Mixing ARMA Models with EGARCH Models and Using it in Modeling and Analyzing the Time Series of Temperature

In this article our goal is mixing ARMA models with EGARCH models and composing a mixed model ARMA(R,M)-EGARCH(Q,P) with two steps, the first step includes modeling the data series by using EGARCH model alone interspersed with steps of detecting the heteroscedasticity effect and estimating the model's parameters and check the adequacy of the model. Also we are predicting the conditional variance and verifying it's convergence to the unconditional variance value. The second step includes mixing ARMA with EGARCH and using the mixed (composite) model in modeling time series data and predict future values then asses the prediction ability of the proposed model by using prediction error criterions.

Forecast Combinations for Structural Breaks in Volatility: Evidence from BRICS Countries

The aim of this paper is to investigate the relevance of structural breaks for forecasting the volatility of daily returns on BRICS countries (Brazil, Russia, India, China and South Africa). The data set used in the analysis is the Morgan Stanley Capital International MSCI daily returns and covers the period from 19 July 1999 to 16 July 2015. To identify structural breaks in the unconditional variance, a binary segmentation algorithm with a test, which considers both the fourth order moment of the process and persistence in the variance, has been implemented. Some forecast combinations that account for the identified structural breaks have been introduced and their performance has been evaluated and compared by using the Model Confidence Set (MCS). The results give significant evidence of the relevance of the structural breaks. In particular, in the regimes identified by the structural breaks, a substantial change in the unconditional variance is quite evident. In forecasting volatility, the combination that averages forecasts obtained using different rolling estimation windows outperforms all the other combinations

Mudança de Regime e Efeito ARCH em Volatilidade: Um Estudo dos Choques das Cotações do Petróleo

Petroleum is an important energy commodity, being used in different activities, having a direct or indirect effect on several sectors in the economy. This commodity has unstable prices, as a result of geopolitical shocks as well as market shocks in the perspective of technological innovation in the area of energy and changing consumption patterns. In this work we study the volatility of the main reference oil prices with three models: GARCH; GARCH with regime change (MS-GARCH); and unconditional variance model with regime change (MSIH). The models are compared in terms of predictive performance and value-at-risk outside the estimation sample. We can identify different regimes on oil prices. The models with Markovian Switching are the best models using predictive performance and also the value at risk performance metric.

Volatility Modeling with Leverage Effect under Laplace Errors

This paper discusses four GARCH-type models (A-GARCH, NA-GARCH, GJR-GARCH, and E-GARCH) in representing volatility of financial returns with leverage effect. In these models, errors are assumed to follow a Laplace distribution in order to deal with the typical leptokurtic feature of financial returns. The properties of these models are analyzed theoretically in terms of unconditional variance, kurtosis, autocorrelation function and news impact, and are further examined in the applications to real financial time series. Comparison is made with other choices of error distributions such as normal, Student-5, and Student-7 distributions, respectively. We also conduct residual analyses to justify the choice of error distributions and find that Laplace-E-GARCH model still performs very well. Our main purpose is to study and compare the proposed models’ relative adequacies and underlying limitations.

CAUSALITY-IN-VARIANCE BETWEEN THE STOCK MARKET AND MACROECONOMIC VARIABLES IN SINGAPORE

The present study investigates the causality-in-variance between macroeconomic variables and the stock market in Singapore from November 1990 to March 2013. This study utilizes the [Sanso, A, V Arago and JL Carrion (2004). Testing for change in the unconditional variance of financial time series. Revista de Economia Financiera, 4, 32–53.] test to detect the structural break in variance; a generalized autoregressive conditional heteroskedasticity (GARCH) process to model volatility; and the cross correlation function (CCF) causality method. The results demonstrate that the most important macroeconomic causes of stock market volatility in Singapore is exchange rate volatility. The findings provide preliminary insights on risk elements for policy makers monitoring the stability of financial markets by providing insights about volatility spillovers and risk transmission between the stock market and macroeconomic variables in Singapore.

The welfare effects of coffee price volatility for Ethiopian coffee producers

Purpose This paper aims to estimate the welfare effects for Ethiopian coffee producers from eliminating coffee price volatility. Design/methodology/approach To estimate volatility, the generalised autoregressive conditional heteroskedasticity technique is applied to monthly coffee prices in Ethiopia for the period 1976-2012. To distinguish between the unpredictable and predictable components of volatility, we obtain separate estimates of the conditional and unconditional variance of the residual. This is combined with estimates of the coefficient of relative risk aversion to measure the welfare effects from eliminating the unpredictable component of price volatility. Findings A key finding is that the welfare gain from eliminating coffee price volatility is small; the gain per producer comes to a meagre US$0.76 in a year. Originality/value This has important policy implications for the efficacy of price stabilisation mechanisms for coffee producers, i.e. any attempt to eliminate coffee price volatility at a cost may not be a preferred outcome for Ethiopian producers. The contribution of the paper lies in using the unconditional variance, as it more truly reflects price risk faced by coffee producers without overestimating it.

Testing constancy of unconditional variance in volatility models by misspecification and specification tests

The topic of this paper is testing the hypothesis of constant unconditional variance in GARCH models against the alternative that the unconditional variance changes deterministically over time. Tests of this hypothesis have previously been performed as misspecification tests after fitting a GARCH model to the original series. It is found by simulation that the positive size distortion present in these tests is a function of the kurtosis of the GARCH process. Adjusting the size by numerical methods is considered. The possibility of testing the constancy of the unconditional variance before fitting a GARCH model to the data is discussed. The power of the ensuing test is vastly superior to that of the misspecification test and the size distortion minimal. The test has reasonable power already in very short time series. It would thus serve as a test of constant variance in conditional mean models. An application to exchange rate returns is included.