scholarly journals A Delta Normal Approach for Modelling Risk Forecasting of Currency Portfolio

2020 ◽  
Vol 11 (4) ◽  
pp. 55-68
Author(s):  
Ardita Todri ◽  
Francesco Roberto Scalera
Keyword(s):  
Value At Risk ◽  
Average Method ◽  
Moving Average ◽  
Moving Window ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Risk Forecasting ◽  
Process Effects ◽  
International Trading ◽  
Weighted Moving Average

This research explores the benefits of a proactive model developed through delta normal approach implementation for the forecasting of currency portfolio volatility. The latter becomes a necessity for the Albanian agro exporters as they act in an international trading environment and face the de-Euroization process effects in domestic market. The forecasting of value at risk (VaR) at 99% confidence level is obtained through the implementation of a moving window containing 251 daily currency exchange rates logarithmic returns calculated by the exponentially weighted moving average method (EWMA). A decay factor of 0.94 is used in the simulated currency portfolios database (composed from six different currency positions) pertaining to 30 agro exporters in reference of 2018 year data. The analysis of incremental VaR decomposed in risk per currency unit and VaR contribution concludes that the implementation of this mechanism offers hedge opportunities and enables the agro exporters to undertake even speculative interventions.

scholarly journals Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process

2021 ◽  
Author(s):  
Massimiliano Frezza ◽  
Sergio Bianchi ◽  
Augusto Pianese
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Average Method ◽  
Moving Average ◽  
Computational Approach ◽  
Local Regularity ◽  
Price Process ◽  
Weighted Moving Average ◽  
Pointwise Regularity

AbstractA new computational approach based on the pointwise regularity exponent of the price time series is proposed to estimate Value at Risk. The forecasts obtained are compared with those of two largely used methodologies: the variance-covariance method and the exponentially weighted moving average method. Our findings show that in two very turbulent periods of financial markets the forecasts obtained using our algorithm decidedly outperform the two benchmarks, providing more accurate estimates in terms of both unconditional coverage and independence and magnitude of losses.

Study on Medium-Term and Short-Term Wind Power Forecasting Methods

2013 ◽  
Vol 361-363 ◽  
pp. 318-322
Author(s):  
Gui Zhong Wu ◽  
Yuan Biao Zhang ◽  
Cheng Su ◽  
Yu Jie Liu
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Average Method ◽  
Moving Average ◽  
Forecasting Model ◽  
Power Prediction ◽  
Medium Term ◽  
Accuracy Rate ◽  
Short Term ◽  
Weighted Moving Average

In the paper, the wind power prediction is devided into medium-term forecasts and short-term forecasts. For medium-term forecasts, we use the weighted moving average method and BP neural network forecasting model, while for short-term forecasts, the ARMA model and combination forecasting model based on the maximum entropy principle are used. The application example shows that the weighted moving average method is easy and can precisely obtain the fluctuation trend of the wind power, while the accuracy rate of the BP neural network forecasting model is 91.23%, which is better than the former. The predictive results of the ARMA model are similar with actual trends and its accuracy rate is 88.98%. The combination model integrates the advantages of the BP neural network and ARMA model, and its accuracy rate is up to 92.58%.

Revisiting the Holt-Winters' Additive Method for Better Forecasting

2019 ◽  
Vol 15 (2) ◽  
pp. 43-57
Author(s):  
Seng Hansun ◽  
Vincent Charles ◽  
Christiana Rini Indrati ◽  
Subanar
Keyword(s):  
Time Series ◽  
Average Method ◽  
Data Science ◽  
Time Series Data ◽  
Initial Conditions ◽  
Moving Average ◽  
Series Data ◽  
Initial Values ◽  
Additive Method ◽  
Weighted Moving Average

