We introduce a new derivative security called a stoption. After paying an upfront premium, the owner of a stoption accrues realized price changes in some underlying security until the exposure is stopped by the owner. Upon stopping, the reward is the sum of all of the previous price changes plus a deterministic amount which can vary with the stopping time. Stoptions are finite-lived and hence must be stopped at or before a fixed maturity date. We propose a particular discrete-time probabilistic model for the underlying's price changes and then determine the optimal stopping strategy and stoption premium for that model in closed-form. We also present an application to DVA (debit valuation adjustment) under full collateralization.
{"title":"Stoptions: Representations and Applications","authors":"P. Carr","doi":"10.2139/ssrn.3929583","DOIUrl":"https://doi.org/10.2139/ssrn.3929583","url":null,"abstract":"We introduce a new derivative security called a stoption. After paying an upfront premium, the owner of a stoption accrues realized price changes in some underlying security until the exposure is stopped by the owner. Upon stopping, the reward is the sum of all of the previous price changes plus a deterministic amount which can vary with the stopping time. Stoptions are finite-lived and hence must be stopped at or before a fixed maturity date. We propose a particular discrete-time probabilistic model for the underlying's price changes and then determine the optimal stopping strategy and stoption premium for that model in closed-form. We also present an application to DVA (debit valuation adjustment) under full collateralization.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127089405","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}
Chuanhai Zhang, Huan Ma, Gideon Bruce Arkorful, Zhe Peng
The impact of Bitcoin futures introduction on the underlying Bitcoin volatility has been a controversial topic. Conflicting results had been obtained with different sample periods and methodologies. To address this debate, this study examines the impacts of Bitcoin futures trading on spot market volatility in the short and long run. Using exponential GARCH model, we introduce a dummy in the variance equation to capture the changes in the volatility before and after the introduction of Bitcoin futures. We find that after Bitcoin futures introduction, spot return volatility decreases in the short run, but increases in the long run. Besides, in the short run, there exists an inverse leverage effect before and after the introduction of futures; in the long run, it changes from an inverse leverage effect to a usual leverage effect. Finally, we examine whether greater futures trading activity, including volume and open interest, is associated with greater Bitcoin volatility. To do so, we decompose each proxy for trading activity into expected and unexpected components and document that Bitcoin volatility covaries positively with unexpected futures trading volume, but negatively related to forecastable futures trading volume.
{"title":"The Impacts of Futures Introduction on Spot Market Volatility: Evidence from the Bitcoin Market","authors":"Chuanhai Zhang, Huan Ma, Gideon Bruce Arkorful, Zhe Peng","doi":"10.2139/ssrn.3903735","DOIUrl":"https://doi.org/10.2139/ssrn.3903735","url":null,"abstract":"The impact of Bitcoin futures introduction on the underlying Bitcoin volatility has been a controversial topic. Conflicting results had been obtained with different sample periods and methodologies. To address this debate, this study examines the impacts of Bitcoin futures trading on spot market volatility in the short and long run. Using exponential GARCH model, we introduce a dummy in the variance equation to capture the changes in the volatility before and after the introduction of Bitcoin futures. We find that after Bitcoin futures introduction, spot return volatility decreases in the short run, but increases in the long run. Besides, in the short run, there exists an inverse leverage effect before and after the introduction of futures; in the long run, it changes from an inverse leverage effect to a usual leverage effect. Finally, we examine whether greater futures trading activity, including volume and open interest, is associated with greater Bitcoin volatility. To do so, we decompose each proxy for trading activity into expected and unexpected components and document that Bitcoin volatility covaries positively with unexpected futures trading volume, but negatively related to forecastable futures trading volume.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123098328","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}
A new acceptable price approach to stochastic endpoint determination at given horizon accounting for the marginal investor beliefs and behaviour was proposed. Two-sided filtration with FBSDE defined stochastic dynamics was formulated for acceptable asset price under the risk-neutral probability measure, at that the target price distribution is characterized by the averaged over active market agent subset parameters. For the current price at market equilibrium, the acceptable price of risk distribution was found. The implied volatility dependencies for the equilibrium conditions and with predicted utility and liquidity premiums were determined. A generalized solution for the forward-backward stochastic problem and a partial solution for the formulated for options stochastic terminal conditions were found. The deep learning algorithm with simulated idiosyncratic noise was tested.
