Stability, empirical estimates and scenario generation in stochastic optimization - applications in finance

Vlasta Kaňková

Kybernetika (2017)

  • Volume: 53, Issue: 6, page 1026-1046
  • ISSN: 0023-5954

Abstract

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Economic and financial processes are mostly simultaneously influenced by a random factor and a decision parameter. While the random factor can be hardly influenced, the decision parameter can be usually determined by a deterministic optimization problem depending on a corresponding probability measure. However, in applications the “underlying” probability measure is often a little different, replaced by empirical one determined on the base of data or even (for numerical reason) replaced by simpler (mostly discrete) one. Consequently, real one and approximate one correspond to applications. In the paper we try to investigate their relationship. To this end we employ the results on stability based on the Wasserstein metric and ℒ 1 norm, their applications to empirical estimates and scenario generation. Moreover, we apply the achieved new results to simple financial applications. The corresponding model will a problem of stochastic programming.

How to cite

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Kaňková, Vlasta. "Stability, empirical estimates and scenario generation in stochastic optimization - applications in finance." Kybernetika 53.6 (2017): 1026-1046. <http://eudml.org/doc/294823>.

@article{Kaňková2017,
abstract = {Economic and financial processes are mostly simultaneously influenced by a random factor and a decision parameter. While the random factor can be hardly influenced, the decision parameter can be usually determined by a deterministic optimization problem depending on a corresponding probability measure. However, in applications the “underlying” probability measure is often a little different, replaced by empirical one determined on the base of data or even (for numerical reason) replaced by simpler (mostly discrete) one. Consequently, real one and approximate one correspond to applications. In the paper we try to investigate their relationship. To this end we employ the results on stability based on the Wasserstein metric and $\{\mathcal \{L\}\}_\{1\}$ norm, their applications to empirical estimates and scenario generation. Moreover, we apply the achieved new results to simple financial applications. The corresponding model will a problem of stochastic programming.},
author = {Kaňková, Vlasta},
journal = {Kybernetika},
keywords = {stochastic programming problems; probability constraints; stochastic dominance; stability; Wasserstein metric; $\{\mathcal \{L\}\}_\{1\}$ norm; Lipschitz property; empirical estimates; scenario; error approximation; financial applications; loan; debtor; installments; mortgage; bank},
language = {eng},
number = {6},
pages = {1026-1046},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Stability, empirical estimates and scenario generation in stochastic optimization - applications in finance},
url = {http://eudml.org/doc/294823},
volume = {53},
year = {2017},
}

TY - JOUR
AU - Kaňková, Vlasta
TI - Stability, empirical estimates and scenario generation in stochastic optimization - applications in finance
JO - Kybernetika
PY - 2017
PB - Institute of Information Theory and Automation AS CR
VL - 53
IS - 6
SP - 1026
EP - 1046
AB - Economic and financial processes are mostly simultaneously influenced by a random factor and a decision parameter. While the random factor can be hardly influenced, the decision parameter can be usually determined by a deterministic optimization problem depending on a corresponding probability measure. However, in applications the “underlying” probability measure is often a little different, replaced by empirical one determined on the base of data or even (for numerical reason) replaced by simpler (mostly discrete) one. Consequently, real one and approximate one correspond to applications. In the paper we try to investigate their relationship. To this end we employ the results on stability based on the Wasserstein metric and ${\mathcal {L}}_{1}$ norm, their applications to empirical estimates and scenario generation. Moreover, we apply the achieved new results to simple financial applications. The corresponding model will a problem of stochastic programming.
LA - eng
KW - stochastic programming problems; probability constraints; stochastic dominance; stability; Wasserstein metric; ${\mathcal {L}}_{1}$ norm; Lipschitz property; empirical estimates; scenario; error approximation; financial applications; loan; debtor; installments; mortgage; bank
UR - http://eudml.org/doc/294823
ER -

References

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