Purpose -The purpose of this paper is to use the local correlation technique to measure flight to quality, which is defined as a pronounced and generally rapid increase in risk aversion. Flight to quality between American, British, German, Japanese, and Hong Kong spot equity indices and index futures is examined. Design/methodology/approach -The technique of non-linear local correlation is employed to detect flight to quality in both spot and futures markets. The use of this methodology allows us to properly process both normally or non-normally distributed time series. In addition, the estimation of local correlation minimizes the theoretical restrictions resulting from the selection of conditional events and the use of linear regression. Findings -As market risk grows, an increase in flight to quality is documented. For example, a crash in the US stock market results in the flight of capital to the Treasury bond market. Evidence of flight to quality from domestic and foreign spot equity markets to US Treasury bonds is provided. Furthermore, flights to quality from domestic and foreign index futures to US bond futures are revealed. The strength of the reaction from one market to the other is measured and reported. Surprisingly, the authors observe that when market risk becomes extremely high, flight to quality diminishes. Originality/value -To the best of the authors' knowledge, this is the first study that examines flight to quality in the futures markets by applying local correlation analysis. This study broadens the application of local polynomial regression and local correlation analysis.
In the past, financial analysis mostly relied on subjective judgment, but with the improvement of the tools brought about by the development of The Times, the use of quantitative tools has become more and more important in financial investment. As an easy to use and expanding computer language, Python is also widely used in quantitative finance. China's convertible bond market has entered a phase of rapid development since 2017. In this paper, in addition to the traditional research on convertible bond indicators, an innovative research factor of absolute parity premium is cited as a research factor of convertible bonds use Python to back-test and analyze the convertible bond market in the past three years. The results indicate that the absolute parity premium indicator has predictive power for the future return of convertible bonds, and investing in convertible bonds with lower absolute parity premiums can bring significant excess returns. The significance of this paper is to discover and prove the quantitative investment strategy of absolute parity premium bond selection, which provides a new way of thinking for convertible bond investment research.
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