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Author : Nikita Medvedev
Publisher :
ISBN 13 :
Total Pages : 122 pages
Book Rating : 4.:/5 (114 download)
Download or read book Multi-step Forecast of the Implied Volatility Surface Using Deep Learning written by Nikita Medvedev and published by . This book was released on 2019 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Shengli Chen
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Forecasting Implied Volatility Smile Surface Via Deep Learning and Attention Mechanism written by Shengli Chen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The implied volatility smile surface is the basis of option pricing, and the dynamic evolution of the option volatility smile surface is difficult to predict. In this paper, attention mechanism is introduced into LSTM, and a volatility surface prediction method combining deep learning and attention mechanism is pioneeringly established. LSTM's forgetting gate makes it have strong generalization ability, and its feedback structure enables it to characterize the long memory of financial volatility. The application of attention mechanism in LSTM networks can significantly enhance the ability of LSTM networks to select input features. This paper considers the discrete points of the implied volatility smile surface as an overall prediction target, extracts the daily, weekly, and monthly option implied volatility as input features and establishes a set of LSTM-Attention deep learning systems. Using the dropout mechanism in training reduces the risk of over-fitting. For the prediction results, we use arbitrage-free smoothing to form the final implied volatility smile surface. This article uses the S&P 500 option market to conduct an empirical study. The research shows that the error curve of the LSTM-attention prediction system converges, and the prediction of the implied volatility surface is more accurate than other predicting system. According to the implied volatility surface of the 3-year rolling forecast, the BS formula is used to pricing the option contract, and then a time spread strategy and a butterfly spread strategy are constructed respectively. The experimental results show that the two strategies constructed using the predicted implied volatility surfaces have higher returns and sharp ratios than that the volatility surfaces are not predicted. This paper confirms that the use of AI to predict the implied volatility surface has theoretical and economic value. The research method provides a new reference for option pricing and strategy.
Author : Francesco Audrino
Publisher :
ISBN 13 :
Total Pages : 40 pages
Book Rating : 4.:/5 (129 download)
Download or read book Forecasting Implied Volatility Surfaces written by Francesco Audrino and published by . This book was released on 2013 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper introduces a new semi-parametric methodology for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility. To overcome the poor predicting power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an optimal stopping value for the tree boosting. Back testing the out-of-sample performance on a large data set of implied volatilities on Samp;P 500 options, we provide empirical evidence of the strong predictive potential of our methodology.
Author : Blanka Horvath
Publisher :
ISBN 13 :
Total Pages : 41 pages
Book Rating : 4.:/5 (13 download)
Download or read book Deep Learning Volatility written by Blanka Horvath and published by . This book was released on 2019 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a consistent neural network based calibration method for a number of volatility models-including the rough volatility family-that performs the calibration task within a few milliseconds for the full implied volatility surface.The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling: The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several model families (such as rough volatility models) within the scope of applicability in industry practice. As customary for machine learning, the form in which information from available data is extracted and stored is crucial for network performance. With this in mind we discuss how our approach addresses the usual challenges of machine learning solutions in a financial context (availability of training data, interpretability of results for regulators, control over generalisation errors). We present specific architectures for price approximation and calibration and optimize these with respect different objectives regarding accuracy, speed and robustness. We also find that including the intermediate step of learning pricing functions of (classical or rough) models before calibration significantly improves network performance compared to direct calibration to data.
Author : Babak Mahdavi-Damghani
Publisher :
ISBN 13 :
Total Pages : 27 pages
Book Rating : 4.:/5 (13 download)
Download or read book Machine Learning Techniques for Deciphering Implied Volatility Surface Data in a Hostile Environment written by Babak Mahdavi-Damghani and published by . This book was released on 2019 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: The change subsequent to the sub-prime crisis pushed pressure on decreased financial products complexity, going from exotics to vanilla options but increase in pricing efficiency. We introduce in this paper a more efficient methodology for vanilla option pricing using a scenario based particle filter in a hostile data environment. In doing so we capitalise on the risk factor decomposition of the the Implied Volatility surface Parameterization (IVP) recently introduced in order to define our likelihood function and therefore our sampling methodology taking into consideration arbitrage constraints.
