Pub Date : 2023-07-19DOI: 10.1080/14697688.2023.2221281
Julien Guyon, Jordan Lekeufack
We learn from data that volatility is mostly path-dependent: up to 90% of the variance of the implied volatility of equity indexes is explained endogenously by past index returns, and up to 65% for (noisy estimates of) future daily realized volatility. The path-dependency that we uncover is remarkably simple: a linear combination of a weighted sum of past daily returns and the square root of a weighted sum of past daily squared returns with different time-shifted power-law weights capturing both short and long memory. This simple model, which is homogeneous in volatility, is shown to consistently outperform existing models across equity indexes and train/test sets for both implied and realized volatility. It suggests a simple continuous-time path-dependent volatility (PDV) model that may be fed historical or risk-neutral parameters. The weights can be approximated by superpositions of exponential kernels to produce Markovian models. In particular, we propose a 4-factor Markovian PDV model which captures all the important stylized facts of volatility, produces very realistic price and (rough-like) volatility paths, and jointly fits SPX and VIX smiles remarkably well. We thus show that a continuous-time Markovian parametric stochastic volatility (actually, PDV) model can practically solve the joint SPX/VIX smile calibration problem. This article is dedicated to the memory of Peter Carr whose works on volatility modeling have been so inspiring to us.
{"title":"Volatility is (mostly) path-dependent","authors":"Julien Guyon, Jordan Lekeufack","doi":"10.1080/14697688.2023.2221281","DOIUrl":"https://doi.org/10.1080/14697688.2023.2221281","url":null,"abstract":"We learn from data that volatility is mostly path-dependent: up to 90% of the variance of the implied volatility of equity indexes is explained endogenously by past index returns, and up to 65% for (noisy estimates of) future daily realized volatility. The path-dependency that we uncover is remarkably simple: a linear combination of a weighted sum of past daily returns and the square root of a weighted sum of past daily squared returns with different time-shifted power-law weights capturing both short and long memory. This simple model, which is homogeneous in volatility, is shown to consistently outperform existing models across equity indexes and train/test sets for both implied and realized volatility. It suggests a simple continuous-time path-dependent volatility (PDV) model that may be fed historical or risk-neutral parameters. The weights can be approximated by superpositions of exponential kernels to produce Markovian models. In particular, we propose a 4-factor Markovian PDV model which captures all the important stylized facts of volatility, produces very realistic price and (rough-like) volatility paths, and jointly fits SPX and VIX smiles remarkably well. We thus show that a continuous-time Markovian parametric stochastic volatility (actually, PDV) model can practically solve the joint SPX/VIX smile calibration problem. This article is dedicated to the memory of Peter Carr whose works on volatility modeling have been so inspiring to us.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"36 1","pages":"1221 - 1258"},"PeriodicalIF":1.3,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85865303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-10DOI: 10.1080/14697688.2023.2230249
B. Eroğlu, Haluk Yener, Taner M. Yigit
We show that applying the wavelet transform to S&P 500 constituents' prices generates a substantial increase in the returns of the pairs-trading strategy. Pairs trading strategy is based on finding prices that move together, but if there is shared noise in the asset prices, the co-movement, on which one base the trades, might be caused by this common noise. We show that wavelet transform filters away the noise, leading to more profitable trades. The most notable change occurs in the parameter estimation stage, which forms the weights of the assets in the pairs portfolio. Without filtering, the parameters estimated in the training period lose relevance in the trading period. However, when prices are filtered from common noise, the parameters maintain relevance much longer and result in more profitable trades. Particularly, we show that more precise parameter estimation is reflected on a more stationary and conservative spread, meaning more mean reversion in opened pairs trades. We also show that wavelet filtering the prices reduces the downside risk of the trades considerably.
