scholarly journals Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

2019 ◽  
Vol 7 (2) ◽  
pp. 27
Author(s):  
Fuzuli Aliyev
Keyword(s):  
Random Walk ◽  
Market Efficiency ◽  
Financial Time Series ◽  
Weak Form ◽  
Ar Model ◽  
Smooth Transition ◽  
Star Model ◽  
Forecast Performance ◽  
Star Models ◽  
Out Of Sample

Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) type nonlinear model. I develop nonlinear ARCH and STAR models, a linear AR model and random walk model for 10 years’ weekly data and then out-of-sample forecast next 12 weeks’ return. Comparing forecast performance powers, I find that the STAR model outperforms random walk, that is Borsa Istanbul returns are predictable at the given period. The results show that the shareholders may earn abnormal return and identify the direction of the return change for the next week with at least 66% accuracy. Contrary to the linear level studies, these findings show that the Borsa Istanbul is not weak form efficient at nonlinear level within the studied period.

Forecasting Inflation Rate by Star Model: An Indian Experience

2002 ◽  
Vol 53 (3-4) ◽  
pp. 265-288
Author(s):  
G.P. Samanta
Keyword(s):  
Inflation Rate ◽  
Wholesale Price ◽  
Smooth Transition ◽  
Forecast Errors ◽  
Star Model ◽  
Star Models ◽  
Non Linear ◽  
Out Of Sample ◽  
Short Run ◽  
Forecasting Inflation

In this empirical study, an attempt has been made to model non-linear dynamics of inflation rate in India through Smooth Transition ⁄ Threshold Auto-Regression (STAR). Inflation is measured based on weekly data on Wholesale Price Index (WPI) fur a period of seven years from the week ended April 2, 1994 to the week ended March 31, 2001. The log(WPI) series is detected to be a Difference-Stationary process, indicating that the series is non-stationary but its first-order difference is stationary. The generating process of the transformed-stationary series is identified to be non-linear. Six variants of STAR model are estimated for transformed-stationary series and are used to forecast WPI and annual inflation rate. Empirical assessment of out-of-sample forecast errors eveals that estimated STAR models perform reasonably well in generating short-run forecasts of both the variables.

Do the Stock Market Indices Follow a Random Walk?

2021 ◽  
pp. 389-410
Author(s):  
Cristina Vasco ◽  
Pedro Pardal ◽  
Rui Teixeira Dias
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
South Korea ◽  
Market Efficiency ◽  
Stock Markets ◽  
Weak Form ◽  
Efficient Market ◽  
Efficiency Hypothesis

This chapter aims to test the hypothesis of an efficient market, in its weak form, in the stock markets of Brazil, China, South Korea, USA, Spain, Italy, in the period from December 2, 2020 to May 12, 2020. The results show that the market efficiency hypothesis is rejected in all markets. In corroboration the DFA exponents show long memories, which put in question the market efficiency, in its weak form, suggesting that the stock markets analyzed show some predictability. In conclusion, investors should avoid investing in stock markets, at least while this pandemic lasts, and invest in less risky markets in order to mitigate risk and improve the efficiency of their portfolios.

scholarly journals The Evolving Efficiency Of The South African Stock Exchange

2012 ◽  
Vol 11 (9) ◽  
pp. 997 ◽  
Author(s):  
Lumengo Bonga-Bonga
Keyword(s):  
Stock Returns ◽  
South African ◽  
Stock Exchange ◽  
Weak Form ◽  
Time Varying ◽  
The South ◽  
Forecast Performance ◽  
Nested Models ◽  
Out Of Sample ◽  
Weak Form Efficiency

This paper tests the weak-form efficiency in the South African stock exchange - the Johannesburg Securities Exchange (JSE) - under the hypothesis that emerging markets efficiency evolves through time as these markets constantly enhance their regulatory environment. The paper makes use of the time varying GARCH model in testing this hypothesis. In addition, the paper compares the out-of-sample forecast performance of the time varying and fixed parameter GARCH models in predicting stock returns in the JSE making use of MSE-F statistics for nested models proposed (McCracken, 1999). The findings of the paper show that the two models provide the same conclusion in showing that the JSE has been efficient during the period of the analysis. In addition, the time varying model outperforms the fixed coefficient model in predicting the JSE stock returns. This finding indicates that the time-varying parameter model adds a benefit in testing the weak-form efficiency or modelling stock return in the JSE.

scholarly journals Genetic Algorithm Based Improved ESTAR Nonlinear Models for Modelling Sunspot Numbers and Global Temperatures

2021 ◽  
Author(s):  
Bishal Gurung ◽  
Achal Lama ◽  
Santosha Rathod ◽  
K N Singh
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Models ◽  
Forecast Error ◽  
Optimization Procedure ◽  
Smooth Transition ◽  
Star Model ◽  
Estimation Of Parameters ◽  
Sunspot Numbers ◽  
Star Models ◽  
One Step

Abstract Smooth Transition Autoregressive (STAR) models are employed to describe cyclical data. As estimation of parameters of STAR using nonlinear methods was time-consuming, Genetic algorithm (GA), a powerful optimization procedure was applied for the same. Further, optimal one step and two step ahead forecasts along with their forecast error variances are derived theoretically for fitted STAR model using conditional expectations. Given the importance of the issue of global warming, the current paper aims to model the sunspot numbers and global mean temperatures. Further, appropriate tests are carried out to see if the model employed is appropriate for the datasets.

scholarly journals PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR)

2018 ◽  
Vol 7 (1) ◽  
pp. 84-95
Author(s):  
Gayuh Kresnawati ◽  
Budi Warsito ◽  
Abdul Hoyyi
Keyword(s):  
Time Series ◽  
Transition Function ◽  
Mean Absolute Percentage Error ◽  
Ar Model ◽  
Smooth Transition ◽  
Percentage Error ◽  
Star Model ◽  
Significance Level ◽  
Absolute Percentage Error ◽  
Smooth Transition Autoregressive

