Pub Date : 2022-05-18DOI: 10.3905/jod.2022.29.4.006
Cathy J. Scott
{"title":"Practical Application of Derivatives in Asset Management","authors":"Cathy J. Scott","doi":"10.3905/jod.2022.29.4.006","DOIUrl":"https://doi.org/10.3905/jod.2022.29.4.006","url":null,"abstract":"","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"6 - 7"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49620527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Financial derivatives have been consistently stigmatized. Undoubtedly, most if not all of the negative press arises from high-profile cases of companies that lost significant amounts of money with these products—the so-called derivatives debacles. These products are unique in the sense that, unlike any other financial instrument, some critics have consistently argued for their demise. This article aims to answer the question of whether this criticism is justified from a historical perspective by analyzing the 30 largest losses caused by financial derivatives among non-financial end users from 1987 to 2017. The results of this analysis do not support most of these accusations. The data show four main patterns: (1) All the derivatives products in the sample performed as expected, and all the losses resulted from wrongly timed market strategies. No losses were attributable to operational or legal reasons. (2) In the overwhelming majority of cases, these derivatives strategies were deliberately not designed as bona fide hedges but instead were used to attain off-market rates or extraordinary returns at no upfront cost for the users (“speculative hedging”). (3) Given their academic background, most end users likely did not fully grasp the math behind the most complex strategies, but they seemed to understand the risk-return tradeoffs of their strategies because they agreed to assume additional market risks to achieve off-market rates with no upfront costs. Moreover, having a deep understanding of derivatives was not a guarantee of success. Some of the executives responsible for the largest losses in our sample have mathematical or advanced finance backgrounds. (4) Most financial managers flagrantly underestimated the likelihood of extreme market events. In general, these losses happened after a period of persistent market trends. Financial managers therefore believed that recent market trends would continue for the foreseeable future and overlooked the risk of extreme and unexpected market moves.
{"title":"50 Years On: Are Derivatives a “Product from Hell”? Historical Perspectives on 30 Cases of Derivatives Losses","authors":"J. Seoane","doi":"10.3905/jod.2022.1.162","DOIUrl":"https://doi.org/10.3905/jod.2022.1.162","url":null,"abstract":"Financial derivatives have been consistently stigmatized. Undoubtedly, most if not all of the negative press arises from high-profile cases of companies that lost significant amounts of money with these products—the so-called derivatives debacles. These products are unique in the sense that, unlike any other financial instrument, some critics have consistently argued for their demise. This article aims to answer the question of whether this criticism is justified from a historical perspective by analyzing the 30 largest losses caused by financial derivatives among non-financial end users from 1987 to 2017. The results of this analysis do not support most of these accusations. The data show four main patterns: (1) All the derivatives products in the sample performed as expected, and all the losses resulted from wrongly timed market strategies. No losses were attributable to operational or legal reasons. (2) In the overwhelming majority of cases, these derivatives strategies were deliberately not designed as bona fide hedges but instead were used to attain off-market rates or extraordinary returns at no upfront cost for the users (“speculative hedging”). (3) Given their academic background, most end users likely did not fully grasp the math behind the most complex strategies, but they seemed to understand the risk-return tradeoffs of their strategies because they agreed to assume additional market risks to achieve off-market rates with no upfront costs. Moreover, having a deep understanding of derivatives was not a guarantee of success. Some of the executives responsible for the largest losses in our sample have mathematical or advanced finance backgrounds. (4) Most financial managers flagrantly underestimated the likelihood of extreme market events. In general, these losses happened after a period of persistent market trends. Financial managers therefore believed that recent market trends would continue for the foreseeable future and overlooked the risk of extreme and unexpected market moves.","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"99 - 123"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43397244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, some insights are presented on the risks associated with trading autocallable financial products. This class of structured products survived the Lehman Brothers collapse in 2008 and the sovereign crisis of 2011 but was deeply affected by the emergence of the COVID-19 pandemic in 2020. This article highlights the important role played by dividend risk, which was neglected until 2020 in the derivatives literature on equity structured products. The article also emphasizes that both equity volatility uncertainty and dividend uncertainty play a crucial role in pricing and risk-managing autocallables. The article uses the Black-Scholes model in a Bayesian setup, demonstrating how volatility uncertainty affects the estimation of dividend yield and vice versa.
