### Abstract

We consider a buying-selling problem with a finite time horizon when two stops of a sequence of dependent observations can be made. The aim is to find an optimal sequential procedure which maximizes the total expected revenue. In this paper, we obtain an optimal double stopping rule and apply it for a geometric random walk and an autoregressive sequence.

Language | English |
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Journal | Methodology and Computing in Applied Probability |

Early online date | 3 Nov 2018 |

DOIs | |

Publication status | E-pub ahead of print - 3 Nov 2018 |

### Fingerprint

### Keywords

- Autoregressive sequence
- Buying-selling problem
- Geometric random walk
- Optimal stopping rules
- Sequential decision analysis

### Cite this

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**An optimal double stopping rule for a buying-selling problem.** / Sofronov, Georgy Yu.

Research output: Contribution to journal › Article › Research › peer-review

TY - JOUR

T1 - An optimal double stopping rule for a buying-selling problem

AU - Sofronov, Georgy Yu

PY - 2018/11/3

Y1 - 2018/11/3

N2 - We consider a buying-selling problem with a finite time horizon when two stops of a sequence of dependent observations can be made. The aim is to find an optimal sequential procedure which maximizes the total expected revenue. In this paper, we obtain an optimal double stopping rule and apply it for a geometric random walk and an autoregressive sequence.

AB - We consider a buying-selling problem with a finite time horizon when two stops of a sequence of dependent observations can be made. The aim is to find an optimal sequential procedure which maximizes the total expected revenue. In this paper, we obtain an optimal double stopping rule and apply it for a geometric random walk and an autoregressive sequence.

KW - Autoregressive sequence

KW - Buying-selling problem

KW - Geometric random walk

KW - Optimal stopping rules

KW - Sequential decision analysis

UR - http://www.scopus.com/inward/record.url?scp=85056115953&partnerID=8YFLogxK

U2 - 10.1007/s11009-018-9684-6

DO - 10.1007/s11009-018-9684-6

M3 - Article

JO - Methodology and Computing in Applied Probability

T2 - Methodology and Computing in Applied Probability

JF - Methodology and Computing in Applied Probability

SN - 1387-5841

ER -