arXiv · arXiv q-fin · 2023
Monroe (1978) demonstrates that any local semimartingale can be represented as a time-changed Brownian Motion (BM). A natural question arises: does this representation theorem hold when the BM and the time-change are independent? We prove that a local semimartingale is not equivalent to a BM with a time-change that is independent from the BM. Our result is obtained utilizing a class of additive processes: the additiv…
Michele Azzone, Roberto Baviera
arXiv · arXiv q-fin · 2018
In this study, we focus on the market clearing problem of Turkish day-ahead electricity market. We propose a mathematical model by extending the variety of bid types for different price regions. The commercial solvers may not find any feasible solution for the proposed problem in some instances within the given time limits. Hence, we design an adaptive tabu search (ATS) algorithm to solve the problem. ATS discretizes…
Nermin Elif Kurt, H. Bahadir Sahin, Kürşad Derinkuyu
arXiv · arXiv q-fin · 2021
Causal learning is the key to obtaining stable predictions and answering \textit{what if} problems in decision-makings. In causal learning, it is central to seek methods to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE. However, the DML estimators can suffer from an \textit{error-compounding issue} and ev…
Yiyan Huang, Cheuk Hang Leung, Qi Wu, Xing Yan
arXiv · arXiv q-fin · 2019
This paper questions some current ideas about the practice of specific capital market operations - the so-called day trading operations. The text advanced from theoretical propositions to a detailed analysis of the study entitled "Is it possible to live by day-trading?" (CHAGUE and GIOVANNETTI, 2019), to which it offers a counterpoint. This investigation reveal the existence of important elements that are not yet pro…
Roberto Ernani Porcher Junior
arXiv · arXiv q-fin · 2016
It has been recently shown that numerical semiparametric bounds on the expected payoff of fi- nancial or actuarial instruments can be computed using semidefinite programming. However, this approach has practical limitations. Here we use column generation, a classical optimization technique, to address these limitations. From column generation, it follows that practical univari- ate semiparametric bounds can be found …
Robert Howley, Robert Storer, Juan Vera, Luis F. Zuluaga