Econometric Modeling: Derivatives eJournal最新文献_第4页

Testing for Asset Price Bubbles using Options Data 使用期权数据测试资产价格泡沫

Econometric Modeling: Derivatives eJournal Pub Date : 2020-08-10 DOI: 10.2139/ssrn.3670999

Nicola Fusari, R. Jarrow, Sujan Lamichhane

引用次数: 7

Option-Implied Quantiles and Market Returns (Extended Abstract) 期权隐含分位数与市场收益(扩展摘要)

Econometric Modeling: Derivatives eJournal Pub Date : 2020-07-17 DOI: 10.2139/ssrn.3654110

Yan Wang

{"title":"Option-Implied Quantiles and Market Returns (Extended Abstract)","authors":"Yan Wang","doi":"10.2139/ssrn.3654110","DOIUrl":"https://doi.org/10.2139/ssrn.3654110","url":null,"abstract":"Fear of disastrous tail events embedded in the short-term option contracts is reflected in the long-term equity risk premiums (ERP). A novel formula is proposed to identify the risk-neutral return quantiles from European option prices in a model-free manner. We use this formula to extract risk-neutral return quantiles of the S&P 500 index from January 1996 to June 2019. In the uni-variate predictive regressions, we find the difference between 5% and 95% risk-neutral quantiles (TD) significantly predicts equity risk premiums at horizons of more than one year, based on the standard error estimates of Hansen and Hodrick (1980) corrected for heteroskedasticity, Hodrick (1992) and Newey-West. We argue that TD captures the aversion to disastrous tail events of the market participants and, consistent with this, we find that TD is highly persistent. In the bi-variate predictive regressions that control for the well-known market predictors, TD is complementary to the variance risk premium of Bollerslev, Tauchen and Zhou (2009), which is a significant predictor of the ERP at horizons of less than one year. The correlation of TD with the dividend price ratio is 35% and highly significant, which is consistent with the finding of Campbell and Shiller (1989) that the variation of dividend price ratio is driven mainly by the variation of future discount rates.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116069762","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}

引用次数: 0

Pricing Discretely-Monitored Double Barrier Options with Small Probabilities of Execution 具有小执行概率的离散监控双障碍期权定价

Econometric Modeling: Derivatives eJournal Pub Date : 2020-07-14 DOI: 10.2139/ssrn.3132336

Vasileios E. Kontosakos, Keegan Mendonca, A. Pantelous, K. Zuev

引用次数: 5

The Effects of Financial and Operational Hedging on Company Value: The Case of Malaysian Multinationals 财务和经营套期保值对公司价值的影响:以马来西亚跨国公司为例

Econometric Modeling: Derivatives eJournal Pub Date : 2020-07-13 DOI: 10.2139/ssrn.3724984

Azadeh Hadian, Cahit Adaoglu

{"title":"The Effects of Financial and Operational Hedging on Company Value: The Case of Malaysian Multinationals","authors":"Azadeh Hadian, Cahit Adaoglu","doi":"10.2139/ssrn.3724984","DOIUrl":"https://doi.org/10.2139/ssrn.3724984","url":null,"abstract":"Abstract This study examines the value effects of financial and operational hedging in a managed floating exchange rate regime with strict limitations on the trading of Malaysian Ringgit for a sample of 109 Malaysian multinationals from 2004–2018. Using Tobin’s Q as a proxy for company value, the two-step system GMM estimation results show that, on average, derivatives hedging creates a value premium range of 7.88–8.21 % in the short-run, and 18.81–19.80 % in the long-run. This value premium emerged both after controlling for non-operational foreign exchange profits (losses), and its two components: transaction and translation profits (losses). In contrast, foreign debt hedging, on average, creates a value discount range of 8.19–8.54 % in the short-run and 12.70–13.12 % in the long-run. No evidence shows value effect for operational hedging though. The positive value effect of derivatives hedging should motivate managers of Malaysian multinationals to hedge foreign currency exposure through derivatives and encourage policymakers to take steps in developing derivatives market and products. However, the negative value effect of foreign debt hedging indicates that it destroys value. This negative effect might reflect two potential causes; higher company risk due to FC debt financing, and improper hedging practices including high costs of hedging in the underdeveloped derivatives market. These potential causes need further empirical evaluations.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122002078","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}

引用次数: 18

The Impact of Derivatives on Cash Markets: Evidence From the Introduction of Bitcoin Futures Contracts 衍生品对现金市场的影响:来自比特币期货合约引入的证据

Econometric Modeling: Derivatives eJournal Pub Date : 2020-07-10 DOI: 10.2139/ssrn.3648406

Patrick Augustin, Alexey Rubtsov, Donghwa Shin

引用次数: 8

Robust Pricing and Hedging via Neural SDEs 基于神经SDEs的稳健定价和对冲

Econometric Modeling: Derivatives eJournal Pub Date : 2020-07-08 DOI: 10.2139/ssrn.3646241

Patryk Gierjatowicz, Marc Sabate Vidales, D. Šiška, L. Szpruch, Zan Zuric

{"title":"Robust Pricing and Hedging via Neural SDEs","authors":"Patryk Gierjatowicz, Marc Sabate Vidales, D. Šiška, L. Szpruch, Zan Zuric","doi":"10.2139/ssrn.3646241","DOIUrl":"https://doi.org/10.2139/ssrn.3646241","url":null,"abstract":"Mathematical modelling is ubiquitous in the financial industry and drives key decision processes. Any given model provides only a crude approximation to reality and the risk of using an inadequate model is hard to detect and quantify. By contrast, modern data science techniques are opening the door to more robust and data-driven model selection mechanisms. However, most machine learning models are \"black-boxes\" as individual parameters do not have meaningful interpretation. The aim of this paper is to combine the above approaches achieving the best of both worlds. Combining neural networks with risk models based on classical stochastic differential equations (SDEs), we find robust bounds for prices of derivatives and the corresponding hedging strategies while incorporating relevant market data. The resulting model called neural SDE is an instantiation of generative models and is closely linked with the theory of causal optimal transport. Neural SDEs allow consistent calibration under both the risk-neutral and the real-world measures. Thus the model can be used to simulate market scenarios needed for assessing risk profiles and hedging strategies. We develop and analyse novel algorithms needed for efficient use of neural SDEs. We validate our approach with numerical experiments using both local and stochastic volatility models.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121861347","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}

引用次数: 26

Option Pricing: A Heuristic Based on Exponential Decay

Econometric Modeling: Derivatives eJournal Pub Date : 2020-07-05 DOI: 10.2139/ssrn.3643931

Rogério Pereira

引用次数: 0

Large Deviation Principles for Stochastic Volatility Models with Reflection and Three Faces of the Stein and Stein Model Stein和Stein模型反射和三面随机波动模型的大偏差原理

Econometric Modeling: Derivatives eJournal Pub Date : 2020-06-27 DOI: 10.2139/ssrn.3757783

Archil Gulisashvili

引用次数: 2

Predictability Puzzles 可预测性谜题

Econometric Modeling: Derivatives eJournal Pub Date : 2020-06-12 DOI: 10.2139/ssrn.3625709

Bjørn Eraker

引用次数: 1

Extensions of Dupire Formula: Stochastic Interest Rates and Stochastic Local Volatility Dupire公式的扩展:随机利率和随机局部波动率

Econometric Modeling: Derivatives eJournal Pub Date : 2020-05-12 DOI: 10.2139/ssrn.3598736

O. Ogetbil

引用次数: 3