models (GARCH), which are widely used for forecasting non - stationary time series. GARCH methodology was discussed and non … (Baltijos Šalys). Pirmojoje darbo dalyje išnagrinėti apibendrinti autoregresiniai sąlyginio heteroskedastiškumo modeliai (GARCH …), kurie dažniausiai yra taikomi nestacionarių laiko eilučių prognozavimui. Aptarta GARCH metodologija, pateikiami netiesinių …

, out-of-sample forecasting was carried out for USD/EUR and GBP/JPY currency pairs. Forecasting results show that GARCH …Currency exchange rate forecasting is a subject that concerns many parties that participate in foreign exchange market … participants, different currency instruments traded, and a 24 hour timeframe. Statistical approaches such as ARIMA and GARCH models …

Forecasting is an underestimated field of research in supply chain management. Recently advanced methods are coming … and transparency. In this chapter we explore advanced forecasting tools for decision support in supply chain scenarios and …Making sense of data may benefit from high volume data acquisition and analysis using GARCH and VAR-MGARCH (Datta et al …

Proof that application of GARCH technique offers potential for profitability. Forecasting is an underestimated field of … forecasting methods in context of supply chains and demonstrated financial profitability from use of the GARCH technique. It … advanced forecasting tools for decision support in supply chain scenarios and provide preliminary simulation results from their …

practitioners. The GARCH model has been exceptionally successful in this area. Our approach, the minimally cross …-entropic conditional density (MCECD) model, is a generalization of GARCH(1,1) which can cope with conditional skewness and kurtosis. It is …

conditional heteroskedasticity (GARCH) model is used to identify the magnitude and significance of mean and volatility spillovers … of strong ARCH and GARCH effects. Contrary to evidence from studies in North American electricity markets, the results …

This study employs an extended version of the Generalised Autoregressive Conditional Heteroskedasticity in Mean (GARCH …

on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially … weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH … framework for volatility forecasting. This chapter proposes a one-factor and a two-factor model that combine useful properties …