A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance

In the present paper, we test the benefit of using Markov-Switching models and volatility futures diversification in a Euro-based stock portfolio. With weekly data of the Eurostoxx 50 (ESTOXX50) stock index, we forecasted the smoothed regime-specific probabilities at T + 1 and used them as the weighting method of a diversified portfolio in ESTOXX50 and ESTOSS50 volatility index (VSTOXX) futures. With the estimated smoothed probabilities from 9 July 2009 to 29 September 2020, we simulated the performance of three theoretical investors who paid different trading costs and invested in ESTOXX50 during calm periods (low volatility regime) or VSTOXX futures and the three-month German treasury bills in distressed or highly distressed periods (high and extreme volatility regimes). Our results suggest that diversification benefits hold in the short-term, but if a given investor manages a two-asset portfolio with ESTOXX50 and our simulated portfolios, the stock portfolio’s performance is enhanced significantly, in the long term, with the presence of trading costs. These results are of use to practitioners for algorithmic and active trading applications in ESTOXX50 ETFs and VSTOXX futures.

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Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading

Symmetry ◽

10.3390/sym13122346 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2346

Author(s):

Oscar V. De la Torre-Torres ◽

Dora Aguilasocho-Montoya ◽

José Álvarez-García

Keyword(s):

Markov Switching ◽

Stock Index ◽

Garch Models ◽

Algorithmic Trading ◽

Stock Trading ◽

Markov Switching Models ◽

Switching Models ◽

Asymmetric Garch ◽

The Impact ◽

T Distribution

In the present paper, we extend the current literature in algorithmic trading with Markov-switching models with generalized autoregressive conditional heteroskedastic (MS-GARCH) models. We performed this by using asymmetric log-likelihood functions (LLF) and variance models. From 2 January 2004 to 19 March 2021, we simulated 36 institutional investor’s portfolios. These used homogenous (either symmetric or asymmetric) Gaussian, Student’s t-distribution, or generalized error distribution (GED) and (symmetric or asymmetric) GARCH variance models. By including the impact of stock trading fees and taxes, we found that an institutional investor could outperform the S&P 500 stock index (SP500) if they used the suggested trading algorithm with symmetric homogeneous GED LLF and an asymmetric E-GARCH variance model. The trading algorithm had a simple rule, that is, to invest in the SP500 if the forecast probability of being in a calm or normal regime at t + 1 is higher than 50%. With this configuration in the MS-GARCH model, the simulated portfolios achieved a 324.43% accumulated return, of which the algorithm generated 168.48%. Our results contribute to the discussion on using MS-GARCH models in algorithmic trading with a combination of either symmetric or asymmetric pdfs and variance models.

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Markov-Switching Models with Endogenous Explanatory Variables II: A Two-Step MLE Procedure with Standard-Error Correction

SSRN Electronic Journal ◽

10.2139/ssrn.516683 ◽

2004 ◽

Cited By ~ 1

Author(s):

Chang-Jin Kim

Keyword(s):

Error Correction ◽

Standard Error ◽

Markov Switching ◽

Explanatory Variables ◽

Markov Switching Models ◽

Switching Models

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Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

SN Applied Sciences ◽

10.1007/s42452-021-04728-9 ◽

2021 ◽

Vol 3 (8) ◽

Author(s):

Majid Javari

Keyword(s):

Environmental Planning ◽

Rainfall Variability ◽

Markov Switching ◽

Daily Rainfall ◽

First Order ◽

Markov Switching Models ◽

Markov Transition Matrix ◽

Switching Models ◽

Markov Transition ◽

Modeling And Forecasting

AbstractThis paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.

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