{"title":"Pairs trading with wavelet transform","authors":"B. Eroğlu, Haluk Yener, Taner M. Yigit","doi":"10.1080/14697688.2023.2230249","DOIUrl":"https://doi.org/10.1080/14697688.2023.2230249","url":null,"abstract":"We show that applying the wavelet transform to S&P 500 constituents' prices generates a substantial increase in the returns of the pairs-trading strategy. Pairs trading strategy is based on finding prices that move together, but if there is shared noise in the asset prices, the co-movement, on which one base the trades, might be caused by this common noise. We show that wavelet transform filters away the noise, leading to more profitable trades. The most notable change occurs in the parameter estimation stage, which forms the weights of the assets in the pairs portfolio. Without filtering, the parameters estimated in the training period lose relevance in the trading period. However, when prices are filtered from common noise, the parameters maintain relevance much longer and result in more profitable trades. Particularly, we show that more precise parameter estimation is reflected on a more stationary and conservative spread, meaning more mean reversion in opened pairs trades. We also show that wavelet filtering the prices reduces the downside risk of the trades considerably.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"44 1","pages":"1129 - 1154"},"PeriodicalIF":1.3,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84529900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-09DOI: 10.1080/14697688.2023.2226370
M. L. Bianchi, G. Tassinari
In this work, we explore the information content of senior, subordinated and additional tier 1 (or contingent convertible) bonds issued by euro-area banks. We analyze both the asset volatility implied in senior and subordinated bonds and credit default swap market spreads, and the common equity tier 1 (CET1) ratio volatility extracted from additional tier 1 bonds secondary market spreads in the period from December 31, 2012 to March 31, 2021. Furthermore, we jointly consider the following important bank variables: asset, equity and CET1 ratio volatilities. In doing so, we can obtain the market view on credit spreads, banks balance sheet and capital ratio dynamics on a daily basis even if bank data are released quarterly. The approach can be used to monitor the risk of each bank, as perceived by the market, and to investigate banking fragility at a stand-alone or at a country level. Finally, we compare our estimated equity implied volatilities with the volatilities implied in equity option quotes and we show that this indicator depends on the model and the financial instruments considered in the calibration.
{"title":"Extracting implied volatilities from bank bonds","authors":"M. L. Bianchi, G. Tassinari","doi":"10.1080/14697688.2023.2226370","DOIUrl":"https://doi.org/10.1080/14697688.2023.2226370","url":null,"abstract":"In this work, we explore the information content of senior, subordinated and additional tier 1 (or contingent convertible) bonds issued by euro-area banks. We analyze both the asset volatility implied in senior and subordinated bonds and credit default swap market spreads, and the common equity tier 1 (CET1) ratio volatility extracted from additional tier 1 bonds secondary market spreads in the period from December 31, 2012 to March 31, 2021. Furthermore, we jointly consider the following important bank variables: asset, equity and CET1 ratio volatilities. In doing so, we can obtain the market view on credit spreads, banks balance sheet and capital ratio dynamics on a daily basis even if bank data are released quarterly. The approach can be used to monitor the risk of each bank, as perceived by the market, and to investigate banking fragility at a stand-alone or at a country level. Finally, we compare our estimated equity implied volatilities with the volatilities implied in equity option quotes and we show that this indicator depends on the model and the financial instruments considered in the calibration.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"243 1","pages":"1177 - 1197"},"PeriodicalIF":1.3,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80532693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1080/14697688.2023.2222158
Shashidhar Murthy, John K. Wald
We consider the problem of optimal dynamic trading in the presence of predictable returns and proportional transaction costs for an investor choosing among multiple assets. The value of each security equals the expected value of holding the asset plus the value of all options to trade. We provide exact trading rules for N-assets that follow an MA(1) process. Simulations demonstrate the impact of transaction costs, volatility, and predictability on optimal trading behavior. The optimal trading rule can substantially increase performance if transaction costs vary among assets.
