Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 3

first rewind previous Page / 1 next fast forward last

Search results

Search:
in the keywords:  trading strategy
help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Publication available in full text mode
Content available

On upper gain bound for trading strategy based on cointegration

100%
Łochowski R.
Metody Ilościowe w Badaniach Ekonomicznych
|
2010
|
vol. 11
|
issue 1
110-117
EN
A long-run trading strategy based on cointegration relationship between prices of two commodities is considered. A linear combination of the prices is assumed to be a stationary AR(1) process. In some range of parameters, AR(1) process is obtained by discrete sampling of Ornstein-Uhlenbeck process. This allows to calculate approximate number of transactions in long run trade horizon and obtain approximate upper bound for possible gain.
2
Publication available in full text mode
Content available

Portfolio management of a small RES utility with a structural vector autoregressive model of electricity markets in Germany

88%
Maciejowska K.
Operations Research and Decisions
|
2022
|
vol. 32
|
issue 4
EN
Electricity producers and traders are exposed to various risks, among which price and volume risk play very important roles. This research considers portfolio-building strategies that enable the proportion of electricity traded in different electricity markets (day-ahead and intraday) to be chosen dynamically. Two types of approaches are considered: a simple strategy, which assumes that these proportions are fixed, and a data-driven strategy, in which the ratios fluctuate. To explore the market information, a structural vector autoregressive model is applied, which allows one to estimate the relationship between the variables of interest and simulate their future distribution. The approach is evaluated using data from the electricity market in Germany. The outcomes indicate that data-driven strategies increase revenue and reduce trading risk. These financial gains may encourage energy traders to apply advanced statistical methods in their portfolio-building process.
3
Publication available in full text mode
Content available

ON AN UPPER GAIN BOUND FOR STRATEGIES WITH CONSTANT AND PROPORTIONAL NUMBER OF ASSETS TRADED

63%
Łochowski R.
Metody Ilościowe w Badaniach Ekonomicznych
|
2013
|
vol. 14
|
issue 2
29-38
EN
We introduce general formulas for the upper bound of gain obtained from any finite-time trading strategy in discrete and continuous time models. We consider strategies with constant number of assets traded and strategies with proportional number of assets traded. Unfortunately, the estimates obtained in the discrete case become trivial in the continuous case, hence we introduce transaction costs. This leads to the interesting estimates in terms of the so called truncated variation of the price series. We apply the obtained estimates in specific cases of financial time series.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.