Generalized Autoregressive Conditional Heteroscedasticity In Mean Research Articles

The Performance of Dividend ETFs: The Study of the Spillover and Leverage Effects

The objective of this study is to identify the existence of spillover and leverage effects from returns and return volatilities of high yield and low yield dividend ETFs on tracing market stock indices, and vice versa. The Generalized Autoregressive Conditional Heteroscedasticity-in-Mean-Autoregressive Moving Average (GARCH-M-ARMA) and the Exponentially Generalized Autoregressive Conditional Heteroscedasticity-in Mean Autoregressive Moving Average (EGARCH-M-ARMA) are utilized by authors. The six ETFs, recognized in Top 100 ETFs of etfdb.com database, with their underlying indices are selected to represent the high and low dividend yield ETFs group. The findings show that the spillover effect in return is more happening in a group of low yield dividend ETFs, while the spillover effect on return volatilities is more dominant in a group of high yield dividend ETFs. In the case of the leverage effect, it exists in all ETFs and the stock Index, in which the negative asymmetric volatility effect more happens when comparing the positive asymmetric volatility effect.

Superstitious behavior and stock returns: The case of Javanese traditional calendar

Some Indonesian people who are still attached to traditional rituals, often mark several days on their Javanese calendar as sacred days. This study aims to investigate the influence of the sacred days between daily cycles in Georgian calendar with pasaran cycle in Javanese calendar toward Jakarta Composite Index (JCI) returns in a period of January 2009–June 2016. The data was analyzed using Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M). This study found some sacred days i.e. Kliwon Friday and Wage Thursday resulted in a lower return than the normal day, while Wage Thursday had a significant effect on the return of the Jakarta Composite Index (JCI). These findings indicated that investment decisions made by investors were still influenced by superstition, which leads to behavior biases.

Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data

Bonds have become an important part of investment portfolios for individuals as well as for institutions, particularly after the recent financial crisis. This paper empirically investigates the Adaptive Market Hypothesis (AMH) in two of the most established bond markets in the world: the US and UK and two emerging markets: South Africa and India, using monthly data series spanning very long time periods. We examine the long memory properties of the series using several long memory estimations methods and multiple structural breaks techniques to examine the possibility of time varying market efficiency. We then examine the weak-form efficiency of government bond markets, using a time varying approaches namely the state-space generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) to date the time varying behavior of bond market efficiency. Results show that efficiency of these markets has been changing over time, depending on the prevailing economic, political and market conditions. Further, we observe that the degree of the weak-form efficiency of these markets has been gradually improving recently. In particular, the US government bond market has been highly efficient, showing the highest degree of market efficiency among the four bond markets. Overall, our results suggest that the AMH provides a better description of the behavior of government bond returns than the Efficient Market Hypothesis (EMH).

Time varying market efficiency of the GCC stock markets

This paper investigates the time-varying levels of weak-form market efficiency for the GCC stock markets over the period spanning from May 2005 to September 2013. We use two empirical approaches: (1) the generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) model with state space time varying parameter (Kalman filter), and (2) a rolling technique sample test of the fractional long memory parameter d. As long memory estimation methods, we use the detrended fluctuation analysis (DFA) technique, the modified R/S statistic, the exact local whittle (ELW) and the feasible Exact Local Whittle (FELW) methods. Moreover, we use the Bai and Perron (1998, 2003) multiple structural breaks technique to test and date the time varying behavior of stock market efficiency. Empirical results show that GCC markets have different degrees of time-varying efficiency, and also have experiencing periods of efficiency improvement. Results also show evidence of structural breaks in all GCC markets. Moreover, we observe that the recent financial shocks such as Arab spring and subprime crises have a significant impact on the time path evolution of market efficiency.

Sin stock returns and investor sentiment

Abstract This paper studies the impact of investor sentiment on a portfolio formed of sin stocks—publicly traded companies in the alcohol, tobacco, and gaming industries. Using a variety of sentiments-augmented asset pricing models, this research examines whether investor sentiment is a risk factor for sin stock returns. It also studies if the abnormal returns – found in the literature – for sin stocks persist after controlling for investor sentiment. Furthermore, we utilize a generalized autoregressive conditional heteroscedasticity-in-mean (GARCH) model to study the relationship between investor sentiment and the sin portfolio's conditional volatility. Our findings show that both individual and institutional investor sentiment are priced factors in sin stock returns. Furthermore, after controlling for the role of investor sentiment, the asset-pricing results suggest that the abnormal returns for sin stocks found in previous studies disappear. The results from the GARCH models indicate that investor sentiment has a significant impact on sin stocks’ conditional volatility.