Author : Gilles O. Zumbach
Publisher :
ISBN 13 :
Total Pages : 21 pages
Book Rating : 4.:/5 (129 download)
Download or read book Volatility Forecasts and the At-the-Money Implied Volatility written by Gilles O. Zumbach and published by . This book was released on 2008 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a given time horizon $ DT$, this article explores the relationship between the realized volatility (the volatility that will occur between $t$ and $t DT$), the implied volatility (corresponding to at-the-money option with expiry at $t DT$), and several forecasts for the volatility build from multi-scales linear ARCH processes. The forecasts are derived from the process equations, and the parameters set { it a priori}. An empirical analysis across multiple time horizons $ DT$ shows that a forecast provided by an I-GARCH(1) process (1 time scale) does not capture correctly the dynamic of the realized volatility. An I-GARCH(2) process (2 time scales, similar to GARCH(1,1)) is better, while a long memory LM-ARCH process (multiple time scales) replicates correctly the dynamic of the realized volatility and delivers consistently good forecast for the implied volatility. The relationship between market models for the forward variance and the volatility forecasts provided by ARCH processes is investigated. The structure of the forecast equations is identical, but with different coefficients. Yet the process equations for the variance are very different (postulated for a market model, induced by the process equations for an ARCH model), and not of any usual diffusive type when derived from ARCH.
Download or read book Semi-parametric Implied Volatility Surface Models and Forecasts Based on a Regression Tree-boosting Algorithm written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A new methodology for semi-parametric modelling of implied volatility surfaces is presented. This methodology is dependent upon the development of a feasible estimating strategy in a statistical learning framework. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predicting power of existing models, a grid is included in the region of interest and a cross-validation strategy is implemented to find an optimal stopping value for the boosting procedure. Back testing the out-of-sample performance on a large data set of implied volatilities from S & P 500 options provides empirical evidence of the strong predictive power of the model. Accurate IVS forecasts also for single equity options assist in obtaining reliable trading signals for very profitable pure option trading strategies.
Author : Wayne Ferson
Publisher : MIT Press
ISBN 13 : 0262039370
Total Pages : 497 pages
Book Rating : 4.2/5 (62 download)
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Author : Georgi Dimitroff
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Volatility Model Calibration With Convolutional Neural Networks written by Georgi Dimitroff and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We use a supervised deep convolution neural network to replicate the calibration of the Heston model to equity volatility surfaces. For this purpose we treat the implied volatility surface together with some auxiliary data, namely the strikes and moneyness of the corresponding options and the equity forwards, as a 3-dimensional input tensor for the neural network, in analogy to a colour channel image representation like the RGB. To extract the main features of the input data we are using inception layers with (1;1), (1;3) and (2;1) dimensional kernels. The specific choice is motivated by the no-arbitrage conditions on the call price surface. In terms of a local surface modelling the (1;3) filters with different weights can model the position, slope and curvature in the moneyness direction while the (2;1) filters can model Position and slope in the maturity direction. The neural network has been implemented using the open source library tensorflow.
Author : Nalan Baştürk
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (136 download)
Download or read book A Neural Network with Shared Dynamics for Multi-step Prediction of Value-at-risk and Volatility written by Nalan Baştürk and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Francesco Audrino
Publisher :
ISBN 13 :
Total Pages : 47 pages
Book Rating : 4.:/5 (254 download)
Download or read book Forecasting Implied Volatility Surfaces written by Francesco Audrino and published by . This book was released on 2007 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Sergio Andrés González Orjuela
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (115 download)
Download or read book Volatility Trading with Machine Learning Forecasting Methods written by Sergio Andrés González Orjuela and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility trading has become a prominent alternative to the traditional stock trading as the rapid development of web-trading in recent years has reduced significantly the costs of operating in the market. Moreover, machine learning techniques have enabled traders to rely heavily on statistical decision-making models to enhance the commonly used technical analysis. In this paper, a machine learning approach is used to predict proxies of short-term implied volatility clusters with high-frequency data, in order to perform trading strategies using vanilla options on a commercial platform. The empirical results indicate that tree-based methods outperform linear models in classifying these clusters using the time of the day as a key variable in the forecasting task. Financial results were mixed due to the high costs of operating in a 5-hour horizon, but it was found that long positions on at the money straddle strategies expiring in one day were profitable. The framework developed here can be used by small investors as a guidance to implement and assess theoretical strategies in accessible markets.
