Learning from Liquidation Prices

ERN: Search Pub Date : 2020-11-25 DOI:10.2139/ssrn.3737823
Gianluca Rinaldi
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Abstract

I develop a model of investor learning driven by mistaken inference from market prices. Investors have heterogeneous beliefs about the worst case return of a risky asset and take leverage to buy it. When the worst case becomes more likely, forced liquidations result in price crashes, which investors mistake for negative information about worst case returns. They therefore revise cash flow expectations downwards, henceforth requiring larger returns. The model predicts that crashes lead to persistent changes in future average returns and that larger crashes are followed by larger changes. To link the model to historical crashes, I consider two strategies associated with the Black Monday crash in 1987 and the Lehman Brothers bankruptcy in 2008. Hedged put options selling suffered severe losses around Black Monday, while arbitraging the difference in implied credit risk between the corporate bond and CDS markets was similarly negatively affected after the Lehman bankruptcy. The losses on these strategies in those crisis episodes were likely exacerbated by deleveraging, but the increased returns after the crashes have been remarkably persistent, consistent with the implications of my model.
从清算价格中学习
我开发了一个由市场价格错误推断驱动的投资者学习模型。投资者对风险资产最坏情况下的回报有不同的看法,并利用杠杆来购买。当最坏情况发生的可能性增大时,被迫平仓就会导致价格暴跌,投资者将其误认为是有关最坏情况回报的负面信息。因此,他们下调了现金流预期,因此需要更高的回报。该模型预测,崩盘会导致未来平均收益的持续变化,而更大的崩盘之后会出现更大的变化。为了将该模型与历史上的崩盘联系起来,我考虑了与1987年黑色星期一崩盘和2008年雷曼兄弟(Lehman Brothers)破产相关的两种策略。对冲看跌期权在黑色星期一前后遭受严重损失,而在雷曼兄弟破产后,公司债券和CDS市场之间隐含信用风险差异的套利交易同样受到负面影响。在那些危机时期,这些策略的损失很可能因去杠杆化而加剧,但崩盘后回报的增加却非常持久,与我的模型的含义一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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