In the present paper, we test the use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance the performance of a U.S. dollar-based portfolio that invest in the S&P 500 (SP500) stock index, the 3-month U.S. Treasury-bill (T-BILL) or the 1-month volatility index (VIX) futures. For the investment algorithm, we propose the use of two and three-regime, Gaussian and t-Student, MS and MS-GARCH models. This is done to forecast the probability of high volatility episodes in the SP500 and to determine the investment level in each asset. To test the algorithm, we simulated 8 portfolios that invested in these three assets, in a weekly basis from 23 December 2005 to 14 August 2020. Our results suggest that the use of MS and MS-GARCH models and VIX futures leads the simulated portfolio to outperform a buy and hold strategy in the SP500. Also, we found that this result holds only in high and extreme volatility periods. As a recommendation for practitioners, we found that our investment algorithm must be used only by institutional investors, given the impact of stock trading fees.

中文翻译：

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使用马尔可夫转换GARCH模型提高投资组合绩效和VIX期货交易时机

在本文中，我们测试了具有固定时间或广义自回归条件异方差（GARCH）方差的Markov切换（MS）模型的使用。这是为了增强投资于S＆P 500（SP500）股票指数，3个月美国国库券（T-BILL）或1个月波动率指数（VIX）期货的基于美元的投资组合的绩效。对于投资算法，我们建议使用两种和三种制度，即高斯和t型学生，MS和MS-GARCH模型。这样做是为了预测SP500中高波动性事件的可能性，并确定每种资产的投资水平。为了测试该算法，我们从2005年12月23日到2020年8月14日每周模拟8个投资于这三种资产的投资组合。我们的结果表明，使用MS和MS-GARCH模型以及VIX期货会使模拟投资组合的表现优于SP500中的购买和持有策略。此外，我们发现该结果仅在高波动性和极端波动性期间有效。作为对从业人员的建议，我们发现，鉴于股票交易费的影响，我们的投资算法只能由机构投资者使用。