{"title":"Optimal trading with transaction costs and short-term predictability","authors":"Shashidhar Murthy, John K. Wald","doi":"10.1080/14697688.2023.2222158","DOIUrl":"https://doi.org/10.1080/14697688.2023.2222158","url":null,"abstract":"We consider the problem of optimal dynamic trading in the presence of predictable returns and proportional transaction costs for an investor choosing among multiple assets. The value of each security equals the expected value of holding the asset plus the value of all options to trade. We provide exact trading rules for N-assets that follow an MA(1) process. Simulations demonstrate the impact of transaction costs, volatility, and predictability on optimal trading behavior. The optimal trading rule can substantially increase performance if transaction costs vary among assets.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"48 1","pages":"1115 - 1127"},"PeriodicalIF":1.3,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88068176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.1080/14697688.2023.2202708
M. Fukasawa, Basile Maire, Marcus Wunsch
Impermanent Loss in Decentralized Finance can be hedged with weighted variance swaps
去中心化金融中的非永久性损失可以用加权方差掉期进行对冲
{"title":"Weighted variance swaps hedge against impermanent loss","authors":"M. Fukasawa, Basile Maire, Marcus Wunsch","doi":"10.1080/14697688.2023.2202708","DOIUrl":"https://doi.org/10.1080/14697688.2023.2202708","url":null,"abstract":"Impermanent Loss in Decentralized Finance can be hedged with weighted variance swaps","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"199 1","pages":"901 - 911"},"PeriodicalIF":1.3,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76394225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1080/14697688.2023.2205583
Hanna Hultin, Henrik Hult, A. Proutière, Samuel Samama, Ala Tarighati
In this work, a generative model based on recurrent neural networks for the complete dynamics of a limit order book is developed. The model captures the dynamics of the limit order book by decomposing the probability of each transition into a product of conditional probabilities of order type, price level, order size and time delay. Each such conditional probability is modelled by a recurrent neural network. Several evaluation metrics for generative models related to trading execution are introduced. Using these metrics, it is demonstrated that the generative model can be successfully trained to fit both synthetic and real data from the Nasdaq Stockholm exchange.
{"title":"A generative model of a limit order book using recurrent neural networks","authors":"Hanna Hultin, Henrik Hult, A. Proutière, Samuel Samama, Ala Tarighati","doi":"10.1080/14697688.2023.2205583","DOIUrl":"https://doi.org/10.1080/14697688.2023.2205583","url":null,"abstract":"In this work, a generative model based on recurrent neural networks for the complete dynamics of a limit order book is developed. The model captures the dynamics of the limit order book by decomposing the probability of each transition into a product of conditional probabilities of order type, price level, order size and time delay. Each such conditional probability is modelled by a recurrent neural network. Several evaluation metrics for generative models related to trading execution are introduced. Using these metrics, it is demonstrated that the generative model can be successfully trained to fit both synthetic and real data from the Nasdaq Stockholm exchange.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"46 1","pages":"931 - 958"},"PeriodicalIF":1.3,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90773560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1080/14697688.2023.2203844
A. Lo, Manish Singh
The measurement of financial risk premia, the amount that a risky asset will outperform a risk-free one, is an important problem in asset pricing. The noisiness and non-stationarity of asset returns makes the estimation of risk premia using machine learning (ML) techniques challenging. In this work, we develop ML models that solve the problems associated with risk premia forecasting by separating risk premia prediction into two independent tasks, a time series model and a cross-sectional model, and using neural networks with skip connections to enable their deep neural network training. These models are tested robustly with different metrics, and we observe that our models outperform several existing standard ML models. A known issue with ML models is their ‘black box’ nature, i.e. their opaqueness to interpretability. We interpret these deep neural networks using local approximation-based techniques that provide explanations for our model's predictions.
{"title":"Deep-learning models for forecasting financial risk premia and their interpretations","authors":"A. Lo, Manish Singh","doi":"10.1080/14697688.2023.2203844","DOIUrl":"https://doi.org/10.1080/14697688.2023.2203844","url":null,"abstract":"The measurement of financial risk premia, the amount that a risky asset will outperform a risk-free one, is an important problem in asset pricing. The noisiness and non-stationarity of asset returns makes the estimation of risk premia using machine learning (ML) techniques challenging. In this work, we develop ML models that solve the problems associated with risk premia forecasting by separating risk premia prediction into two independent tasks, a time series model and a cross-sectional model, and using neural networks with skip connections to enable their deep neural network training. These models are tested robustly with different metrics, and we observe that our models outperform several existing standard ML models. A known issue with ML models is their ‘black box’ nature, i.e. their opaqueness to interpretability. We interpret these deep neural networks using local approximation-based techniques that provide explanations for our model's predictions.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"5 1","pages":"917 - 929"},"PeriodicalIF":1.3,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88956652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-09DOI: 10.1080/14697688.2023.2200406
M. Dempster
{"title":"Real Time Computing (NATO ASI Series. Series F, Computer and Systems Sciences, Vol. 127)","authors":"M. Dempster","doi":"10.1080/14697688.2023.2200406","DOIUrl":"https://doi.org/10.1080/14697688.2023.2200406","url":null,"abstract":"","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"52 1","pages":"1053 - 1054"},"PeriodicalIF":1.3,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85726821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：