Warrant introduction effects on stock return processes

As the underpricing of warrants remains unsolved after many adjustments presented by previous researchers, we further investigate the impact of the warrant introduction on the underlying stock return processes. This research attempts to determine whether the introduction of warrants influences the return processes of underlying stocks. If the introduction creates a potential dilution effect on stock return process, full dilution adjustment pricing models would lead to underpricing. To examine whether full dilution adjustment is required for warrant pricing, the Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M) model has been extended to derive four models for testing the dilution effect on stock return processes. Empirical results show that the volatilities of underlying stock return processes are significantly reduced following warrant introduction even after distinguishing dilution from asymmetric effect.

The Great Moderation and the Relationship between Output Growth and Its Volatility

This study examines the effect of the Great Moderation on the relationship between U.S. output growth and its volatility over the period 1947 to 2006. First, we consider the possible effects of structural changes in the volatility process. We employ generalized autoregressive conditional heteroscedasticity in mean (GARCH‐M) specifications, which describe output growth rate and its volatility with and without a one‐time structural break in volatility. Second, our data analyses and empirical results suggest no significant relationship between the output growth rate and its volatility; this favors the traditional wisdom of dichotomy in macroeconomics. Moreover, the evidence shows that the time‐varying variance falls sharply or even disappears once we incorporate a one‐time structural break in the unconditional variance of output starting in 1982 or 1984. That is, the integrated GARCH effect proves spurious. Finally, a joint test of a trend change and a one‐time shift in the volatility process finds that the one‐time shift dominates.

The empirical relationship between risk and return: evidence from the UK stock market

Previous studies reach no consensus on the relationship between risk and return using data from one market. We argue that the world market factor should not be ignored in assessing the risk–return relationship in a partially integrated market. Applying a bivariate generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) model to the weekly stock index returns from the UK and the world market, we document a significant positive relationship between stock returns and the variance of returns in the UK stock market after controlling for the covariance of the UK and the world market return. In contrast, conventional univariate GARCH-M models typically fail to detect this relationship. Nonnested hypothesis tests supplemented with other commonly used model selection criteria unambiguously demonstrate that our bivariate GARCH-M model is more likely to be the true model for UK stock market returns than univariate GARCH-M models. Our results have implications for empirical assessments of the risk–return relationship, expected return estimation, and international diversification.

Efficient pricing of ships in the dry bulk sector of the shipping industry

The aim of this paper was to investigate the validity of the Efficient Market Hypothesis in conjunction with Rational Expectations in the formation of dry bulk ship prices over the period January 1976-December 1997. Tests for market efficiency include those of orthogonality and unpredictability of excess returns on investments and tests based on the Vector Autoregressive models proposed by Campbell and Shiller. The latter methodology is extended further to a 3-variable Vector Autoregressive model, which is applicable to real assets with limited economic life. Results indicate that prices for newbuilding and second-hand vessels are not determined efficiently in the sense of Fama. Failure of the Efficient Market Hypothesis in the formation of ship prices is explained by the existence of timevarying risk premia, which relate excess returns to investors' perceptions of risk. These are modelled through the Generalized Autoregressive Conditional Heteroscedasticity in mean (GARCH-M) models. The results have important implications for shipping investment strategies, both in the newbuilding and second-hand markets.

Risk-return-volume relationship in an emerging stock market

This paper aims to provide empirical evidence for the risk-return-volume relationship in the Ýstanbul Stock Exchange (ISE) for the period 2 January 1992 to 29 May 1998. The Generalized Autoregressive Conditional Heteroscedasticity-in-Mean (GARCH-M) specification reveals that daily return volatility is time-varying and highly persistent. In addition, return is positively associated with risk, i.e. the estimate of the conditional standard deviation. Contemporaneous changes in volume have a positive effect on returns. The previous day's change in volume affects conditional volatility of returns positively.