Kurs JISDOR selama pandemi COVID-19 berpengaruh terhadap perekonomian Indonesia, sehingga tujuan penelitian ini adalah memodelkan data kurs pandemi. Model dibentuk mengikuti sifat- sifat yang dimiliki tersebut. Data memiliki tren dan non stasioner, maka di differencing 1, menjadi stasioner setelah uji ADF. Kemudian sesuai plot ACF, PACF, nilai minimum AIC SIC didapatkan model tepat ARIMA(1,1,1)...

Abstract A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds certain threshold value is proposed. The method consists of bootstrap algorithm combines simulation techniques with estimations trend and variability. local linear estimator, considering bandwidth matrix selected by takes spatial dependence into account, used trend. var...

This paper develops a new econometric framework for investigating how the sensitivity of the financial market volatility to shocks varies with the volatility level. For this purpose, the paper first introduces the square-root (SQ) GARCH model for financial time series. It is an ARCH analogue of the continuous-time square-root stochastic volatility model popularly used in derivatives pricing and...

In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The modeling framework incorporates diurnal and annual periodicity modeling by peri...

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression-based t...

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. Th...

We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. We call the model Dynamic Factor GARCH, as the information contained in large macroeconomic or financial datasets is captured by a few dynamic common factors, which we assume being conditionally heteroskedastic. After describing the estimation of the model, we present simulation res...

Stocks are one of the best-known forms investment and still used today. In stock investment, it is necessary to know movement risk loss that may be obtained from so investors can consider possibility profit. One way calculating use Expected Shortfall (ES). Because in form a time series, model formed predict which then for ES calculations using series analysis. The purpose study was determine ex...

The aim of this research was to present the changes in biomass production, especially pellets Poland, context world’s and European Union’s (EU) climate energy policy, compared other renewable sources. We also analyzed law concerning production EU. Finally, we have elaborated prognosis pellet on world scale. used different methods achieve goals, among which most important are Generalized Autoreg...