{"title":"A Bayesian View on Autocallable Pricing and Risk Management","authors":"Tommaso Paletta, R. Tunaru","doi":"10.3905/jod.2022.1.161","DOIUrl":"https://doi.org/10.3905/jod.2022.1.161","url":null,"abstract":"In this article, some insights are presented on the risks associated with trading autocallable financial products. This class of structured products survived the Lehman Brothers collapse in 2008 and the sovereign crisis of 2011 but was deeply affected by the emergence of the COVID-19 pandemic in 2020. This article highlights the important role played by dividend risk, which was neglected until 2020 in the derivatives literature on equity structured products. The article also emphasizes that both equity volatility uncertainty and dividend uncertainty play a crucial role in pricing and risk-managing autocallables. The article uses the Black-Scholes model in a Bayesian setup, demonstrating how volatility uncertainty affects the estimation of dividend yield and vice versa.","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"40 - 59"},"PeriodicalIF":0.0,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41456470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous academic research reveals that mean-variance asset pricing (MVAP) models such as the single-period capital asset pricing model (CAPM) fail to produce rational European option prices. This article shows two adaptations of MVAP models that may be used to value derivatives with nonlinear payouts. The first removes static option arbitrage in investors’ optimized aggregate portfolio selection. The second linearizes the pricing kernel, using a static version of the self-financing condition applied in dynamic option modeling. Both adaptations produce risk-neutral derivative prices in equilibrium for all finite-moment probability distributions of underlying asset prices with compact support. The derivation does not require stochastic calculus, frictionless continuous trading assumptions, or the solution of differential equations. The resulting model is a hybrid of equilibrium and arbitrage techniques that rationally values assets and derivatives.
{"title":"Simplified Option Price Derivations","authors":"D. Shimko","doi":"10.3905/jod.2022.1.160","DOIUrl":"https://doi.org/10.3905/jod.2022.1.160","url":null,"abstract":"Previous academic research reveals that mean-variance asset pricing (MVAP) models such as the single-period capital asset pricing model (CAPM) fail to produce rational European option prices. This article shows two adaptations of MVAP models that may be used to value derivatives with nonlinear payouts. The first removes static option arbitrage in investors’ optimized aggregate portfolio selection. The second linearizes the pricing kernel, using a static version of the self-financing condition applied in dynamic option modeling. Both adaptations produce risk-neutral derivative prices in equilibrium for all finite-moment probability distributions of underlying asset prices with compact support. The derivation does not require stochastic calculus, frictionless continuous trading assumptions, or the solution of differential equations. The resulting model is a hybrid of equilibrium and arbitrage techniques that rationally values assets and derivatives.","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"9 - 19"},"PeriodicalIF":0.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44048156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, the authors discuss the various approaches and issues associated with hedging with stock index futures. The most common approach used in practice is based on minimizing the variance of the hedge within the mean-variance framework to obtain the optimal hedge ratio. In determining the optimal hedge ratio, consideration must be given to the basis risk to which a fund is exposed when using stock index futures. An optimal hedge ratio based on variance minimization is the slope coefficient estimated from an ordinary least squares (OLS) regression of the returns of the portfolio to be hedged on the returns of the stock index futures contract. The estimated slope coefficient is referred to as beta. The optimal hedge ratio can be further refined by adjusting for the beta estimated from an OLS regression of the return on the underlying stock index on the return on the stock index futures. A criticism of the OLS model is twofold. The first is that there are statistical issues in estimating beta using the basic OLS regression model. Several models that employ advanced econometric techniques have been proposed for estimating hedge ratios. The second criticism is that the OLS model assumes a constant hedge ratio, despite the theoretical and empirical evidence showing the hedge ratio should be time varying. Evidence suggests that employing advanced econometric models to estimate the slope coefficient offers little improvement in hedging effectiveness—and even if there is some improvement, the modeling cost may not justify the extra effort. As for the second criticism, the well-known autoregressive conditional heteroscedasticity (ARCH) and the generalized ARCH (GARCH) have been used to allow for time-varying hedge ratios. Although some studies have reported that ARCH and GARCH can improve hedge effectiveness, the effort may not be warranted due to the additional modeling as well as the time-varying hedge ratios involving rebalancing the portfolio periodically, which can add significantly to the cost of hedging.