{"title":"Forward Backward Stochastic Model of Financial Asset Pricing with Idiosyncratic Noise","authors":"Pavel Levin","doi":"10.2139/ssrn.3858106","DOIUrl":"https://doi.org/10.2139/ssrn.3858106","url":null,"abstract":"A new acceptable price approach to stochastic endpoint determination at given horizon accounting for the marginal investor beliefs and behaviour was proposed. Two-sided filtration with FBSDE defined stochastic dynamics was formulated for acceptable asset price under the risk-neutral probability measure, at that the target price distribution is characterized by the averaged over active market agent subset parameters. For the current price at market equilibrium, the acceptable price of risk distribution was found. The implied volatility dependencies for the equilibrium conditions and with predicted utility and liquidity premiums were determined. A generalized solution for the forward-backward stochastic problem and a partial solution for the formulated for options stochastic terminal conditions were found. The deep learning algorithm with simulated idiosyncratic noise was tested.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133142785","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}
The article models the term structure of commodity futures prices using the Nelson-Siegel framework. Exploiting the information embedded in the level, slope, and curvature parameters, we develop novel investment strategies that assume short-term continuation of recent parallel, twist or butterfly movements of futures curves. Systematic strategies based on changes in the slope and curvature generate statistically significant profits uncorrelated to previously documented commodity factors. The information content embedded in the curvature parameter appears to be sensitive to market frictions, but the strategy based on the slope parameter remains profitable net of transaction costs and is valuable as an overlay to traditional commodity portfolios.
{"title":"Exploiting the Dynamics of Commodity Futures Curves","authors":"R. Bianchi, John Hua Fan, J. Miffre, T. Zhang","doi":"10.2139/ssrn.3749061","DOIUrl":"https://doi.org/10.2139/ssrn.3749061","url":null,"abstract":"The article models the term structure of commodity futures prices using the Nelson-Siegel framework. Exploiting the information embedded in the level, slope, and curvature parameters, we develop novel investment strategies that assume short-term continuation of recent parallel, twist or butterfly movements of futures curves. Systematic strategies based on changes in the slope and curvature generate statistically significant profits uncorrelated to previously documented commodity factors. The information content embedded in the curvature parameter appears to be sensitive to market frictions, but the strategy based on the slope parameter remains profitable net of transaction costs and is valuable as an overlay to traditional commodity portfolios.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127709773","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 study introduces a new technique to analyse the evolution of correlations for multiple time series. The technique is based on applying Topological Data Analysis (TDA) and we use it to gain insights about the evolution of commodity futures markets over the 1997-2017 period. Our findings complement the existing literature and provide new insights into the dynamics of commodity futures markets in the past two decades. Our analysis has both global and local aspects and could be applied to detect changes in correlation structure in a variety of time series as well as cross sectional settings.
{"title":"The Four Seasons of Commodity Futures: Insights from Topological Data Analysis","authors":"D. Basu, P. Dłotko","doi":"10.2139/ssrn.3506780","DOIUrl":"https://doi.org/10.2139/ssrn.3506780","url":null,"abstract":"This study introduces a new technique to analyse the evolution of correlations for multiple time series. The technique is based on applying Topological Data Analysis (TDA) and we use it to gain insights about the evolution of commodity futures markets over the 1997-2017 period. Our findings complement the existing literature and provide new insights into the dynamics of commodity futures markets in the past two decades. Our analysis has both global and local aspects and could be applied to detect changes in correlation structure in a variety of time series as well as cross sectional settings.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133169647","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 paper examines the dynamics of quoted bid-ask spreads, price volatility and percentage trading volume for the most liquid interest rate futures trading on the Sydney Futures Exchange. Using data for both the overnight and intraday markets of the Sydney Futures Exchange, patterns contrast the existing theory and prior research. During the Intra-night trading session, volume and volatility patterns show low trading activities between 5:30 and 9:30; while spreads show high asymmetric information during this interval. During the Intra-day trading session, both volume and volatility exhibits a significant increase at the opening and around 2.30pm, followed a significant decrease towards the end of the day session; Spreads are low at the opening and tendentially increase throughout the day up until the close of the day. We find that percentage Volume traded is higher during the day session; although spread significantly increases towards the end of the day session, it is tighter than the overnight spread. A number of tests are carried out documenting that these patterns are consistent with the effects of contagion from overseas markets, US versus Australia daylight savings, and major macro-economics information releases.
