SSRN Author: Giacomo BormettiGiacomo Bormetti SSRN Content
https://privwww.ssrn.com/author=1819664
https://privwww.ssrn.com/rss/en-usThu, 20 May 2021 01:19:19 GMTeditor@ssrn.com (Editor)Thu, 20 May 2021 01:19:19 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: The SINC way: A fast and accurate approach to Fourier pricingThe goal of this paper is to investigate the method outlined by one of us (PR) in Cherubini et al. (2009) to compute option prices. We name it the SINC approach. While the COS method by Fang and Osterlee (2009) leverages the Fourier-cosine expansion of truncated densities, the SINC approach builds on the Shannon Sampling Theorem revisited for functions with bounded support. We provide several results which were missing in the early derivation: i) a rigorous proof of the convergence of the SINC formula to the correct option price when the support grows and the number of Fourier frequencies increases; ii) ready to implement formulas for put, Cash-or-Nothing, and Asset-or-Nothing options; iii) a systematic comparison with the COS formula for several log-price models; iv) a numerical challenge against alternative Fast Fourier specifications, such as Carr and Madan (1999) and Lewis (2000); v) an extensive pricing exercise under the rough Heston model of Jaisson and Rosenbaum (2015); vi) ...
https://privwww.ssrn.com/abstract=3684706
https://privwww.ssrn.com/2026279.htmlWed, 19 May 2021 10:36:37 GMTREVISION: Filtering and Smoothing with Score-Driven ModelsWe propose a methodology for filtering, smoothing and assessing parameter and filtering uncertainty in score-driven models. Our technique is based on a general representation of the Kalman filter and smoother recursions for linear Gaussian models in terms of the score of the conditional log-likelihood. We prove that, when data is generated by a nonlinear non-Gaussian state-space model, the proposed methodology results from a local expansion of the true filtering density. A formal characterization of the approximation error is provided. As shown in extensive Monte Carlo analyses, our methodology performs very similarly to exact simulation-based methods, while remaining computationally extremely simple. We illustrate empirically the advantages in employing score-driven models as approximate filters rather than purely predictive processes.
https://privwww.ssrn.com/abstract=3139666
https://privwww.ssrn.com/1993613.htmlMon, 22 Feb 2021 09:24:39 GMTREVISION: Rough Heston: The SINC wayThe goal of this paper is to investigate the method outlined by one of us (PR) in Cherubini et al. (2009) to compute option prices. We named it the SINC approach. While the COS method by Fang and Osterlee (2009) leverages the Fourier-cosine expansion of truncated densities, the SINC approach builds on the Shannon Sampling Theorem revisited for functions with bounded support. We provide several important results which were missing in the early derivation: i) a rigorous proof of the converge of the SINC formula to the correct option price when the support growths and the number of Fourier frequencies increases; ii) ready to implement formulas for put, Cash-or-Nothing, and Asset-or-Nothing options; iii) a systematic comparison with the COS formula in several settings; iv) a numerical challenge against alternative Fast Fourier specifications, such as Carr and Madan (1999) and Lewis (2000); v) an extensive pricing exercise under the rough Heston model of Jaisson and Rosenbaum (2015); vi) ...
https://privwww.ssrn.com/abstract=3684706
https://privwww.ssrn.com/1953405.htmlTue, 20 Oct 2020 11:51:30 GMTREVISION: A Tale of Two Sentiment Scales: Disentangling Short-Run and Long-Run Components in Multivariate Sentiment DynamicsWe propose a novel approach to sentiment data filtering for a portfolio of assets. In our framework, a dynamic factor model drives the evolution of the observed sentiment and allows to identify two distinct components: a long-term component, modeled as a random walk, and a short-term component driven by a stationary VAR(1) process. Our model encompasses alternative approaches available in literature and can be readily estimated by means of Kalman filtering and expectation maximization. This feature makes it convenient when the cross-sectional dimension of the portfolio increases. By applying the model to a portfolio of Dow Jones stocks, we find that the long term component co-integrates with the market principal factor, while the short term one captures transient swings of the market associated with the idiosyncratic components and captures the correlation structure of returns. Using quantile regressions, we assess the significance of the contemporaneous and lagged explanatory power ...
https://privwww.ssrn.com/abstract=3463691
https://privwww.ssrn.com/1939172.htmlTue, 08 Sep 2020 10:35:33 GMTREVISION: Deep Learning Profit & LossBuilding the future profit and loss (P&L) distribution of a portfolio holding, among other assets, highly non-linear and path-dependent derivatives is a challenging task. We provide a simple machinery where more and more assets could be accounted for in a simple and semi-automatic fashion. We resort to a variation of the Least Square Monte Carlo algorithm where interpolation of the continuation value of the portfolio is done with a feed forward neural network. This approach has several appealing features not all of them will be fully discussed in the paper. Neural networks are extremely flexible regressors. We do not need to worry about the fact that for multi assets payoff, the exercise surface could be non connected. Neither we have to search for smart regressors. The idea is to use, regardless of the complexity of the payoff, only the underlying processes. Neural networks with many outputs can interpolate every single assets in the portfolio generated by a single Monte Carlo ...
https://privwww.ssrn.com/abstract=3629365
https://privwww.ssrn.com/1935687.htmlThu, 27 Aug 2020 09:34:25 GMTREVISION: Deep Learning Profit & LossBuilding the future profit and loss (P&L) distribution of a portfolio holding, among other assets, highly non-linear and path-dependent derivatives is a challenging task. We provide a simple machinery where more and more assets could be accounted for in a simple and semi-automatic fashion. We resort to a variation of the Least Square Monte Carlo algorithm where interpolation of the continuation value of the portfolio is done with a feed forward neural network. This approach has several appealing features. Neural networks are extremely flexible regressors. We do not need to worry about the fact that for multi assets payoff, the exercise surface could be non connected. Neither we have to search for smart regressors. The idea is to use, regardless of the complexity of the payoff, only the underlying processes. Neural networks with many outputs can interpolate every single assets in the portfolio generated by a single Monte Carlo simulation. This is an essential feature to account ...
https://privwww.ssrn.com/abstract=3629365
https://privwww.ssrn.com/1919700.htmlFri, 10 Jul 2020 12:58:10 GMTREVISION: A Tale of Two Sentiment Scales: Disentangling Short-Run and Long-Run Components in Multivariate Sentiment DynamicsWe propose a novel approach to sentiment data filtering for a portfolio of assets. In our framework, a dynamic factor model drives the evolution of the observed sentiment and allows to identify two distinct components: a long-term component, modeled as a random walk, and a short-term component driven by a stationary VAR(1) process. Our model encompasses alternative approaches available in literature and can be readily estimated by means of Kalman filtering and expectation maximization. This feature makes it convenient when the cross-sectional dimension of the portfolio increases. By applying the model to a portfolio of Dow Jones stocks, we find that the long term component co-integrates with the market principal factor, while the short term one captures transient swings of the market associated with the idiosyncratic components and captures the correlation structure of returns. Using quantile regressions, we assess the significance of the contemporaneous and lagged explanatory power ...
https://privwww.ssrn.com/abstract=3463691
https://privwww.ssrn.com/1917278.htmlTue, 07 Jul 2020 08:36:40 GMT