Generalized Autoregressive Conditional Heteroskedasticity In Mean Research Articles

Dynamic and Asymmetric Response of Inequality to Income Volatility: The Case of the United Kingdom

Using the quarterly data of the United Kingdom (UK) for the period from 1975Q1 to 2016Q1, the paper analyses the dynamic and the asymmetric responses of inequality to the real gross domestic product (GDP) (income) volatility. For this purpose, we consider the bivariate Generalized Autoregressive Conditional Heteroskedasticity-in-mean (GARCH-M) Structural Vector Autoregressive (VAR) based models to examine the related relationship. Applying this method to the different measures of both income- and consumption inequality (i.e. the measures of the Gini, the standard deviation, and the 90–10 percentile differential), we find that income volatility has an increasing effect on inequality. Not only the real GDP volatility significantly increases inequality, but also its effect is asymmetric. In other words, inequality differently responds to positive and negative income growth volatility shocks. Moreover, the volatility in the GDP-inequality equation tends to amplify the positive dynamic response of inequality to a positive income shock, while diminishing the response of inequality to a negative income shock. The implications of these findings are also drawn.

Growth and Volatility: An Analysis for the Brazilian Economy

This paper aims to analyze if there is a relationship between economic growth and the volatility of that growth in the Brazilian economy, and, if it exists, to infer if that relationship is positive or negative, since the literature shows evidence for both cases. For that purpose, the econometric strategy used is that of a Generalized Autoregressive Conditional Heteroskedasticity in Mean (GARCH-M) model, using economic growth data compiled by the Central Bank of Brazil, for the period of 1995-2018. The results corroborate the findings of the empirical literature, suggesting a negative relationship between economic growth and its volatility; that is, the hypothesis of the irreversibility of investments prevails. Therefore, the tradeoff between short-term stability and long-term growth for the Brazilian economy in the analyzed period does not seem to occur.

Modelling and forecasting inflation in Egypt: univariate and multivariate approaches

Inflation forecasts are highly important in the actual management of monetary policy implying that central banks must acquire accurate inflation forecasts. Given the high risks involved in inflation, models that account for uncertainty are expected to do well in terms of forecasting power. The current paper focuses on estimating 12 different specifications including 10 univariate models and 2 multivariate models. The univariate models are generalized autoregressive conditional heteroskedasticity in mean (GARCH-M) and threshold autoregressive generalized conditional heteroskedasticity in mean (TARCH-M) models assuming three different distributions for the error term as well as an extension of the GARCH-M model that allows for time-varying higher order moments. Furthermore, we employed three models that take into consideration the possibility of structural shifts, namely, Markov switch, threshold autoregressive and time-varying coefficients (TVCs) models. The multivariate models are the vector- half operator model and the dynamic stochastic general equilibrium - vector autoregression model (DSGE-VAR). Results indicate that TVCs model and time-varying higher moments outperform other competing models. Finally, forecasts are improved by generating combined forecasts using equal weights, Bayesian model averaging and dynamic model averaging.

Empirical investigation on modeling solar radiation series with ARMA–GARCH models

Simulation of radiation is one of the most important issues in solar utilization. Time series models are useful tools in the estimation and forecasting of solar radiation series and their changes. In this paper, the effectiveness of autoregressive moving average (ARMA) models with various generalized autoregressive conditional heteroskedasticity (GARCH) processes, namely ARMA–GARCH models are evaluated for their effectiveness in radiation series. Six different GARCH approaches, which contain three different ARMA–GARCH models and corresponded GARCH in mean (ARMA–GARCH-M) models, are applied in radiation data sets from two representative climate stations in China. Multiple evaluation metrics of modeling sufficiency are used for evaluating the performances of models. The results show that the ARMA–GARCH(-M) models are effective in radiation series estimation. Both in fitting and prediction of radiation series, the ARMA–GARCH(-M) models show better modeling sufficiency than traditional models, while ARMA–EGARCH-M models are robustness in two sites and the ARMA–sGARCH-M models appear very competitive. Comparisons of statistical diagnostics and model performance clearly show that the ARMA–GARCH-M models make the mean radiation equations become more sufficient. It is recommended the ARMA–GARCH(-M) models to be the preferred method to use in the modeling of solar radiation series.

Previsão do câmbio real-dólar sob um arcabouço de apreçamento de ativos

Este artigo estima via componentes principais uma serie temporal para o Fator Estocastico de Desconto contendo informacoes sobre os retornos de fundos cambiais e de renda fixa operando no Brasil, visando utiliza-lo na modelagem do câmbio nominal mensal Real Brasileiro/Dolar Americano atraves de um Generalized Autoregressive Conditional Heteroskedasticity-in-Mean (GARCH-M) bivariado. Este exercicio empirico baseado na teoria de aprecamento de ativos segue metodologicamente Chong et alii (2002) e Da Costa et alii (2010), sendo possivel evidenciar um erro de previsao de 5,27% para depreciacao cambial e um acerto do sinal da variacao cambial em 57,5% dos meses, durante o periodo de janeiro de 2000 a dezembro de 2009. Estes resultados sugerem a literatura que nao se deve desprezar a variacao temporal dos momentos condicionais de segunda ordem.

