a function of maturity and moneyness was studied. The in-sample analysis, interested in the stability of the pricing models across time, showed that the new approach, developed under the affine GARCH process, is the most accurate. The study of the out-of-sample performance, which aims to evaluate the forecasting ability of different approaches, confirmed the results of the in-sample analysis. For the optional portfolio hedging, always the best hedging approach is that obtained under the affine GARCH model. After a regression study, it was found that the difference between theoretical and observed option values can be explained by factors, which are not taken into account in the proposed pricing formulae.

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31

RODRÍGUEZ, JESÚS F. "HEDGING SWING OPTIONS." International Journal of Theoretical and Applied Finance 14, no. 02 (2011): 295–312. http://dx.doi.org/10.1142/s021902491100636x.

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We study models for electricity pricing and derivatives in the context of a deregulated market setting. In particular we value swing options, since these are the electricity derivatives that attract the most attention from market participants. These are American style options in that they allow for multiple exercises subject to a set of constraints on the consumption process. Through the use of a penalty function, we generalize the problem by allowing for the consumption restrictions to be broken. We characterize the price function as a stochastic optimal control problem, and show that the option is exercised in a bang-bang fashion. The value of the swing option is the solution to a backward stochastic differential equation, and we show how European calls, along with forward contracts, can be used to hedge them.

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32

Kharbanda, Varuna, and Archana Singh. "Hedging and effectiveness of Indian currency futures market." Journal of Asia Business Studies 14, no. 5 (2020): 581–97. http://dx.doi.org/10.1108/jabs-10-2018-0279.

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Purpose The purpose of this paper is to measure the effectiveness of the hedging with futures currency contracts. Measuring the effectiveness of hedging has become mandatory for Indian companies as the new Indian accounting standards, Ind-AS, specify that the effectiveness of hedges taken by the companies should be evaluated using quantitative methods but leaves it to the company to choose a method of evaluation. Design/methodology/approach The paper compares three models for evaluating the effectiveness of hedge – ordinary least square (OLS), vector error correction model (VECM) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH) model. The OLS and VECM are the static models, whereas DCC-MGARCH is a dynamic model. Findings The overall results of the study show that dynamic model (DCC-MGARCH) is a better model for calculating the hedge effectiveness as it outperforms OLS and VECM models. Practical implications The new Indian accounting standards (Ind-AS) mandates the calculation of hedge effectiveness. The results of this study are useful for the treasurers in identifying appropriate method for evaluation of hedge effectiveness. Similarly, policymakers and auditors are benefitted as the study provides clarity on different methods of evaluation of hedging effectiveness. Originality/value Many previous studies have evaluated the efficiency of the Indian currency futures market, but with rising importance of hedging in the Indian companies, Reserve Bank of India’s initiatives and encouragement for the use of futures for hedging the currency risk and now the mandatory accounting requirement for measuring hedging effectiveness, it has become more relevant to evaluate the effectiveness of hedge. To the authors’ best knowledge, this is one of the first few papers which evaluate the effectiveness of the currency future hedging.

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33

Sun, Youfa, George Yuan, Shimin Guo, Jianguo Liu, and Steven Yuan. "Does model misspecification matter for hedging? A computational finance experiment based approach." International Journal of Financial Engineering 02, no. 03 (2015): 1550023. http://dx.doi.org/10.1142/s2424786315500231.

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To assess whether the model misspecification matters for hedging accuracy, we carefully select six increasingly complicated asset models, i.e., the Black–Scholes (BS) model, the Merton (M) model, the Heston (H) model, the Heston jump-diffusion (HJ) model, the double Heston (dbH) model and the double Heston jump-diffusion (dbHJ) model, and then impartially evaluate their performances in mitigating the risk of an option, under a controllable experimental market. In experiments, the ℙ measure asset paths are piecewisely simulated by a hybrid-model (including the Black–Scholes-type and the (double) Heston-type, with or without jump-diffusion term) with randomly given properly defined parameters. We access the hedging accuracy of six models within the operational dynamic hedging framework proposed by sun (2015), and apply the Fourier-COS-expansion method (i.e., the COS formula, Fang and Oosterlee (2008) to price options and to calculate the Greeks). Extensive numerical results indicate that the model misspecification shows no significant impact on hedging accuracy, but the market fit does matter critically for hedging.

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34

VON HAMMERSTEIN, ERNST AUGUST, EVA LÜTKEBOHMERT, LUDGER RÜSCHENDORF, and VIKTOR WOLF. "OPTIMALITY OF PAYOFFS IN LÉVY MODELS." International Journal of Theoretical and Applied Finance 17, no. 06 (2014): 1450041. http://dx.doi.org/10.1142/s0219024914500411.