{"title":"Dynamics of Interest Rate Futures: A Comprehensive Study from The Sydney Futures Exchange","authors":"Dionigi Gerace, A. Frino","doi":"10.2139/ssrn.3540633","DOIUrl":"https://doi.org/10.2139/ssrn.3540633","url":null,"abstract":"This paper examines the dynamics of quoted bid-ask spreads, price volatility and percentage trading volume for the most liquid interest rate futures trading on the Sydney Futures Exchange. Using data for both the overnight and intraday markets of the Sydney Futures Exchange, patterns contrast the existing theory and prior research. During the Intra-night trading session, volume and volatility patterns show low trading activities between 5:30 and 9:30; while spreads show high asymmetric information during this interval. During the Intra-day trading session, both volume and volatility exhibits a significant increase at the opening and around 2.30pm, followed a significant decrease towards the end of the day session; Spreads are low at the opening and tendentially increase throughout the day up until the close of the day. We find that percentage Volume traded is higher during the day session; although spread significantly increases towards the end of the day session, it is tighter than the overnight spread. A number of tests are carried out documenting that these patterns are consistent with the effects of contagion from overseas markets, US versus Australia daylight savings, and major macro-economics information releases.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125903913","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 paper studies the trading behavior of different types of traders in commodity futures and their impact on liquidity consumption/provision as well as price discovery in the market. CME classifies each trade by its Customer Type Indicator (CTI) into four groups: a local trader who trades for his own account (CTI1), a commercial clearing member for his proprietary accounts (CTI2), an exchange member for his own account though a local trader (CTI3), and the general public (non-members) (CTI4). We find that non-members (CTI4) consume most of the short-term (intraday) liquidity while local traders as market makers are its main provider. Such a liquidity provision yields a substantial Sharpe ratio for the latter and constitutes most of the intraday volume. Most of the interday trading and position taking come from groups CTI2 and CTI3, reflecting their longer term needs for hedging and speculation. We also find that the imbalance in demand and supply in the market can explain a significant part of the daily price movements. In addition, changes in the overnight positions of the general public and clearing members contribute mostly to daily price changes. Moreover, we find that daily changes in the positions of CTI3 group can forecast future price movements, reflecting possible information advantage they may possess.
{"title":"Trading and Information in Futures Markets","authors":"G. Llorente, Jiang Wang","doi":"10.2139/ssrn.2605347","DOIUrl":"https://doi.org/10.2139/ssrn.2605347","url":null,"abstract":"This paper studies the trading behavior of different types of traders in commodity futures and their impact on liquidity consumption/provision as well as price discovery in the market. CME classifies each trade by its Customer Type Indicator (CTI) into four groups: a local trader who trades for his own account (CTI1), a commercial clearing member for his proprietary accounts (CTI2), an exchange member for his own account though a local trader (CTI3), and the general public (non-members) (CTI4). We find that non-members (CTI4) consume most of the short-term (intraday) liquidity while local traders as market makers are its main provider. Such a liquidity provision yields a substantial Sharpe ratio for the latter and constitutes most of the intraday volume. Most of the interday trading and position taking come from groups CTI2 and CTI3, reflecting their longer term needs for hedging and speculation. We also find that the imbalance in demand and supply in the market can explain a significant part of the daily price movements. In addition, changes in the overnight positions of the general public and clearing members contribute mostly to daily price changes. Moreover, we find that daily changes in the positions of CTI3 group can forecast future price movements, reflecting possible information advantage they may possess.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116040650","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}
The commodity futures basis—the difference between the first and second futures prices—is known to forecast commodity futures returns, arguably through its relation with the convenience yield. We propose a refined measure of the basis, dubbed the relative basis, which is the difference between the traditional basis and a similarly defined longer-term basis. We argue that the relative basis is more informative about expected commodity futures returns than the basis, because it excludes components in the traditional basis that are closely related to storage costs and financing costs. In our empirical analyses, we show that a) the relative basis exhibits much more time variation than the traditional basis, and b) subsumes the traditional basis in forecasting commodity futures returns.
