Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):271-279, 2013.
Abstract
Starting from a multivariate data set, we study several techniques to isolate affine combinations of the variables with a maximum amount of mean reversion, while constraining the variance to be larger than a given threshold. We show that many of the optimization problems arising in this context can be solved exactly using semidefinite programming and some variant of the \mathcalS-lemma. In finance, these methods are used to isolate statistical arbitrage opportunities, i.e. mean reverting portfolios with enough variance to overcome market friction. In a more general setting, mean reversion and its generalizations are also used as a proxy for stationarity, while variance simply measures signal strength.
@InProceedings{pmlr-v28-cuturi13,
title = {Mean Reversion with a Variance Threshold},
author = {Marco Cuturi and Alexandre D’Aspremont},
booktitle = {Proceedings of the 30th International Conference on Machine Learning},
pages = {271--279},
year = {2013},
editor = {Sanjoy Dasgupta and David McAllester},
volume = {28},
number = {3},
series = {Proceedings of Machine Learning Research},
address = {Atlanta, Georgia, USA},
month = {17--19 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v28/cuturi13.pdf},
url = {http://proceedings.mlr.press/v28/cuturi13.html},
abstract = {Starting from a multivariate data set, we study several techniques to isolate affine combinations of the variables with a maximum amount of mean reversion, while constraining the variance to be larger than a given threshold. We show that many of the optimization problems arising in this context can be solved exactly using semidefinite programming and some variant of the \mathcalS-lemma. In finance, these methods are used to isolate statistical arbitrage opportunities, i.e. mean reverting portfolios with enough variance to overcome market friction. In a more general setting, mean reversion and its generalizations are also used as a proxy for stationarity, while variance simply measures signal strength.}
}
%0 Conference Paper
%T Mean Reversion with a Variance Threshold
%A Marco Cuturi
%A Alexandre D’Aspremont
%B Proceedings of the 30th International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2013
%E Sanjoy Dasgupta
%E David McAllester
%F pmlr-v28-cuturi13
%I PMLR
%J Proceedings of Machine Learning Research
%P 271--279
%U http://proceedings.mlr.press
%V 28
%N 3
%W PMLR
%X Starting from a multivariate data set, we study several techniques to isolate affine combinations of the variables with a maximum amount of mean reversion, while constraining the variance to be larger than a given threshold. We show that many of the optimization problems arising in this context can be solved exactly using semidefinite programming and some variant of the \mathcalS-lemma. In finance, these methods are used to isolate statistical arbitrage opportunities, i.e. mean reverting portfolios with enough variance to overcome market friction. In a more general setting, mean reversion and its generalizations are also used as a proxy for stationarity, while variance simply measures signal strength.
TY - CPAPER
TI - Mean Reversion with a Variance Threshold
AU - Marco Cuturi
AU - Alexandre D’Aspremont
BT - Proceedings of the 30th International Conference on Machine Learning
PY - 2013/02/13
DA - 2013/02/13
ED - Sanjoy Dasgupta
ED - David McAllester
ID - pmlr-v28-cuturi13
PB - PMLR
SP - 271
DP - PMLR
EP - 279
L1 - http://proceedings.mlr.press/v28/cuturi13.pdf
UR - http://proceedings.mlr.press/v28/cuturi13.html
AB - Starting from a multivariate data set, we study several techniques to isolate affine combinations of the variables with a maximum amount of mean reversion, while constraining the variance to be larger than a given threshold. We show that many of the optimization problems arising in this context can be solved exactly using semidefinite programming and some variant of the \mathcalS-lemma. In finance, these methods are used to isolate statistical arbitrage opportunities, i.e. mean reverting portfolios with enough variance to overcome market friction. In a more general setting, mean reversion and its generalizations are also used as a proxy for stationarity, while variance simply measures signal strength.
ER -
Cuturi, M. & D’Aspremont, A.. (2013). Mean Reversion with a Variance Threshold. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(3):271-279
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