Economic gains of realized volatility in the Brazilian stock market
| Autor | Márcio Gomes Pinto Garcia - Marcelo Cunha Medeiros - Francisco Eduardo de Luna e Almeida Santos |
| Cargo | PUC/Rio, Rio de Janeiro, RJ, Brasil - PUC/Rio, Rio de Janeiro, RJ, Brasil - IPEA, Rio de Janeiro, RJ, Brasil |
Economic Gains of Realized Volatility in the
Brazilian Stock Market
(Ganhos Econˆ
omicos da Volatilidade Realizada no Mercado Brasileiro
de Ac¸ ˜
oes)
M´
arcio Gomes Pinto Garcia*
Marcelo Cunha Medeiros**
Francisco Eduardo de Luna e Almeida Santos***
Abstract
This paper evaluates the economic gains associated with following a volatility tim-
ing strategy based on a multivariate model of realized volatility. To study this is-
sue, we build a high frequency database with the most actively traded Brazilian
stocks. Comparing with traditional volatility methods, we find that, when estima-
tion risk is controlled, economic gains associated with realized measures perform
well and increase proportionally to the target return. When expected returns are
bootstrapped, however, performance fees are not significant, which is an indication
that economic gains of realized volatility are offset by estimation risk.
Keywords:realized volatility; utility; forecasting.
JEL code: G11; G17.
Resumo
O artigo avalia os ganhos econˆomicos associados `a execuc¸˜ao de uma estrat´egia
baseada em volatilidade a partir de um modelo multivariado de volatilidade real-
izada. Para tanto, constru´ımos uma base de dados em alta frequˆencia que cont´em
as ac¸ ˜oes mais negociadas no mercado de ac¸ ˜oes Brasileiro. A comparac¸˜ao com
modelos tradicionais de volatilidade mostra que, quando o risco de estimac¸ ˜ao ´e
controlado, h´aganhos eco nˆomicos positivos associados `asmed idas de volatilidade
realizada e tais ganhos crescem proporcionalmente aos retor nos-alvo. No entanto,
quando os retornos esperados s ˜ao randomizados, as taxas de desempenho n ˜ao s˜ao
significativas, sugerindo que os ganhos econˆomicos da volatilidade realizada s˜ao
compensados pelo risco de estimac¸˜ao.
Submitted 29 May 2014. Reformulated 15 August 2014. Accepted 18 September
2014. Published on-line 26 May 2015. The article was double blind refereed and evaluated
by the editor. Supervising editor: Ricardo P. C. Leal.
*PUC/Rio, Rio de Janeiro, RJ, Brasil. E-mail: mgarcia@econ.puc-rio.br
**PUC/Rio, Rio de Janeiro, RJ, Brasil. E-mail: mcm@econ.puc-rio.br
***IPEA, Rio de Janeiro, RJ, Brasil. E-mail: francisco.luna@superig.com.br
Rev. Bras. Financ¸as(Online), Rio de Janeiro, Vol. 12, No. 3, September 2014, pp. 319–349
ISSN 1679-0731, ISSN online 1984-5146
c
2014 Sociedade Brasileira de Financ¸as, under a Creative Commons Attribution 3.0 license -
http://creativecommons.org/licenses/by/3.0
Garcia, M., Medeiros, M.,Santos, F.
Palavras-chave:volatilidade realizada; utilidade; previs˜ao.
1. Introduction
Given the growth of financial markets and the increasing complexity
of its securities, volatility models play an essential role to help the task of
risk management and investment decisions. Since realization of volatility
returns based on daily data is not observable, the traditional approach is to
invoke parametric assumptions regarding the evolution of the first and sec-
ond moments of the returns, which is the idea behind ARCH and stochastic
volatility models. Nevertheless, these models fail to capture some stylized
facts such as autocorrelation persistence and fat tail of returns. The avail-
ability of intraday data opens up the possibility of approximating volatility
directly from asset returns. The use of an observable variable, in turn, facil-
itates the task of dealing with problems that involves a significant number
of assets. Indeed, traditional methods suffer from the curse of dimension-
ality, which is to say, the difficulty of these methods to handle with a wider
range of assets.
The advantages of realized measures have been extensively analyzed
in the recent period, when technological barriers have been gradually sur-
mounted so as to provide the kind of data necessary to its calculation. An-
dersen et al. (2003) compared it to traditional methods and confirmed its
superiority in terms of forecasting performance. Identical conclusion has
been reached by many concurrent studies, like Engle et al. (2008). Previ-
ous findings relating microstructure parameters and volatility were revisited
considering realized measures. Chan & Fong (2006), for instance, found
that trading volume is the main factor driving the relationship between vol-
ume and realized volatility, as opposed tostudies that pointed out orderim-
balance as the most important one. In Brazil, Carvalho et al. (2006) found
that returns displayed a normal distributional when standardized by realized
measures, a useful property concerning risk management purposes, namely
Value-at-Risk statistics. The authors based their conclusions on a sample of
the five most liquid stocks traded at the domestic stock exchange, sampling
at a 15-min frequency. In spite of the apparent consensus over the subject,
there are many relevant issues that deserve attention, in particular the bias
originated by microstructure noise and measurement errors. McAleer &
Medeiros (2008) documented a review of the literature, stressing the future
improvements that must be made in order to deal with such biases.
The objective of this paper isto evaluate the economic gains associated
320 Rev. Bras. Financ¸ as (Online), Rio de Janeiro, Vol. 12, No. 3, September 2014
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