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In this paper, we determine the lowest cost strategy for a given payoff in Lévy markets where the pricing is based on the Esscher martingale measure. In particular, we consider Lévy models where prices are driven by a normal inverse Gaussian (NIG)- or a variance Gamma (VG)-process. Explicit solutions for cost-efficient strategies are derived for a variety of vanilla options, spreads, and forwards. Applications to real financial market data show that the cost savings associated with these strategies can be quite substantial. The empirical findings are supplemented by a result that relates the magnitude of these savings to the strength of the market trend. Moreover, we consider the problem of hedging efficient claims, derive explicit formulas for the deltas of efficient calls and puts and apply the results to German stock market data. Using the time-varying payoff profile of efficient options, we further develop alternative delta hedging strategies for vanilla calls and puts. We find that the latter can provide a more accurate way of replicating the final payoff compared to their classical counterparts.

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35

ISSAKA, AZIZ. "VALUATION, HEDGING, AND BOUNDS OF SWAPS UNDER MULTI-FACTOR BNS-TYPE STOCHASTIC VOLATILITY MODELS." Annals of Financial Economics 15, no. 02 (2020): 2050007. http://dx.doi.org/10.1142/s2010495220500074.

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In this paper, we consider price weighted-volatility swap and price weighted-variance swap. The underlying asset considered in this paper is assumed to follow a general stochastic differential equation and exhibits stochastic volatility. We obtain analytical pricing formulas for the weighted-variance swap and approximate expression for the weighted-volatility swap. Nice bounds for the arbitrage-free variance swap price are also found. The proposed pricing formulas are easy to implement in real time and can be applied efficiently for practical applications. We consider the problem of hedging volatility swap with variance swap and obtain analytical formula for the hedge ratio. We also consider a problem of hedging an asset with variance swap and option. We determined the optimal amount of the underlying asset that has to be held for minimizing the hedging error by taking positions in options and weighted-variance swap. A numerical example is also provided.

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36

OBŁÓJ, JAN, and FRÉDÉRIK ULMER. "PERFORMANCE OF ROBUST HEDGES FOR DIGITAL DOUBLE BARRIER OPTIONS." International Journal of Theoretical and Applied Finance 15, no. 01 (2012): 1250003. http://dx.doi.org/10.1142/s0219024911006516.

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We analyze the performance of robust hedging strategies of digital double barrier options of Cox and Obłój (2011) against that of traditional hedging methods such as delta and delta/vega hedging. Digital double barrier options are financial derivative contracts which pay out a fixed amount on the condition that the underlying asset remains within or breaks into a range defined by two distinct barrier levels. We perform the analysis in hypothetical forward markets driven by models with stochastic volatility and jumps, calibrated to the AUD/USD foreign exchange rate market. Our findings are strikingly unanimous and suggest that, in the presence of model uncertainty and/or transaction costs, robust hedging strategies typically outperform in a substantial way model-specific hedging methods.

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37

OHSAKI, SHUICHI, and AKIRA YAMAZAKI. "STATIC HEDGING OF DEFAULTABLE CONTINGENT CLAIMS: A SIMPLE HEDGING SCHEME ACROSS EQUITY AND CREDIT MARKETS." International Journal of Theoretical and Applied Finance 14, no. 02 (2011): 239–64. http://dx.doi.org/10.1142/s0219024911006383.

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This paper proposes a simple scheme for static hedging of defaultable contingent claims. It generalizes the techniques developed by Carr and Chou (1997), Carr and Madan (1998), and Takahashi and Yamazaki (2009a) to credit-equity models. Our scheme provides a hedging strategy across credit and equity markets, where suitable defaultable contingent claims are accurately replicated by a feasible number of plain vanilla equity options. Another point is that shorter maturity options are available to hedge longer maturity defaultable contingent claims. Through numerical examples, it is shown that the scheme is applicable to both structural and intensity-based models.

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38

Stilger, Przemyslaw S., Ngoc Quynh Anh Nguyen, and Tri Minh Nguyen. "Empirical performance of stochastic volatility option pricing models." International Journal of Financial Engineering 08, no. 01 (2021): 2050056. http://dx.doi.org/10.1142/s2424786320500565.

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This paper examines the empirical performance of four stochastic volatility option pricing models: Heston, Heston[Formula: see text], Bates and Heston–Hull–White. To compare these models, we use individual stock options data from January 1996 to August 2014. The comparison is made with respect to pricing and hedging performance, implied volatility surface and risk-neutral return distribution characteristics, as well as performance across industries and time. We find that the Heston model outperforms the other models in terms of in-sample pricing, whereas Heston[Formula: see text] model outperforms the other models in terms of out-of-sample hedging. This suggests that taking jumps or stochastic interest rates into account does not improve the model performance after accounting for stochastic volatility. We also find that the model performance deteriorates during the crises as well as when the implied volatility surface is steep in the maturity or strike dimension.

