We study financial market contagion by applying the volatility index and the correlation between global markets.
The first discovery is that it is difficult for any foreign economic crisis to spread to the US equity market. US market may be slightly affected and drops 1%-5%. However, when this event happens, it is usually a good buyback opportunity.
Another discovery is that when there is an economic crisis in the US, the fear must spread to all global markets. In US financial crises, it often said, “all correlations go to 1.” Francois Longin and Bruno Solnik (2001) used “extreme value theory” to derive the distribution of extreme correlation between US, European, and Asian stock markets. They found that the S&P 500 seemed to lead the other two markets in terms of extreme positive or negative returns. Therefore, investing in other equity markets during the US financial crisis doesn’t reduce losses.
Data:
1997 Asian financial crisis
1998 Russian financial crisis
1999 Argentina economic crisis
2000 US Tech Bubble
2008 US financial crisis
2009 Spanish financial crisis
2010 European debt crisis
2010 Greece crisis
2011 Japanese earthquake
2014 Russian crisis
2015 Chinese market crisis
2018 Turkish crisis
2019 Argentina crisis
Introduction With the recent advent of large financial datasets, machine learning, and high-performance computing, analysts can backtest millions of alternative investment strategies. Backtest optimizers search for combinations of parameters to maximize a strategy's simulated historical performance, leading to backtest overfitting. The performance inflation problem goes beyond backtesting. More generally, researchers and investment managers tend to report only positive results, a phenomenon is known as selection bias. Failure to control the number of tests involved in a given finding can lead to overly optimistic performance expectations. The Deflated Sharpe Ratio (DSR) corrects for two major sources of performance inflation: selection bias under multiple tests and non-normally distributed returns . By doing so, DSR helps separate legitimate empirical results from statistical deception. Backtesting is a good example. Backtesting is a historical simulation of how a particular inv
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