{"title":"A Primer on Hedging with Stock Index Futures","authors":"F. Fabozzi, F. Fabozzi","doi":"10.3905/jod.2022.1.159","DOIUrl":"https://doi.org/10.3905/jod.2022.1.159","url":null,"abstract":"In this article, the authors discuss the various approaches and issues associated with hedging with stock index futures. The most common approach used in practice is based on minimizing the variance of the hedge within the mean-variance framework to obtain the optimal hedge ratio. In determining the optimal hedge ratio, consideration must be given to the basis risk to which a fund is exposed when using stock index futures. An optimal hedge ratio based on variance minimization is the slope coefficient estimated from an ordinary least squares (OLS) regression of the returns of the portfolio to be hedged on the returns of the stock index futures contract. The estimated slope coefficient is referred to as beta. The optimal hedge ratio can be further refined by adjusting for the beta estimated from an OLS regression of the return on the underlying stock index on the return on the stock index futures. A criticism of the OLS model is twofold. The first is that there are statistical issues in estimating beta using the basic OLS regression model. Several models that employ advanced econometric techniques have been proposed for estimating hedge ratios. The second criticism is that the OLS model assumes a constant hedge ratio, despite the theoretical and empirical evidence showing the hedge ratio should be time varying. Evidence suggests that employing advanced econometric models to estimate the slope coefficient offers little improvement in hedging effectiveness—and even if there is some improvement, the modeling cost may not justify the extra effort. As for the second criticism, the well-known autoregressive conditional heteroscedasticity (ARCH) and the generalized ARCH (GARCH) have been used to allow for time-varying hedge ratios. Although some studies have reported that ARCH and GARCH can improve hedge effectiveness, the effort may not be warranted due to the additional modeling as well as the time-varying hedge ratios involving rebalancing the portfolio periodically, which can add significantly to the cost of hedging.","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"39 - 60"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44646952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Its intuitiveness and the simplicity of its calculations make the seminal Black-Scholes-Merton option pricing model the most commonly known and used among all asset pricing models ever developed. Almost half a century after it was introduced, a massive literature has been devoted, and is still being generated, to empirical testing of the original model, to developing new models addressing its original assumptions and biases, and to extending the framework of option pricing. This article presents a review of fundamental option pricing models from Black-Scholes-Merton to the present day, covering alternative option pricing approaches, including those for options on different underlying assets as well as those with different asset price and volatility dynamics. This article also reviews contemporary topics in options, including applications to novel risks such as climate-related risks and volatility risk, as well as implementation of novel methodologies from data science and machine learning.