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ALGHALITH, MOAWIA, and WING-KEUNG WONG. "WELFARE GAINS FROM MACRO-HEDGING." Annals of Financial Economics 15, no. 02 (2020): 2050009. http://dx.doi.org/10.1142/s2010495220500098.

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Macro-hedging is one of the most important issues in hedging, but there are very few studies on the welfare impact of macro-hedging. To bridge a gap in the literature of macro-hedging, this paper introduces a method that generalizes and extends existing models of macro-hedging in several significant ways. We first assume the existence of basis risk in a small country to hedge in futures markets instead of forward contracts and relax the full-hedging assumption. We use the quantity being hedged in futures contracts as a decision variable. We also relax the restrictive assumption regarding the form of the spot price. We then derive the formula to estimate the welfare gain which can be easily implemented in any empirical case. In contrast to quasi-simulation being used in some existing approaches, our proposed method can be used for any real data, including future data, but existing methods in the literature cannot. Our approach is for investors for their investment decision-making when they use macro-hedging as their trading strategy.

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40

Cao, Lingyan, and Zheng-Feng Guo. "Analysis of Hedging Profits Under Two Stock Pricing Models." Journal of Mathematical Finance 01, no. 03 (2011): 120–24. http://dx.doi.org/10.4236/jmf.2011.13015.

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Goutte, Stéphane. "Pricing and Hedging in Stochastic Volatility Regime Switching Models." Journal of Mathematical Finance 03, no. 01 (2013): 70–80. http://dx.doi.org/10.4236/jmf.2013.31006.

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42

Tankov, Peter, and Ekaterina Voltchkova. "Asymptotic analysis of hedging errors in models with jumps." Stochastic Processes and their Applications 119, no. 6 (2009): 2004–27. http://dx.doi.org/10.1016/j.spa.2008.10.002.

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Li, Jing, Lingfei Li, and Gongqiu Zhang. "Pure jump models for pricing and hedging VIX derivatives." Journal of Economic Dynamics and Control 74 (January 2017): 28–55. http://dx.doi.org/10.1016/j.jedc.2016.11.001.

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Topaloglou, Nikolas, Hercules Vladimirou, and Stavros A. Zenios. "CVaR models with selective hedging for international asset allocation." Journal of Banking & Finance 26, no. 7 (2002): 1535–61. http://dx.doi.org/10.1016/s0378-4266(02)00289-3.

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Nawalkha, Sanjay K., Gloria M. Soto, and Jun Zhang. "Generalized M-vector models for hedging interest rate risk." Journal of Banking & Finance 27, no. 8 (2003): 1581–604. http://dx.doi.org/10.1016/s0378-4266(03)00089-x.

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Laurent, J. P., A. Cousin, and J. D. Fermanian. "Hedging default risks of CDOs in Markovian contagion models." Quantitative Finance 11, no. 12 (2010): 1773–91. http://dx.doi.org/10.1080/14697680903390126.

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47

Louhichi, Waël, and Hassen Rais. "Refinement of the hedging ratio using copula-GARCH models." Journal of Asset Management 20, no. 5 (2019): 403–11. http://dx.doi.org/10.1057/s41260-019-00133-5.

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Ivanov, R. V., and A. N. Shiryaev. "On Duality Principle for Hedging Strategies in Diffusion Models." Theory of Probability & Its Applications 56, no. 3 (2012): 376–402. http://dx.doi.org/10.1137/s0040585x97985480.

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SOTTINEN, TOMMI, and LAURI VIITASAARI. "CONDITIONAL-MEAN HEDGING UNDER TRANSACTION COSTS IN GAUSSIAN MODELS." International Journal of Theoretical and Applied Finance 21, no. 02 (2018): 1850015. http://dx.doi.org/10.1142/s0219024918500152.

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We consider so-called regular invertible Gaussian Volterra processes and derive a formula for their prediction laws. Examples of such processes include the fractional Brownian motions and the mixed fractional Brownian motions. As an application, we consider conditional-mean hedging under transaction costs in Black–Scholes type pricing models where the Brownian motion is replaced with a more general regular invertible Gaussian Volterra process.

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50

Huang, Shih-Feng, and Chan-Yi Tsai. "Hedging Barrier Options in GARCH Models with Transaction Costs." Australian & New Zealand Journal of Statistics 57, no. 3 (2015): 301–24. http://dx.doi.org/10.1111/anzs.12120.

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