Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets

Abstract

Abstract:In this study, the performance of the Multifractal Model of Asset Returns (MMAR) was examined for stock index returns of four emerging markets. The MMAR, which takes into account stylized facts of financial time series, such as long memory, fat tails and trading time, was developed as an alternative to the ARCH family models. Empirical analysis of the study consists of two sections. In the first section, we estimated the parameters of GARCH, EGARCH, FIGARCH, MRS-GARCH and MMAR for the stock index returns of Croatia, Greece, Poland and Turkey. In the second section, 1000 paths were obtained for each model using Monte Carlo simulations. We then compared the scaling function values of simulated and original time series for different q orders (1-5). According to the obtained results, the MMAR is mostly superior to other models and presents the best replica of the original time series. Another important finding is the achievement of the MRS-GARCH. We found that for lower levels of persistency (long memory) of return series, the performance of the MRS-GARCH excels, and for H = 0.5, it narrowly outperforms the MMAR.

Multifractal analysis of financial markets: a review

“…Günay investigated the comparative performance of the lognormal MMAR model with respect to four benchmark econometric models (GARCH, EGARCH, FIGARCH and MRS-GARCH) using daily stock index returns of four emerging markets (Croatia, Greece, Poland and Turkey) from 4 January 2000 to 3 July 2014 [497]. He estimated the parameters of the five models and simulated each model with 1000 runs.…”

The Impact of COVID-19 on BRICS and MSCI Emerging Markets Efficiency: Evidence from MF-DFA

“…This study further ranks the indices according to their level of efficiency in both pre-and post-COVID-19 periods, specifically elaborating on the consequences of the COVID-19 outbreak for the ranking of market efficiency. From a methodological perspective, our study extends the work of Günay [25] by specifically examining the impact of COVID-19 on market efficiency.…”

Multifractal analysis of financial markets

“…Günay investigated the comparative performance of the lognormal MMAR model with respect to four benchmark econometric models (GARCH, EGARCH, FIGARCH and MRS-GARCH) using daily stock index returns of four emerging markets (Croatia, Greece, Poland and Turkey) from 4 January 2000 to 3 July 2014 [459]. He estimated the parameters of the five models and simulated each model with 1000 runs.…”

“…Some researchers report that the width of the estimated multifractal spectrum is correlated to the price fluctuation in the future and thus can be used to predict price fluctuations [816,820,824]. Analogous to ∆α, some researchers also used another quantity [816,817,820,824,899] ∆ f = f (α max ) − f (α min ), (459) which is reported to be able to uncover some information about price fluctuations. Alternatively, Dewandaru et al used the inefficiency index D, together with momentum factors to construct factor models for the forecasting of future returns [1043].…”