{"title":"Option Pricing Models: From Black-Scholes-Merton to Present","authors":"Ahmet K. Karagozoglu","doi":"10.3905/jod.2022.1.158","DOIUrl":"https://doi.org/10.3905/jod.2022.1.158","url":null,"abstract":"Its intuitiveness and the simplicity of its calculations make the seminal Black-Scholes-Merton option pricing model the most commonly known and used among all asset pricing models ever developed. Almost half a century after it was introduced, a massive literature has been devoted, and is still being generated, to empirical testing of the original model, to developing new models addressing its original assumptions and biases, and to extending the framework of option pricing. This article presents a review of fundamental option pricing models from Black-Scholes-Merton to the present day, covering alternative option pricing approaches, including those for options on different underlying assets as well as those with different asset price and volatility dynamics. This article also reviews contemporary topics in options, including applications to novel risks such as climate-related risks and volatility risk, as well as implementation of novel methodologies from data science and machine learning.","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"61 - 80"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45054492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Option hedging is critical in financial risk management. The traditional methods to determine the hedging position require assumptions of a frictionless market and continuous hedging. In this article, these two assumptions are removed, and a hedging strategy based on the reinforcement learning technique is proposed. This new strategy maximizes the expectation of the present value of accounting and realized profits of the hedging portfolio while limiting the sensitivity of the hedging position to changes in the underlying asset. The performance of this method is tested on option trading data (from 2004 to 2020) for the Standard and Poor’s (S&P) 500, S&P 100, and Dow Jones Industrial Average.
{"title":"Delta-Gamma-Like Hedging with Transaction Cost under Reinforcement Learning Technique","authors":"Wei Xu, Bing Dai","doi":"10.3905/jod.2022.1.156","DOIUrl":"https://doi.org/10.3905/jod.2022.1.156","url":null,"abstract":"Option hedging is critical in financial risk management. The traditional methods to determine the hedging position require assumptions of a frictionless market and continuous hedging. In this article, these two assumptions are removed, and a hedging strategy based on the reinforcement learning technique is proposed. This new strategy maximizes the expectation of the present value of accounting and realized profits of the hedging portfolio while limiting the sensitivity of the hedging position to changes in the underlying asset. The performance of this method is tested on option trading data (from 2004 to 2020) for the Standard and Poor’s (S&P) 500, S&P 100, and Dow Jones Industrial Average.","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"60 - 82"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42129876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article investigates the dynamic empirical relationships among realized, risk-neutral, and risk premium measures of volatility and correlation of the S&P 500 stock index from January 1, 2000, to December 31, 2020. The empirical investigation runs a spillover analysis to identify the receiver and the transmitter variables and implements a wavelet local multiple correlation (WLMC) methodology to study the multiscale correlations. The results identify the implied measures as the most influential variables and also reveal that the strength of correlation is changing with time scales; moreover, the correlation between volatility risk premium and correlation risk premium is not always statistically significant through either time scales or time periods. These findings support the use of scale-based correlation metrics in derivatives studies.
{"title":"Wavelet Multiscale and Spillover Analyses of Volatility and Correlation","authors":"Sofiane Aboura","doi":"10.3905/jod.2022.1.155","DOIUrl":"https://doi.org/10.3905/jod.2022.1.155","url":null,"abstract":"This article investigates the dynamic empirical relationships among realized, risk-neutral, and risk premium measures of volatility and correlation of the S&P 500 stock index from January 1, 2000, to December 31, 2020. The empirical investigation runs a spillover analysis to identify the receiver and the transmitter variables and implements a wavelet local multiple correlation (WLMC) methodology to study the multiscale correlations. The results identify the implied measures as the most influential variables and also reveal that the strength of correlation is changing with time scales; moreover, the correlation between volatility risk premium and correlation risk premium is not always statistically significant through either time scales or time periods. These findings support the use of scale-based correlation metrics in derivatives studies.","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"20 - 39"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46647547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, the author demonstrates methods for processing derivatives exposures that are beneficial to investment portfolio performance and that accurately reflect the portfolio managers’ objectives. The article will help the reader evaluate the success of portfolio management decisions.
{"title":"Handling the Use of Derivatives in Performance Attribution","authors":"Bruce J. Feibel","doi":"10.3905/jod.2022.1.154","DOIUrl":"https://doi.org/10.3905/jod.2022.1.154","url":null,"abstract":"In this article, the author demonstrates methods for processing derivatives exposures that are beneficial to investment portfolio performance and that accurately reflect the portfolio managers’ objectives. The article will help the reader evaluate the success of portfolio management decisions.","PeriodicalId":34223,"journal":{"name":"Jurnal Derivat","volume":"29 1","pages":"121 - 138"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44991530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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