英文标题：
《Mean-Reverting Portfolios: Tradeoffs Between Sparsity and Volatility》
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作者：
Marco Cuturi, Alexandre d\'Aspremont
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最新提交年份：
2015
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英文摘要：
  Mean-reverting assets are one of the holy grails of financial markets: if such assets existed, they would provide trivially profitable investment strategies for any investor able to trade them, thanks to the knowledge that such assets oscillate predictably around their long term mean. The modus operandi of cointegration-based trading strategies [Tsay, 2005, {\\S}8] is to create first a portfolio of assets whose aggregate value mean-reverts, to exploit that knowledge by selling short or buying that portfolio when its value deviates from its long-term mean. Such portfolios are typically selected using tools from cointegration theory [Engle and Granger, 1987, Johansen, 1991], whose aim is to detect combinations of assets that are stationary, and therefore mean-reverting. We argue in this work that focusing on stationarity only may not suffice to ensure profitability of cointegration-based strategies. While it might be possible to create syn- thetically, using a large array of financial assets, a portfolio whose aggre- gate value is stationary and therefore mean-reverting, trading such a large portfolio incurs in practice important trade or borrow costs. Looking for stationary portfolios formed by many assets may also result in portfolios that have a very small volatility and which require significant leverage to be profitable. We study in this work algorithmic approaches that can take mitigate these effects by searching for maximally mean-reverting portfo- lios which are sufficiently sparse and/or volatile.
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中文摘要：
均值回复资产是金融市场的圣杯之一：如果这类资产存在，它们将为任何能够交易它们的投资者提供微利的投资策略，因为他们知道这类资产会在其长期均值附近以可预测的方式波动。基于协整的交易策略[Tsay，2005，{S}8]的操作方法是首先创建一个总价值均值回归的资产组合，当其价值偏离长期均值时，通过卖空或购买该组合来利用这一知识。这类投资组合通常使用协整理论中的工具进行选择[Engle and Granger，1987年，Johansen，1991年]，其目的是检测平稳的资产组合，从而进行均值回复。在这项工作中，我们认为仅仅关注平稳性可能不足以确保基于协整的策略的盈利能力。虽然可以使用大量金融资产以同步方式创建一个总价值固定的投资组合，因此均值回归，但交易如此庞大的投资组合在实践中会产生重要的交易或借贷成本。寻找由许多资产组成的固定投资组合也可能会导致波动性非常小的投资组合，需要大量杠杆才能盈利。我们在这项工作中研究了算法方法，可以通过搜索足够稀疏和/或不稳定的最大均值回复端口来减轻这些影响。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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