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Minimização de risco e investimento: período Covid e Pós-Covid
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O presente trabalho investiga a constituição e o desempenho das carteiras de variância
mínima nos períodos Covid e Pós-Covid, utilizando alternativamente as metodologias Mé-
dia Móvel e Modelo DCC. Adicionalmente, é feita uma análise out-of-sample, com previsão
de pesos na carteira, com modelos ARIMA, versus previsão dos elementos constitutivos
dos pesos, com modelos MGARCH, especificamente, VAR(1)-DCC-GARCH(1,1).
Os dados utilizados nesta investigação foram os 11 sectores constitutivos do S&P 500 e
o Ouro, permitindo uma análise de efeitos spillover entre sectores, quer nos retornos, quer
no risco. A inclusão do Ouro permite analisar o seu papel como ativo de refúgio e sua
eficácia, face a que ativos e como tal se verifica no período Covid e Pós-Covid.
Durante o período Covid, as carteiras adotaram uma postura defensiva, com maior
alocação em setores seguros como Bens de Consumo e Ouro, resultando em maior retorno
ajustado ao risco. No período Pós-Covid, houve uma diversificação para setores como
Finanças e Indústria, refletindo uma maior confiança económica, porém com menor
recompensa pelo risco.
As metodologias aplicadas demonstraram que, embora o Ouro tenha atuado como
um ativo de refúgio temporário, sua eficácia como hedge foi limitada e variável entre os
setores. Tanto no período Covid como no período Pós-Covid o papel principal do Ouro
foi de diversificador.
Em termos de contribuição para escolha de abordagem no cálculo de pesos numa
carteira, o estudo in-sample mostra que o método histórico para constituir carteiras de
variância mínima conduz a resultados melhores de performance face ao uso da abordagem
DCC.
Adicionalmente, o estudo mostrou que, out-of-sample a previsão com o modelo DCC,
proporcionou melhores resultados ao analisarmos o risco extremo, utilizando o VaR face
aos outros modelos aplicados.
Assim, o trabalho conclui que a escolha adequada de modelos e metodologias é crucial
para a gestão eficaz de portfólios, especialmente em períodos de elevada incerteza.
This paper investigates the constitution and performance of minimum variance portfolios in the Covid and Post-Covid periods, alternatively using the Moving Average and DCC Model methodologies. In addition, an out-of-sample analysis is carried out, forecasting the weights in the portfolio with ARIMA models versus forecasting the constituent elements of the weights with MGARCH models, specifically VAR(1)-DCC-GARCH(1,1). The data used in this research were the 11 constituent sectors of the S&P 500 and Gold, allowing an analysis of spillover effects between sectors, both in returns and risk. The inclusion of gold makes it possible to analyze its role as a safe-haven asset and its effectiveness in relation to which assets and how it performs in the Covid and post-Covid periods. During the Covid period, the portfolios adopted a defensive stance, with a greater allocation to safe sectors such as Consumer Goods and Gold, resulting in a higher risk- adjusted return. In the post-Covid period, there was diversification into sectors such as Financials and Manufacturing, reflecting greater economic confidence, but with lower risk reward. The methodologies applied showed that although Gold acted as a temporary refuge asset, its effectiveness as a hedge was limited and variable between sectors. In both the Covid and Post-Covid periods, Gold’s main role was as a diversifier. In terms of the contribution to the choice of approach in calculating portfolio weights, the in-sample study shows that the historical method for building minimum variance portfolios leads to better performance results than using the DCC approach. In addition, the study showed that, out-of-sample, forecasting with the DCC model provided better results when analysing extreme risk using VaR compared to the other models applied. The paper therefore concludes that the appropriate choice of models and methodolo- gies is crucial for effective portfolio management, especially in periods of high uncertainty.
This paper investigates the constitution and performance of minimum variance portfolios in the Covid and Post-Covid periods, alternatively using the Moving Average and DCC Model methodologies. In addition, an out-of-sample analysis is carried out, forecasting the weights in the portfolio with ARIMA models versus forecasting the constituent elements of the weights with MGARCH models, specifically VAR(1)-DCC-GARCH(1,1). The data used in this research were the 11 constituent sectors of the S&P 500 and Gold, allowing an analysis of spillover effects between sectors, both in returns and risk. The inclusion of gold makes it possible to analyze its role as a safe-haven asset and its effectiveness in relation to which assets and how it performs in the Covid and post-Covid periods. During the Covid period, the portfolios adopted a defensive stance, with a greater allocation to safe sectors such as Consumer Goods and Gold, resulting in a higher risk- adjusted return. In the post-Covid period, there was diversification into sectors such as Financials and Manufacturing, reflecting greater economic confidence, but with lower risk reward. The methodologies applied showed that although Gold acted as a temporary refuge asset, its effectiveness as a hedge was limited and variable between sectors. In both the Covid and Post-Covid periods, Gold’s main role was as a diversifier. In terms of the contribution to the choice of approach in calculating portfolio weights, the in-sample study shows that the historical method for building minimum variance portfolios leads to better performance results than using the DCC approach. In addition, the study showed that, out-of-sample, forecasting with the DCC model provided better results when analysing extreme risk using VaR compared to the other models applied. The paper therefore concludes that the appropriate choice of models and methodolo- gies is crucial for effective portfolio management, especially in periods of high uncertainty.
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Covid-19 Ouro sectores do S&P 500 Carteira de Variância Mínima hedge Média Móvel