Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Marco Avellaneda"'
Publikováno v:
SIAM Journal on Financial Mathematics. 13:702-744
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e99c60c2c3af7864ebcdc3abca16c1d6
https://doi.org/10.1137/20m1383501
https://doi.org/10.1137/20m1383501
Autor:
Marco Avellaneda
This invaluable book contains lectures presented at the Courant Institute's Mathematical Finance Seminar. The audience consisted of academics from New York University and other universities, as well as practitioners from investment banks, hedge funds
Publikováno v:
The Journal of Financial Data Science. 2:85-109
Principal component analysis (PCA) is a useful tool when trying to construct factor models from historical asset returns. For the implied volatilities of US equities, there is a PCA-based model with a principal eigenportfolio whose return time series
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ded9ca5f4c73d8e3a67255e117edbf4e
https://doi.org/10.3905/jfds.2020.1.032
https://doi.org/10.3905/jfds.2020.1.032
Autor:
Marco Avellaneda
Publikováno v:
Revista Mexicana de Economía y Finanzas. 15:1-16
Asset returns in a multivariate market in which securities are grouped into sectors or blocks (e.g. GIC sectors, derivatives associated with different underlying assets). It is widely known that risk-factors derived from PCA beyond the first eigenpor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1004ce879ef7dde1b9370775f71be101
https://doi.org/10.21919/remef.v15i1.446
https://doi.org/10.21919/remef.v15i1.446
Autor:
Marco Avellaneda, Irene Aldridge
Publikováno v:
The Journal of Financial Data Science. 1:39-62
Neural networks have piqued the interest of many financial modelers, but the concrete applications and implementation have remained elusive. This article discusses a step-by-step technique for building a potentially profitable financial neural networ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56b64efd5041fbb318e0a98857ee6f94
https://doi.org/10.3905/jfds.2019.1.4.039
https://doi.org/10.3905/jfds.2019.1.4.039
We propose a new approach for trading VIX futures. We assume that the term structure of VIX futures follows a Markov model. Our trading strategy selects a position in VIX futures by maximizing the expected utility for a day-ahead horizon given the cu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ecfb36bcab4d3863e435202cf4895cdc
Publikováno v:
The Journal of Investment Strategies.
This paper discusses portfolio construction for investing in N given assets, eg, constituents of the Dow Jones Industrial Average (DJIA) or large cap stocks, based on partitioning the investment universe into clusters. The clusters are determined fro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b7c7288aafa9cf4b4ec3a479ddacc7f2
https://doi.org/10.21314/jois.2021.006
https://doi.org/10.21314/jois.2021.006
Publikováno v:
SSRN Electronic Journal.
We analyze portfolios constructed from the principal eigenvector of the equity returns' correlation matrix and compare how well these portfolios track the capitalization weighted market portfolio. It is well known empirically that principal eigenport
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::594181fed6ce54fa997165051257e949
https://doi.org/10.2139/ssrn.3738769
https://doi.org/10.2139/ssrn.3738769
Autor:
Juan A. Serur, Marco Avellaneda
Publikováno v:
SSRN Electronic Journal.
Modeling cross-sectional correlations between thousands of stocks, acrosscountries and industries, can be challenging. In this paper, we demonstratethe advantages of using Hierarchical Principal Component Analysis (HPCA)over the classic PCA. We also
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bf81bc5ace619d4cdb44979cd2986284
https://doi.org/10.2139/ssrn.3903460
https://doi.org/10.2139/ssrn.3903460
Autor:
Irene Aldridge, Marco Avellaneda
Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. B