تکنیک های تجارت جفت
ترجمه نشده

تکنیک های تجارت جفت

عنوان فارسی مقاله: تکنیک های تجارت جفت: یک تقابل تجربی
عنوان انگلیسی مقاله: Pairs trading techniques: An empirical contrast
مجله/کنفرانس: تحقیقات اروپایی در زمینه مدیریت و اقتصاد کسب و کار - European Research on Management and Business Economics
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: مدیریت استراتژیک، مدیریت کسب و کار، مدیریت مالی
کلمات کلیدی فارسی: تجارت جفت، بازار بی طرف، بازگشت به میانگین، هم انباشتگی
کلمات کلیدی انگلیسی: Pairs trading، Market neutral، Mean reversion، Co-integration
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - DOAJ
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.iedeen.2018.05.002
دانشگاه: Rey Juan Carlos University, Department of Business Management, Facultad de Ciencias Jurídicas y Sociales, Paseo Artilleros s/n, Madrid 28933, Spain
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 2 در سال 2018
شاخص H_index: 11 در سال 2019
شاخص SJR: 0/308 در سال 2018
شناسه ISSN: 2444-8834
شاخص Quartile (چارک): Q3 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 8
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11370
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Theory


3- Data and methodology


4- Results


5- Conclusions and discussion


References

نمونه متن انگلیسی مقاله

Abstract


Pairs trading is one of the most commonly used market neutral strategies. Over the last few years, several hedge funds have used different ways to successfully implement this trading strategy. The most extensively used techniques (correlation, distance, stochastic, stochastic differential residual and cointegration) use different methodologies and statistical tools to determine the two key elements of the strategy: pairs selection and the establishment of the long-term relationship between them. The purpose of this paper is to analyze the process of selecting pairs and determining the residual series using each one of the different techniques and comparing the outputs. Results indicate that far from being differentiated systems, relationships exist between the various techniques in terms of pairs selection and residual series creation. However, some techniques are more efficient at creating residual series than others, which then means that these techniques would have the highest probabilities of generating profits. The analysis concludes that cointegration is the most efficient method of structuring a pairs trading strategy.


Introduction


Pairs trading, together with statistical arbitrage and risk arbitrage, has been one of the strategies most commonly used by hedge funds since the end of the 1990s (Nicholas, 2004). This type of strategy seeks to obtain profits from inefficiencies existing in the market, irrespective of whether it is a bull, bear or neutral market. Pairs trading consists of the simultaneous opening of long and short positions in two assets with a balance point between them. In this way, the earnings from a long position cover the losses from a short position and vice versa, meaning that the market risk is close to zero, as is the joint beta strategy. Therefore, the key elements that determine the success of a trade consists of determining the balance point between two securities and the point in time that prices move sufficiently away from the balance point to take positions. Pairs trading is not without risks as a miscalculation of these two elements can lead to a failure of the strategy (Opiela, 2004). Securities volatility is an additional risk that needs to be considered, even if there is a high degree of correlation between the securities (Whistler, 2004). Nevertheless, pairs trading can be used to not only generate profits regardless of market trend, but also to balance a portfolio given its market neutral properties. But to optimize trading results, it is necessary to first select the best method to implement a pairs trading strategy. There are five main techniques that can be utilized to execute a pairs trading strategy. These are: correlation, distance, stochastic, stochastic differential residual and co-integration although other authors mention others such as the machine learning and the time-series methods (Krauss, 2017). These five techniques have been developed and proposed by different authors, however there have been no studies that analyze all of them jointly and under the same conditions. Accordingly, it is necessary to take a general and objective approach to be able to compare and contrast the properties of each one in relation to one another.

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