Forecasting volatility of emerging stock markets: linear versus non-linear GARCH models

Gokcan, Suleyman

doi:10.1002/1099-131x(200011)19:6<499::aid-for745>3.0.co;2-p

Cited by 96 publications

(54 citation statements)

References 7 publications

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“…Consistent with many previous studies (see for example, Franses & Van Dijk [43], Gokcan [44] and AL-Najjar [45], the study applies the GARCH (1,1). This …”

Section: The Garch (11) Estimation Resultsmentioning

confidence: 99%

Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models

Epaphra¹

2017

JMF

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Policy makers need accurate forecasts about future values of exchange rates. This is due to the fact that exchange rate volatility is a useful measure of uncertainty about the economic environment of a country. This paper applies univariate nonlinear time series analysis to the daily (TZS/USD) exchange rate data spanning from January 4, 2009 to July 27, 2015 to examine the behavior of exchange rate in Tanzania. To capture the symmetry effect in exchange rate data, the paper applies both ARCH and GARCH models. Also, the paper employs exponential GARCH (EGARCH) model to capture the asymmetry in volatility clustering and the leverage effect in exchange rate. The paper reveals that exchange rate series exhibits the empirical regularities such as clustering volatility, nonstationarity, non-normality and serial correlation that justify the application of the ARCH methodology. The results also suggest that exchange rate behavior is generally influenced by previous information about exchange rate. This also implies that previous day's volatility in exchange rate can affect current volatility of exchange rate. In addition, the estimate for asymmetric volatility suggests that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. The main policy implication of these results is that since exchange rate volatility (exchange-rate risk) may increase transaction costs and reduce the gains to international trade, knowledge of exchange rate volatility estimation and forecasting is important for asset pricing and risk management.

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“…Consistent with many previous studies (see for example, Franses & Van Dijk [43], Gokcan [44] and AL-Najjar [45], the study applies the GARCH (1,1). This …”

Section: The Garch (11) Estimation Resultsmentioning

confidence: 99%

Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models

Epaphra¹

2017

JMF

View full text Add to dashboard Cite

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“…The standard way is to use the square of returns (or returns minus the sample mean if it is not zero) as an approximation (see, inter alia, Poon andGranger, 2003, andGokcan, 2000). Andersen and Bollerslev (1998), however, argued that using this definition generally leads to a model with poor goodness-of-fit, because this measurement typically displayed a large degree of idiosyncratic, observation-by-observation variation (see particularly their Figure 1).…”

Section: Application To Volatility Forecastmentioning

confidence: 99%

Forecasting using locally stationary wavelet processes

Xie

Ranneby

2009

Journal of Statistical Computation and Simulation

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Locally stationary wavelet (LSW) processes, built on non-decimated wavelets, can be used to analyze and forecast non-stationary time series. They have been proved useful in the analysis of financial data. In this paper we first carry out a sensitivity analysis, then propose some practical guidelines for choosing the wavelet bases for these processes. The existing forecasting algorithm is found vulnerable to outliers, and a new algorithm is proposed to avoid the sensitivity to extreme observations. The new algorithm is shown stable and outperforms the existing algorithm when applied to real financial data. The volatility forecasting ability of LSW modeling based on our new algorithm is then discussed and shown to be competitive with traditional GARCH models.

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“…Related to this issue, Hill et al (1988) show that unexpected changes in volatility are the most important risk factor in determining the cost of portfolio insurance. Similarly, Chu and Bubnys (1990) use a likelihood ratio test to compare the variance measure of price volatilities of stock market indices and their corresponding futures contracts during the bull market of the 1980s, and find that spot market volatilities are significantly lower than their respective futures price volatilities.…”

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confidence: 99%

“…Braiisford and Faff (1996), on the other hand, find that GARCH models are slightly superior to most simple models for forecasting Australian monthly stock index volatility, and Frances and van Dijk (1996) find that the asymmetric GARCH models perform no better than the standard GARCH model in forecasting the weekly volatility of various European stock markets. Gokcan (2000) finds that, for emerging stock markets, the basic GARCH(1,1) model performs better than EGARCH models, while Wei (2002) presents QGARCH as a better model than either the basic GARCH or the GJR-GARCH model for forecasting the weekly volatility on the China stock market.…”

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confidence: 99%