Risk-Return Tradeoff in U.S. Stock Returns over the Business Cycle

AbstractIn the empirical finance literature, findings on the risk-return tradeoff in excess stock market returns are ambiguous. In this study, I develop a new qualitative response (QR)-generalized autoregressive conditional heteroskedasticity-in-mean (GARCH-M) model combining a probit model for a binary business cycle indicator and a regime-switching GARCH-M model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional intertemporal capital asset pricing model (ICAPM), there is a positive risk-return relationship between volatility and expected return independent of the state of the economy.

The Impact of Investor Sentiment on Excess Returns: A Taiwan Stock Market Case

In this paper, we use a proxy for investor sentiment and employ a generalized autore-gressive conditional heteroskedasticity in mean (GARCH-M) model to show the impact of investor sentiment on excess returns in Taiwan stock market. Firstly, the evidences suggest that the change in trading volume is a suitable proxy for investor sentiment. Furthermore, the conditional volatility and excess returns have a negative and significant relationship. We argue that the irrational sentiment has influence on stock valuations.

A comparative study of technical trading strategies and return predictability: an extension of using NYSE and NASDAQ indices

This study extends the work of Brock et al.’s (1992) empirical analysis on technical trading rules (price and momentum) by including trading volume moving averages; broader indices (New York Stock Exchange (NYSE) and National Association of Security Dealers Automatic Quotations (NASDAQ)) covering both large-cap and small-cap firms using market weightings; and focusing on a time period that includes great innovations in trading and disseminating data to the market. Similar to their study, we base our conclusions on nonparametric analysis. By extending the t-test analysis through a residual bootstrap methodology utilizing a random walk, a generalized autoregressive conditional heteroskedasticity in mean (GARCH-M), and a GARCH-M with instrument variables, criticisms of earlier technical analysis are mitigated. Overall, the results support Brock et al.’s (1992) price-weighted index (Dow Jones Industrial Average (DJIA)) analysis by showing that the technical trading rules add value by capturing profit opportunities when compared to a buy-and-hold strategy. When the analysis of the trading rules are applied to different time periods, the results reveal a weakening in profit potential over time. This may imply that the market is becoming more efficient in disseminating information to a wider range of investors.

A multivariate GARCH in mean approach to testing uncovered interest parity: evidence from Asia-Pacific foreign exchange markets

The existence of time-varying risk premia in deviations from uncovered interest parity (UIP) is investigated based on a conditional capital asset pricing model (CAPM) using data from four Asia-Pacific foreign exchange markets. A parsimonious multivariate generalized autoregressive conditional heteroskedasticity in mean (GARCH-M) parameterization is employed to model the conditional covariance matrix of excess returns. The empirical results indicate that when each currency is estimated separately with an univariate GARCH-M parameterization, no evidence of time-varying risk premia is found except Malaysian ringgit. However, when all currencies are estimated simultaneously with the multivariate GARCH-M parameterization, strong evidence of time-varying risk premia is detected. As a result, the evidence supports the idea that deviations from UIP are due to a risk premium and not to irrationality among market participants. In addition, the empirical evidence found in this study points out that simply modeling the conditional second moments is not sufficient enough to explain the dynamics of the risk premia. A time-varying price of risk is still needed in addition to the conditional volatility. Finally, significant asymmetric world market volatility shocks are found in Asia-Pacific foreign exchange markets.

The Usefulness of Economic Value-Added in the Australian Context

This paper employs a Generalized Autoregressive Conditional Heteroskedasticity in Mean (GARCH-M) model to consider the effect of macroeconomic factors on Australian property returns over the period 1985 to 2002. Three direct (office, retail and industrial property) and two indirect (listed property trust and property stock) returns are included in the analysis, along with market returns, short, medium and long-term interest rates, expected and unexpected inflation, construction activity and industrial employment and production. In general, the macroeconomic factors examined are found to be significant risk factors in Australian commercial property returns. However, the results also indicate that forecast accuracy in these models is higher for direct office, listed property trust and property stock returns and that the persistence of volatility shocks varies across the different markets, with volatility half lives of between five and seven months for direct retail and industrial property, two and three months for direct office property and less than two months with both forms of indirect property investment.

The autocorrelation structure for the GARCH-M process

The autocorrelation structure for the generalized autoregressive conditional heteroskedasticity in mean (GARCH-M) process is presented. It is shown that the autocorrelations for the GARCH-M process are similar to that for ARMA process and are nonnegative under the conventional restrictions on the parameters of GARCH process.