Stock market bubbles and the forecastability of gold returns and volatility

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
David Gabauer, Rangan Gupta, Sayar Karmakar, Joshua Nielsen
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引用次数: 0

Abstract

In this article, multi‐scale LPPLS confidence indicator approach is used to detect both positive and negative bubbles at short‐, medium‐, and long‐term horizons for the stock markets of the G7 and the BRICS countries. This enables detecting major crashes and rallies in the 12 stock markets over the period of the 1st week of January, 1973 to the 2nd week of September, 2020. Similar timing of strong (positive and negative) LPPLS indicator values across both G7 and BRICS countries was also observed, suggesting interconnectedness of the extreme movements in these stock markets. Next, these indicators were utilized to forecast gold returns and its volatility, using a method involving block means of residuals obtained from the popular LASSO routine, given that the number of covariates ranged between 42 and 72, and gold returns demonstrated a heavy upper tail. The finding was, these bubbles indicators, particularly when both positive and negative bubbles are considered simultaneously, can accurately forecast gold returns at short‐ to medium‐term, and also time‐varying estimates of gold returns volatility to a lesser extent. The results of this paper have important implications for the portfolio decisions of investors who seek a safe haven during boom‐bust cycles of major global stock markets.
股市泡沫与黄金回报率和波动率的可预测性
本文采用多尺度 LPPLS 置信度指标方法,对七国集团和金砖国家的股票市场进行短期、中期和长期的正负泡沫检测。这样就能检测出 1973 年 1 月第一周至 2020 年 9 月第二周期间 12 个股票市场的重大暴跌和暴涨。此外,还观察到七国集团和金砖国家的 LPPLS 指标值出现强势(正值和负值)的相似时间,这表明这些股票市场的极端波动是相互关联的。鉴于协变量的数量在 42 到 72 之间,且黄金回报率表现出较强的上尾,因此我们使用这些指标来预测黄金回报率及其波动性,该方法涉及从流行的 LASSO 例程中获得的残差的块均值。研究结果表明,这些泡沫指标,尤其是同时考虑正负泡沫时,可以准确预测中短期黄金回报率,并在较小程度上准确预测黄金回报率波动的时变估计值。本文的结果对于在全球主要股市繁荣-萧条周期中寻求避风港的投资者的投资组合决策具有重要意义。
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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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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