Several competing intraday volatility forecasting models for equally spaced data have been proposed in the literature. This study reviews a number of models and compares their forecasting performance using data on the market index of the Warsaw Stock Exchange. We also discuss choice criteria and issues specific to volatility forecast evaluation.
We present an intraday volatility model for equally spaced data and apply it for the WIG Index- a broad market index of the Warsaw Stock Exchange. The current study is an application and extension of the model proposed by Engle and Sokalska [2010]. We decompose the conditional variance of intraday returns into components that have a natural interpretation and can be easily estimated.
In this paper a new ARCH‑type volatility model is proposed. The Range‑based Heterogeneous Autoregressive Conditional Heteroskedasticity (RHARCH) model draws inspiration from Heterogeneous Autoregressive Conditional Heteroskedasticity presented by Muller et al. (1995, pp. 213–239), but employs more efficient, range‑based volatility estimators instead of simple squared returns in a conditional variance equation. In the first part of this research range‑based volatility estimators (such as Parkinson, or Garman‑Klass estimators) are reviewed, followed by derivation of the RHARCH model. In the second part of this research the RHARCH model is compared with selected ARCH‑type models with particular emphasis on forecasting accuracy. All models are estimated with a maximum likelihood method using data containing EURPLN spot rate quotation. Results show that RHARCH model often outperforms return‑based models in terms of predictive abilities in both in‑sample and out‑of‑sample periods. Also properties of standardized residuals are very encouraging in the case of the RHARCH model.
This paper examines whether VaR models that are created and suited for developed and liquid markets apply to the volatile and shallow financial markets of EU candidate states. To this end, several VaR models are tested on five official stock indexes from EU candidate states over a period of 500 trading days. The tested VaR models are: a historical simulation with rolling windows of 50, 100, 250 and 500 days, a parametric variance-covariance approach, a BRW historical simulation, a RiskMetrics system and a variance-covariance approach using GARCH forecasts. Based on the backtesting results it can be concluded that VaR models that are commonly used in developed financial market are not well-suited to measuring market risk in EU candidate states. Using some of the most widespread VaR models in these circumstances may result in serious problems for both banks and regulators.
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