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The Black-Litterman asset allocation model is an extended portfolio management model to construct optimal portfolios by combining the market equilibrium with investor views into asset allocation decisions. In this paper we apply Black-Litterman model for portfolio optimization on Vietnames stock market. We chose ARIMA methodology utilized in financial econonometrics to predict the views of investor which are used as inputs of the Black-Litterman asset allocation process to find optimal portfolio and weights.

This paper presents a forecasting model for the mortality rates of COVID-19 in six of the top most affected countries depending on the hybrid Genetic Algorithm and Autoregressive Integrated Moving Average (GA-ARIMA). It was aimed to develop an advanced and reliable predicting model that provides future forecasts of possible confirmed cases and mortality rates (Total Deaths per 1 million Population of COVID-19) that could help the public health authorities to develop plans required to resolve the crisis of the pandemic threat in a timely and efficient manner. The study focused on predicting the mortality rates of COVID-19 because the mortality rate determines the prevalence of highly contagious diseases. The Genetic algorithm (GA) has the capability of improving the forecasting performance of the ARIMA model by optimizing the ARIMA model parameters. The findings of this study revealed the high prediction accuracy of the proposed (GA-ARIMA) model. Moreover, it has provided better and consistent predictions compared to the traditional ARIMA model and can be a reliable method in predicting expected death rates as well as confirmed cases of COVID-19. Hence, it was concluded that combining ARIMA with GA is further accurate than ARIMA alone and GA can be an alternative to find the parameters and model orders for the ARIMA model.

In this paper, we put forward a new hybrid methodology to generate forecasts of time series. Indeed, the proposed forecaster is a HWCF that integrates the following techniques: wavelet decomposition; ARIMA models; SVRs; wavelet combination of forecasts; and non-linear programming. Basically, the HWCF is able to capture, simultaneously, linear and non-linear auto-dependence structures exhibited by a time series, which are represented, at time t, by both the linear and non-linear combined forecasts: and , respectively. After obtaining the combined forecasts and, they are summed (i.e., ), producing the hybrid forecast , for each instant t. The numerical results show that HWCF achieved relevant accuracy gains in forecasting process of the annual time series of sunspot, when comparing with other ten competitive forecasters.

This paper investigates three stochastic modelling procedures for generating $N$ (user specified) synthetic annual electricity demand profiles at one-minute resolution. The paper reviews previous work in the application of HMM for synthesizing highly stochastic time-series of domestic electricity demand through a sophisticated framework coalescing 480 distinct HMM. The efficiency of a proposed approach for integrating a time-series deseasonalizing technique with a single HMM has been studied in parallel with a compatible stochastic modeling framework of a time-series deseasonalized ARIMA model. Various statistical measures/characteristics of the real and synthetic profiles have been compared for all the three stochastic modelling procedures to identify the most efficient and practically suitable medium for generating synthetic electricity time-series at a fine temporal resolution. Results have been shown for both the individual buildings and the composite (aggregated) profiles of many buildings.

The accurate forecasting of freight rate index is one of the most important issues in shipping market. The continuous and jump-diffusion stochastic differential equations are used for modeling and forecasting of Baltic exchange Dirty Tanker Index (BDTI). Actual observations and simulated data are applied to estimate the best stochastic model. The comparison of forecasting between SDE methods and the ARIMA time series models show that SDE models have better accuracy than the time series techniques.

The main purposes of this introduction chapter are (i) to give an overview of the following 109 papers, which discuss investment analysis, portfolio management, and financial derivatives; (ii) to classify these 109 chapters into nine topics; and (iii) to classify the keywords in terms of chapter numbers.

Accidents involving heavy metal pollution of water and the environment cause huge casualties and loss of property, making the monitoring of heavy metal pollution of great importance. In order to overcome the disadvantages of the traditional model algorithm, such as low predictive accuracy and poor fusion ability, a new combined forecasting model was developed based on the autoregressive integrated moving average model and grey theory. The autoregressive integrated moving average model time series forecasting methods were used to make a preliminary forecast for heavy metal historical data. Then a non-interval GM(1,1) model was built. The model residual of initial forecasts were put as the input of GM(1,1) to be modified. The non-interval GM(1,1) model is established regarding the residual value of the ARIMA. Finally, the initial forecasts of ARIMA model and the residual prediction value of GM(1, 1) fusion and combination forecast GM-ARIMA model were constructed. The heavy metals monitoring data of a river in Hebei province in 1978–2007 were taken as the sample to be analyzed. The predictive results of the autoregressive integrated moving average model, GM(1,1) model and the GM-ARIMA model are compared. The results show that the GM-ARIMA model has better predictive performance and fusion ability.

In order to achieve the accurate forecasting of dynamic gas emission in coal mining and heading working face, firstly, this paper utilizes grey model GM (1,1) and Autoregressive Integrated Moving Average Model (ARIMA) to forecast the concentration of gas emission respectively based on the time series, and then a new combination forecasting model, ARIMA-GM, built by the method of variance reciprocal weighting is represented to forecast and analyze the concentration of gas emission, finally, the gas pre-warning is obtained according to forecasting results. An application example of forecasting in 10502 heading working face of Xin Xing coal mine is presented and its results show that the combination forecasting model of ARIMA and GM provides a better fitting effect and a higher forecasting accuracy, and which has certain general employing, in addition, its guiding significance to the safe mining and heading of coal mine are determined.