Gestão de Carteiras sob Múltiplos Regimes: Performance Fora da Amostra no Mercado Brasileiro

ID: 59437

Authors: Marcelo Lewin, Carlos Heitor Campani.

Source: Revista Brasileira de Finanças, v. 18, n. 3, p. 0-0, July-September, 2020. 1 page(s).

Keyword: gestão de carteiras , múltiplos regimes; alocação dinâmica; performance

Document type: Article (Portuguese)

Day-Trading Stocks for a Living?

ID: 59434

Authors: Fernando Chague, Bruno Giovannetti.

Source: Revista Brasileira de Finanças, v. 18, n. 3, p. 1-4, July-September, 2020. 4 page(s).

Keyword: Day trade , Day trading for a living , Retail investors

Document type: Article (Portuguese) Show Abstract

The number of individuals trying to day-trade for a living has been in creasing world wide. In this note, westudy the performance of all 98,378 individuals who started to day-trade stocks in Brazil between 2013 and 2016. After analyzing all their daytrades in stocks from 2013 to 2018, we find that only 127 individuals presented an average daily gross profit higher than R$ 100 for more than 300 days.

Hedging the Brazilian Stock Index in the Era of Low Interest Rates: What Has Changed?

ID: 59435

Authors: Fernando Antonio Lucena Aiube, Winicius Botelho Faquieri.

Source: Revista Brasileira de Finanças, v. 18, n. 3, p. 5-26, July-September, 2020. 22 page(s).

Keyword: Brazilian stock market , Hedging equities , MGARCH models

Document type: Article (English) Show Abstract

In this paper we analyze the ability of different asset classes to hedge the Brazilian stock index in periods of high and low interest rates in the Brazilian economy, using two multivariate GARCH models. Our analysis includes two categories of assets: those traded in domestic currency and those traded in U.S. dollars. From the perspective of a local investor, we find that the exchange rate (R$/US$) and gold are the assets least correlated with equities. From the standpoint of a foreign investor, commodity index and fixed-income assets are the most useful. These results prevail in the low- and high-interest-rate periods. Moreover, in the period of low interest rates, the standard deviation of the estimated conditional correlation time series decreases, suggesting that in this period investors are more confident about macroeconomic policies.

Relationships between Price-Earnings Ratios and Returns of Treasury Bonds

ID: 59436

Authors: Daniel Penido de Lima Amorim, Marcos Antônio de Camargos.

Source: Revista Brasileira de Finanças, v. 18, n. 3, p. 27-51, July-September, 2020. 25 page(s).

Keyword: Behavioral finance , Fed Model , Price-earnings ratio , Treasury bonds

Document type: Article (Portuguese) Show Abstract

The ratios P/E1 and P/E10 or the cyclically adjusted price-to-earnings ratio are widely disseminated in the literature based on the U.S. stock market. This paper introduces a method to construct P/E ratios for the Brazilian stock market. The purpose of this paper is to analyze the long-term relationships between both P/E1 and P/E10 and interest rates corresponding to the returns of treasury bonds, in order to test the Fed Model. In general, the period considered was from December 2004 to June 2018. Autoregressive distributed lags models were estimated, which can be represented as conditional error correction models. Results show significant long-term relationships between both P/E1 and P/E10 and the relevant interest rates, suggesting that the Fed Model is in line with the behavior of the Brazilian financial market.

Forecasting Value-At-Risk for the Cryptocurrency Market Using Markov-Switching EGARCH Models

ID: 59438

Authors: Paulo Fernando Marschner, Paulo Sérgio Ceretta.

Source: Revista Brasileira de Finanças, v. 18, n. 3, p. 80-107, July-September, 2020. 28 page(s).

Keyword: Cryptocurrencies , Regime change , Value-at-Risk , Volatility

Document type: Article (Portuguese) Show Abstract

This study aims to understand the volatile behavior of six highly representative cryptocurrencies. To do so, EGARCH and Markov-switching EGARCH models were estimated, combined with different distributions of statistical probability. The predictive capacity of the best models resulting from these combinations were tested by predicting the value-at-risk. The daily returns of the cryptocurrencies clearly show regime changes in their volatility dynamics. In the in-sample analysis, the regime change model confirms the existence of two states: the first characterized by a greater ARCH effect and less affected by asymmetries, while the second reveals a greater effect of the arrival of information, that is, it is more sensitive to asymmetric shocks. In the out-of-sample analysis, the value-at-risk predictions of the regime change model clearly exceed the single-regime model by the extreme quantile of 1%.