In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May 2019, we tested the next trading rule: to invest in the simulated commodity if the investor expects to be in the low-volatility regime at t + 1 or to otherwise hold the risk-free asset. Assumptions for our simulations included the following: (1) we assumed that the investors trade in a homogeneous (Gaussian or t-Student) two regime context and (2) the investor used a time-fixed, ARCH, or GARCH variance in each regime. Our results suggest that the use of the MS Gaussian model, with time-fixed variance, leads to the best performance in the oil market. For the case of natural gas, we found no benefit of using our trading rule against a buy-and-hold strategy in the three-month U.S. Treasury bills.
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.
In the present paper, we review the use of two-state, Generalized Auto Regressive Conditionally Heteroskedastic Markovian stochastic processes (MS-GARCH). These show the quantitative model of an active stock trading algorithm in the three main Latin-American stock markets (Brazil, Chile, and Mexico). By backtesting the performance of a U.S. dollar based investor, we found that the use of the Gaussian MS-GARCH leads, in the Brazilian market, to a better performance against a buy and hold strategy (BH). In addition, we found that the use of t-Student MS-ARCH models is preferable in the Chilean market. Lastly, in the Mexican case, we found that is better to use Gaussian time-fixed variance MS models. Their use leads to the best overall performance than the BH portfolio. Our results are of use for practitioners by the fact that MS-GARCH models could be part of quantitative and computer algorithms for active trading in these three stock markets.
In the present work, we test the mean-variance efficiency that Mexican public pension funds would have shown had these invested their local equity portfolio component only in socially responsible stocks. With a daily simulation (from 1 January 2005 to 31 July 2018) of the Standard & Poors (S&P) Mexico target risk indices, we found that there was no significant difference between the more conservative pension funds that invested only in the Price Index and Quotations (IPC) sustainable index against the ones that invested in the conventional IPC. In the case of the more aggressive type of pension funds (those with a higher Mexican equity investment level), a lower mean-variance efficiency would have been observed had these invested in the IPC sustainable index. We also found, with a two-regime Markov-switching analysis, that socially responsible investment would have been better for most of these pension funds during distress time periods. Even if our results do not give strong short-term proof for the use of a socially responsible investment strategy in the most aggressive pension funds, we found that the benefits will be observed in the long-term, due to a better performance during distress time periods and the lag effect of mid and small-cap stocks in the performance.
Neste artigo, testamos, com um modelo Gaussiano Markov de comutação de dois regimes, o desempenho semanal do índice MSCI EMU avaliado em euro e os índices MSCI EMU que excluem cada país membro. Fizemos isso para testar a integração de longo prazo do mercado e identificar possíveis oportunidades de curto prazo para atividades ativas de gestão de portfólio. Os nossos resultados sugerem que, mesmo que os mercados de ações da EMU sejam integrados no longo prazo, quando filtramos os dados em dois regimes, um para períodos normais e outro para períodos de crise, encontramos fortes evidências em favor de atividades ativas de gestão de portfólio, potencialmente aumentando o desempenho da carteira se excluirmos a Finlândia, Itália, Espanha e Portugal em períodos de tempo normais e Itália, Espanha, França e Finlândia em períodos de crise. Este último resultado é interessante, porque a França é a segunda maior economia da UEM e, tal como a Finlândia, não é considerada um país "periférico".
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