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Stochastic Control and Optimization with Financial Applications

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Prof. Dr. Zbigniew Palmowski

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Department of Applied Mathematics, Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology ul. Janiszewskiego 14a, 50-372 Wrocław, Poland
Interests: applied probability; ruin theory; quantitative finance; stochastic control and optimization; extreme value theory; options; simulations

Dr. Budhi Surya

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School of Mathematics and Statistics, Victoria University of Wellington, Gate 6 Kelburn PDE, Wellington 6140, New Zealand
Interests: actuarial science; financial stochastics; optimal capital structure; optimal portfolio; optimal stopping and free-boundary problem of Levy process; applied probability and stochastic modeling; statistical inference for a finite general mixture; regime switching of Markov jump processes

Special Issue Information

Dear Colleagues,

Stochastic control and optimization has been an active area of research since 1970s, but has recently enjoyed particular revival due to applications in, inter alia, operations research, economics and social sciences, finance.

This call for papers seeks to publish applied work that links stochastic control and optimization with theories of stochastic processes, stochastic calculus, differential equations, filtering theory or game theory. Papers may be theoretical or applied. In particular, we are interested in papers related to numerical algorithms appearing in finance.

To be considered for publication in the Special Issue, please submit your manuscript via the online submission portal. All submissions will be peer-reviewed. Any questions about the Special Issue can be directed to Zbigniew Palmowski at [email protected].

Prof. Dr. Zbigniew Palmowski
Dr. Budhi Surya
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

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Research

22 pages, 510 KiB

Open AccessArticle

A Computational Approach to Sequential Decision Optimization in Energy Storage and Trading

by Paolo Falbo, Juri Hinz, Piyachat Leelasilapasart and Cristian Pelizzari

J. Risk Financial Manag. 2021, 14(6), 235; https://doi.org/10.3390/jrfm14060235 - 24 May 2021

Viewed by 1834

Abstract

Due to recent technical progress, battery energy storages are becoming a viable option in the power sector. Their optimal operational management focuses on load shift and shaving of price spikes. However, this requires optimally responding to electricity demand, intermittent generation, and volatile electricity prices. More importantly, such optimization must take into account the so-called deep discharge costs, which have a significant impact on battery lifespan. We present a solution to a class of stochastic optimal control problems associated with these applications. Our numerical techniques are based on efficient algorithms which deliver a guaranteed accuracy. Full article

(This article belongs to the Special Issue Stochastic Control and Optimization with Financial Applications)

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19 pages, 2845 KiB

Open AccessArticle

Pricing Perpetual American Put Options with Asset-Dependent Discounting

by Jonas Al-Hadad and Zbigniew Palmowski

J. Risk Financial Manag. 2021, 14(3), 130; https://doi.org/10.3390/jrfm14030130 - 20 Mar 2021

Cited by 1 | Viewed by 2580

Abstract

The main objective of this paper is to present an algorithm of pricing perpetual American put options with asset-dependent discounting. The value function of such an instrument can be described as [...] Read more.

The main objective of this paper is to present an algorithm of pricing perpetual American put options with asset-dependent discounting. The value function of such an instrument can be described as

V_{A^{Put}}^{ω} (s) = {sup}_{τ \in T} E_{s} [e^{- \int_{0}^{τ} ω (S_{w}) d w} {(K - S_{τ})}^{+}],

where

T

is a family of stopping times,

ω

is a discount function and

E

is an expectation taken with respect to a martingale measure. Moreover, we assume that the asset price process

S_{t}

is a geometric Lévy process with negative exponential jumps, i.e.,

S_{t} = s e^{ζ t + σ B_{t} - \sum_{i = 1}^{N_{t}} Y_{i}}

. The asset-dependent discounting is reflected in the

ω

function, so this approach is a generalisation of the classic case when

ω

is constant. It turns out that under certain conditions on the

ω

function, the value function

V_{A^{Put}}^{ω} (s)

is convex and can be represented in a closed form. We provide an option pricing algorithm in this scenario and we present exact calculations for the particular choices of

ω

such that

V_{A^{Put}}^{ω} (s)

takes a simplified form. Full article

(This article belongs to the Special Issue Stochastic Control and Optimization with Financial Applications)

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