Author : Yiyi Wang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (86 download)
Download or read book Rational Bubble, Short-dated Volatility Forecasting and Extract More from the Volatility Surface written by Yiyi Wang and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The thesis covers three main chapters. The first chapter (which is a joint work) we develop a theoretical model of rational bubble. In equilibrium, a bubble can persist until it bursts following an exogenous shock, even when all the agents are aware of the bubble and that it will burst in finite time. Applying the model in the context of the sub-prime mortgage crisis, we argue that excessive sub-prime lending behaviour may be sensible with the introduction of securitization. We thus provide a rational explanation for the housing bubble and the dramatic increase in subprime default rates. In the second chapter I conduct empirical short-dated volatility forecasting in foreign exchange, and carry out a realistic volatility swap trading strategy based on the forecast. Additional to applying regime-switch technique, I propose a double-step approach to circumvent the disadvantage of employing GARCH-type model in the high frequency data in FX market, so that it can separate the effect of intraday/intraweek seasonality and pre-scheduled macroeconomic data releases from the underlying data process. By keeping a battery of models and rotating among them, the forecast ability gets significantly enhanced and the trading profit is pronounced even after considering transaction cost. In the third chapter I explore the cross-sectional predictive power of the most important two factors in the implied volatility surface - skew and term structure - at individual firm level. Stocks with lower implied volatility skew and higher implied volatility term structure outperform the comparative peers. In particular, the interaction between these two factors reinforces the predictive power, and the return of a weekly long-short strategy can be improved greatly with the attachment of term structure on skew. By sorting firms based on skew and term structure one may also be able to pick up takeover targets and seize the big positive premium.
Author : Tim Hess
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (136 download)
Download or read book Volatility Forecasting with Machine Learning Methods written by Tim Hess and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Rama Cont
Publisher :
ISBN 13 :
Total Pages : 16 pages
Book Rating : 4.:/5 (129 download)
Download or read book Stochastic Models of Implied Volatility Surfaces written by Rama Cont and published by . This book was released on 2002 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a market-based approach to the modelling of implied volatility, in which the implied volatility surface is directly used as the state variable to describe the joint evolution of market prices of options and their underlying asset. We model the evolution of an implied volatility surface by representing it as a randomly fluctuating surface driven by a finite number of orthogonal random factors. Our approach is based on a Karhunen-Loeve decomposition of the daily variations of implied volatilities obtained from market data on SP500 and DAX options.We illustrate how this approach extends and improves the accuracy of the well-known 'sticky moneyness' rule used by option traders for updating implied volatilities. Our approach gives a justification for the use of 'Vegas' for measuring volatility risk and provides a decomposition of volatility risk as a sum of independent contributions from empirically identifiable factors.
Author : 邱馨儀
Publisher :
ISBN 13 :
Total Pages : 61 pages
Book Rating : 4.:/5 (772 download)
Download or read book Local Volatility Forecasts from Implied Volatility Surfaces written by 邱馨儀 and published by . This book was released on 2007 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Lucas Camelo Sa
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)
Download or read book Applications of Deep Learning to Financial Time Series Forecasting written by Lucas Camelo Sa and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has recently risen as a dominant technique in a variety of settings comprising large-scale and high-dimensional data. In the particular case of financial modeling, one of the most important data analysis problems consists of predicting the future volatility of a given asset. In this thesis, we investigate how the Transformer architecture performs at the task of volatility forecasting by comparing its performance against that of previously explored deep learning architectures such as the LSTM.
