澳洲论文代写-统计套利定量交易策略

在本篇澳洲论文代写-统计套利定量交易策略中，统计套利是一种现有的定量交易策略，已在印度等亚洲国家得到应用。在统计套利技术中，在更传统的意义上，获利实际上来自于交易中的错误定价(Glantz和Kissell, 2013)。获利机会被认为是在短期和长期水平上创造的，因为价值相对于回报的变化。交易员可能没有平仓，因为尽管存在这样的风险，但他们被视为利用这些作为阿尔法策略的一种形式。虽然这是传统的统计套利，但定量交易中使用的方法可能略有不同。接下来有关澳洲论文代写-统计套利定量交易策略分析如下：

Statistical arbitrage is an existing quantitative trading strategy that has been made use of in Asian countries such as India and others. In the statistical arbitrage technique, in a more traditional sense the profit making is actually inspired from the mispricing that is seen in trading (Glantz, and Kissell, 2013). Profit making opportunities are seen to be created in both a short term and a long term level because of the value changes relative to the returns. The traders might not have closed out position because although such a risk exists, the traders are seen to make use of these as a form of alpha strategy. While this is the traditional statistical arbitrage, the method that is used in Quantitative Trading would be slightly variants. in the context of Quantitative Trading, there would be better data opportunities and analysis could be done with a much faster computational speed compared to that of the traditional method. In addition, the quant models such as that of pairing and correlation are made use of. Making use of these forms of high speed strategies hence ensures that the investor is not restricted by the holding period that is seen to be the prime concern in the traditional alpha strategies.
Also the time and investment that have to be put into Quantitative Trading is quite enormous considering that the data has to be cleaned up, the platform has to be set such that the data is collected over different time periods, but such records of data might not exist in countries like china where automation is still relatively new. The infrastructure is also lacking in China. Research studies on the strategies and the differences that are present in China seems to indicate that trade itself could be rather too limited for Quantitative Trading to play a major role. There are not much options on which stock trade or borrowing can be carried out on. There is really no need for a fast trade as in the ones that are carried out in a day (Automated Trader, 2012). Here Quantitative Trading which is helpful for trading within a day will not be of much use. Buying and selling in one day is really not considered a necessity. On the other hand, the only place that does seem to support the use of Quantitative Trading is in the form of commodity exchange market. In the commodity exchange markets, in the case of the Dalian and the Shanghai there are chances for the use of trading where Quantitative Trading might be required because these are more regulated than in the case of all mainland china. Even in these areas it is seen that regulators might reduce the number of times a trade is carried out within a day. Now as discussed earlier the reason behind the use of the Quantitative Trading strategy is because of the volumes it can generate. Where the volumes are controlled already then it is really not necessary to have any form of Quantitative Trading (Automated Trader, 2012).

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