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Forecasting Options Prices Using Discrete Time Volatility Models Estimated at Mixed Timescales
The Journal of Derivatives ( IF 0.647 ) Pub Date : 2019-12-13 , DOI: 10.3905/jod.2019.1.094
Giovanni Calice , Jing Chen , Julian Williams

Option pricing models traditionally have utilized continuous-time frameworks to derive solutions or Monte Carlo schemes to price the contingent claim. Typically these models were calibrated to discrete-time data using a variety of approaches. Recent work on GARCH-based option pricing models have introduced a set of models that easily can be estimated via MLE or GMM directly from discrete time spot data. This article provides a series of extensions to the standard discrete-time options pricing setup and then implements a set of various pricing approaches for a very large cross section of equity and index options against the forward-looking traded market price of these options, out of sample. The authors’ analysis provides two significant findings. First, they provide clear evidence that including autoregressive jumps in the options model is critical in determining the correct price of heavily out-of-the money and in-the-money options relatively close to maturity. Second, for longer maturity options, they show that the anticipated performance of the popular component GARCH models, which exhibit long persistence in volatility, does not materialize. They ascribe this result in part to the inherent instability of the numerical solution to the option price in the presence of component volatility. Taken together, their results suggest that when pricing options, the first best approach is to include jumps directly in the model, preferably using jumps calibrated from intraday data. TOPICS: Options, volatility measures Key Findings • This article presents a new method for estimating the parameters for a jump GARCH model. The authors provide a series of empirical tests of the efficacy of the GARCH-type option models. They analyze the S&P 500 Index and 20 individual equities sampled from the Dow Jones 30. Their out-of-sample test covers over a third of a million individually equity traded prices. • They find three primary empirical results. First, pre-filtering for jumps improves the accuracy of option models based on GARCH processes. Second, for certain stocks, models that explicitly incorporate jumps substantially outperform all other models. Third, for the S&P 500, the GARCH model estimated on jump-filtered returns appears to dominate.

中文翻译:

使用在混合时间尺度上估计的离散时间波动率模型预测期权价格

传统上,期权定价模型利用连续时间框架来得出解决方案,或者使用蒙特卡洛方案对或有债权进行定价。通常,使用各种方法将这些模型校准为离散时间数据。基于GARCH的期权定价模型的最新工作引入了一组模型,可以通过MLE或GMM轻松地直接从离散时间点数据中进行估算。本文提供了对标准离散时间期权定价设置的一系列扩展,然后针对这些期权的前瞻性交易市场价格(针对这些期权的前瞻性交易价格),针对非常大的股票期权和指数期权实施了一系列定价方法。样品。作者的分析提供了两个重要发现。第一,他们提供了明确的证据,将期权模型中的自回归跃迁包括在内,对于确定相对接近到期期限的大量货币内和货币内期权的正确价格至关重要。第二,对于更长的到期日选择权,他们表明流行的GARCH组件模型(表现出对波动率的持久性)的预期性能无法实现。他们将此结果部分归因于在存在组件波动性的情况下对期权价格进行数值解的内在不稳定性。两者合计,他们的结果表明,在选择定价时,最好的方法是直接将跳跃包括在模型中,最好使用从当日数据中校准的跳跃。主题:选项,波动性度量主要发现•本文介绍了一种用于估计GARCH跳跃模型参数的新方法。作者提供了一系列有关GARCH类型期权模型有效性的经验检验。他们分析了标准普尔500指数和从道琼斯30指数中抽取的20只股票。他们的样本外测试覆盖了百万美元个人股票交易价格的三分之一。•他们发现了三个主要的经验结果。首先,对跳转进行预过滤可提高基于GARCH流程的期权模型的准确性。其次,对于某些股票,明确包含跳跃的模型明显优于所有其他模型。第三,对于标准普尔500指数而言,以跳跃过滤收益估算的GARCH模型似乎占主导地位。作者提供了一系列有关GARCH类型期权模型有效性的经验检验。他们分析了标准普尔500指数和从道琼斯30指数中抽取的20只股票。他们的样本外测试覆盖了百万美元个人股票交易价格的三分之一。•他们发现了三个主要的经验结果。首先,对跳转进行预过滤可提高基于GARCH流程的期权模型的准确性。其次,对于某些股票,明确包含跳跃的模型明显优于所有其他模型。第三,对于标准普尔500指数而言,以跳跃过滤收益估算的GARCH模型似乎占主导地位。作者提供了一系列有关GARCH类型期权模型有效性的经验检验。他们分析了标准普尔500指数和从道琼斯30指数中抽取的20只股票。他们的样本外测试覆盖了百万美元个人股票交易价格的三分之一。•他们发现了三个主要的经验结果。首先,对跳转进行预过滤可提高基于GARCH流程的期权模型的准确性。其次,对于某些股票,明确包含跳跃的模型明显优于所有其他模型。第三,对于标准普尔500指数而言,以跳跃过滤收益估算的GARCH模型似乎占主导地位。•他们发现了三个主要的经验结果。首先,对跳转进行预过滤可提高基于GARCH流程的期权模型的准确性。其次,对于某些股票,明确包含跳跃的模型明显优于所有其他模型。第三,对于标准普尔500指数而言,以跳跃过滤收益估算的GARCH模型似乎占主导地位。•他们发现了三个主要的经验结果。首先,对跳转进行预过滤可提高基于GARCH流程的期权模型的准确性。其次,对于某些股票,明确包含跳跃的模型明显优于所有其他模型。第三,对于标准普尔500指数而言,以跳跃过滤收益估算的GARCH模型似乎占主导地位。
更新日期:2019-12-13
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