{"title":"Relative Basis and Expected Returns in Commodity Futures Markets","authors":"Ming Gu, W. Kang, D. Lou, Ke Tang","doi":"10.2139/ssrn.3404561","DOIUrl":"https://doi.org/10.2139/ssrn.3404561","url":null,"abstract":"The commodity futures basis—the difference between the first and second futures prices—is known to forecast commodity futures returns, arguably through its relation with the convenience yield. We propose a refined measure of the basis, dubbed the relative basis, which is the difference between the traditional basis and a similarly defined longer-term basis. We argue that the relative basis is more informative about expected commodity futures returns than the basis, because it excludes components in the traditional basis that are closely related to storage costs and financing costs. In our empirical analyses, we show that a) the relative basis exhibits much more time variation than the traditional basis, and b) subsumes the traditional basis in forecasting commodity futures returns.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"36 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132286577","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}
Following the 2004 introduction of VIX futures, they have become an increasingly important hedging instrument and aid for portfolio diversification. We examine changes in the futures basis which, owing to their unique characteristics, can be interpreted as changes in expectations of future VIX (“fear”) levels. We find that higher levels of VIX are associated with a narrowing of the futures basis, suggesting that investors view “fear” as transitory, and a flatter forward curve. We propose news sentiment as one plausible explanation for changes in the basis. A wider (narrower) basis accompanies the more positive (negative) news associated with a falling (rising) VIX index.
{"title":"Transitory Fear: Explaining Changes in the VIX Futures Basis","authors":"L. Smales","doi":"10.2139/ssrn.3396850","DOIUrl":"https://doi.org/10.2139/ssrn.3396850","url":null,"abstract":"Following the 2004 introduction of VIX futures, they have become an increasingly important hedging instrument and aid for portfolio diversification. We examine changes in the futures basis which, owing to their unique characteristics, can be interpreted as changes in expectations of future VIX (“fear”) levels. We find that higher levels of VIX are associated with a narrowing of the futures basis, suggesting that investors view “fear” as transitory, and a flatter forward curve. We propose news sentiment as one plausible explanation for changes in the basis. A wider (narrower) basis accompanies the more positive (negative) news associated with a falling (rising) VIX index.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134575424","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}
Where does new volatility enter the volatility of securities listed in many countries? While literature has focused on where information enters the price, I develop a framework to study how each market’s volatility contributes to the permanent volatility of the Asset. I build a VECM with an Autoregressive Stochastic Volatility (ASV) framework estimated using the MCMC method and Bayesian inference. This specification allows defining the measures of a market’s contribution to volatility discovery. In the application, I study cash and 3-months futures markets of some metals traded on the London Metals Exchange. I also study the EURO STOXX 50 Index and its futures. I find that for most the securities, while price discovery happens on the cash market, the volatility discovery mostly happens in the futures market. Overall, the results suggest that information discovery and volatility discovery do not necessarily have the same determinants.
{"title":"Volatility Discovery across Interlinked Securities","authors":"Christian Nguenang","doi":"10.2139/ssrn.3453352","DOIUrl":"https://doi.org/10.2139/ssrn.3453352","url":null,"abstract":"Where does new volatility enter the volatility of securities listed in many countries? While literature has focused on where information enters the price, I develop a framework to study how each market’s volatility contributes to the permanent volatility of the Asset. I build a VECM with an Autoregressive Stochastic Volatility (ASV) framework estimated using the MCMC method and Bayesian inference. This specification allows defining the measures of a market’s contribution to volatility discovery. In the application, I study cash and 3-months futures markets of some metals traded on the London Metals Exchange. I also study the EURO STOXX 50 Index and its futures. I find that for most the securities, while price discovery happens on the cash market, the volatility discovery mostly happens in the futures market. Overall, the results suggest that information discovery and volatility discovery do not necessarily have the same determinants.","PeriodicalId":306457,"journal":{"name":"ERN: Futures (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130461614","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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