Smooth Transition Autoregressive (STAR) Model is one of time series model used in case of data that has nonlinear tendency. STAR is an expansion of Autoregressive (AR) Model and can be used if the nonlinear test is accepted. If the transition function G(st,γ,c) is logistic, the method used is Logistic Smooth Transition Autoregressive (LSTAR). Weekly IHSG data in period of 3 January 2010 until 24 December 2017 has nonlinier tend and logistic transition function so it can be modeled with LSTAR . The result of this research with significance level of 5% is the LSTAR(1,1) model. The forecast of IHSG data for the next 15 period has Mean Absolute Percentage Error (MAPE) 2,932612%. Keywords : autoregressive, LSTAR, nonlinier, time series

scholarly journals Weak Form Efficiency In Indian Stock Markets

2011 ◽  
Vol 6 (3) ◽  
Author(s):  
Rakesh Gupta ◽  
Parikshit K. Basu
Keyword(s):  
Random Walk ◽  
Emerging Markets ◽  
Market Efficiency ◽  
Equity Markets ◽  
Weak Form ◽  
Random Walk Model ◽  
Important Concept ◽  
Random Walk Hypothesis ◽  
International Boundaries ◽  
Weak Form Efficiency

Hypothesis of Market Efficiency is an important concept for the investors who wish to hold internationally diversified portfolios. With increased movement of investments across international boundaries owing to the integration of world economies, the understanding of efficiency of the emerging markets is also gaining greater importance. In this paper we test the weak form efficiency in the framework of random walk hypothesis for the two major equity markets in India for the period 1991 to 2006. The evidence suggests that the series do not follow random walk model and there is an evidence of autocorrelation in both markets rejecting the weak form efficiency hypothesis.

scholarly journals Testing Weak Form of Market Efficiency in Nepal

2016 ◽  
Vol 1 ◽  
Author(s):  
Jeetendra Dangol
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Market Efficiency ◽  
Stock Returns ◽  
Weak Form ◽  
Corrected Data ◽  
Infrequent Trading ◽  
Normally Distributed

The paper investigates the weak form of market efficiency for overall and sectorial indices. The Nepalese stock returns are found not being normally distributed during the study period. The autocorrelation of the stock returns was reduced by correcting the data with the application of the methodology suggested by Miller et al. (1994). The Nepalese stock market has suffered from the problem of thin-trading. Overall, the Nepalese market is not weak-form efficient on the basis of the analysis performed by employing observed returns series; but it is found a weak-form efficient in case of the analysis while using corrected data after adjusting infrequent trading. Hence, the study is supported to the random-walk and weak form of market efficiency.

scholarly journals Testing Weak Form of Market Efficiency of DSE Based on Random Walk Hypothesis Model: A Parametric Test Approach

2019 ◽  
Vol 9 (1) ◽  
pp. 400
Author(s):  
Ahmed Raihan Sadat ◽  
Md. Emran Hasan
Keyword(s):  
Random Walk ◽  
Market Efficiency ◽  
Stock Exchange ◽  
Weak Form ◽  
Data Series ◽  
Ratio Test ◽  
Random Walk Hypothesis ◽  
Hypothesis Model ◽  
Augmented Dickey Fuller ◽  
Tests For Normality

Stock market is one great indicator of any country’s economic condition. Hence, measuring the capital market in different forms has always been a great interest to finance researchers. This paper measures the market efficiency and randomness of Dhaka stock Exchange (DSE) in weak form employing daily observations (return) from two comparatively new ventured indices viz. DS30 and DSEX. Initially, the study tests for normality using Jarque-Bera test of normality and found data series are not normally distributed. Later, some widely used parametric tests were conducted to examine the historic price dependencies or to examine the random walk hypothesis (RWH) of DSE indices. Augmented Dickey-Fuller test (ADF), Autocorrelation function (ACF), and variance ratio test (Lo & MacKinlay) were used and all of the results suggested DSE to be not efficient in weak form. Meaning, prices of DSE do not follow a random walk.

Modeling Multiple Regimes in the Business Cycle

1999 ◽  
Vol 3 (3) ◽  
pp. 311-340 ◽  
Author(s):  
Dick van Dijk ◽  
Philip Hans Franses
Keyword(s):  
Business Cycle ◽  
Growth Phase ◽  
High Growth ◽  
Smooth Transition ◽  
Star Model ◽  
Two Phase ◽  
Star Models ◽  
Moderate Growth ◽  
Multiple Regimes ◽  
The Business Cycle

The interest in business-cycle asymmetry has been steadily increasing over the past 15 years. Most research has focused on the different behavior of macroeconomic variables during expansions and contractions, which by now is well documented. Recent evidence suggests that such a two-phase characterization of the business cycle might be too restrictive. In particular, it might be worthwhile to decompose the recovery phase in a high-growth phase (immediately following the trough of a cycle) and a subsequent moderate-growth phase. The issue of multiple regimes in the business cycle is addressed using smooth-transition autoregressive (STAR) models. A possible limitation of STAR models as they currently are used is that essentially they deal with only two regimes. We propose a generalization of the STAR model such that more than two regimes can be accommodated. It is demonstrated that the class of multiple-regime STAR (MRSTAR) models can be obtained from the two-regime model in a simple way. The main properties of the MRSTAR model and several issues that are relevant for empirical specification are discussed in detail. In particular, a Lagrange multiplier-type test is derived that can be used to determine the appropriate number of regimes. A limited simulation study indicates its practical usefulness. Application of the new model class to U.S. real GNP provides evidence in favor of the existence of multiple business-cycle phases.

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