Time series are one of the most common data types encountered by data scientists and, in the context of today's exponentially increasing data, learning how to best model them to derive meaningful insights is an important skill in the Big Data and Data Science toolbox. As a result, many researchers have dedicated their efforts to developing time series analysis methods to predict future values based on previously observed values. One of the well-known methods is the Holt-Winters' seasonal method, which is commonly used to capture the seasonality effect in time series data. In this study, the authors aim to build upon the Holt-Winters' additive method by introducing new formulas for finding the initial values. Obtaining more accurate estimations of the initial values could result in a better forecasting result. The authors use the basic principle found in the weighted moving average method to assign more weight to the most recent data and combine it with the original initial conditions found in the Holt-Winters' additive method. Based on the experiment performed, the authors conclude that the new formulas for finding the initial values in the Holt-Winters' additive method could give a better forecasting when compared to the traditional Holt-Winters' additive method and the weighted moving average method in terms of the accuracy level.

MODEL RISK IN VaR ESTIMATION: AN EMPIRICAL STUDY

2006 ◽  
Vol 05 (03) ◽  
pp. 503-512 ◽  
Author(s):  
JING YAO ◽  
ZHONG-FEI LI ◽  
KAI W. NG
Keyword(s):  
Empirical Study ◽  
Value At Risk ◽  
Stock Exchange ◽  
Statistical Tests ◽  
Moving Average ◽  
Specification Test ◽  
Model Risk ◽  
Lr Test ◽  
Weighted Moving Average ◽  
Better Than

This paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk.

Disk usage prediction based on an improved weighted moving average method

2016 ◽  
Vol 33 (1) ◽  
pp. 72
Author(s):  
Zonghua Zhang ◽  
Haiquan Zhang ◽  
Shihang Li ◽  
Xinzheng Niu
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Weighted Moving Average

Incorporating the Time-Varying Tail-Fatness into the Historical Simulation Method for Portfolio Value-at-Risk

2006 ◽  
Vol 09 (02) ◽  
pp. 257-274 ◽  
Author(s):  
Chu-Hsiung Lin ◽  
Chang-Cheng Chang Chien ◽  
Sunwu Winfred Chen
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Moving Average ◽  
Simulation Method ◽  
Estimation Accuracy ◽  
Time Varying ◽  
Historical Simulation ◽  
Extreme Risk ◽  
Weighted Moving Average

This study extends the method of Guermat and Harris (2002), the Power EWMA (exponentially weighted moving average) method in conjunction with historical simulation to estimating portfolio Value-at-Risk (VaR). Using historical daily return data of three hypothetical portfolios formed by international stock indices, we test the performance of this modified approach to see if it can improve the precise forecasting capability of historical simulation. We explicitly highlight the extended Power EWMA owns privileged flexibilities to capture time-varying tail-fatness and volatilities of financial returns, and therefore may promote the quality of extreme risk management. Our empirical results, derived from the Kupiec (1995) tests and failure ratios, show that our proposed method indeed offers substantial improvements on capturing dynamic returns distributions, and can significantly enhance the estimation accuracy of portfolio VaR.

scholarly journals DRG-Oriented Mathematical Calculation Model and Method of Integrated Medical Service Cost

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaowei Sun ◽  
Yi Zhu
Keyword(s):  
Average Method ◽  
Medical Service ◽  
Moving Average ◽  
Maximum Error ◽  
Calculation Model ◽  
Medical Services ◽  
Activity Based Costing ◽  
Service Cost ◽  
The Cost ◽  
Weighted Moving Average

In the context of the new round of medical and health reform, in order to alleviate the problem of “difficult to see a doctor and expensive to see a doctor,” the state focuses on reducing the cost of medical services, so it puts forward the calculation and method research of medical costs. The purpose of this study is to calculate and predict the cost of medical services in a DRG-oriented integrated environment. In this study, activity-based costing and weighted moving average methods are used. First, basic data of medical services are collected, then all medical activities are confirmed and all service costs are collected, then a cost database is established, and a calculation model of medical costs is designed. Finally, calculation suggestions and optimization methods are put forward by analyzing the calculated data. The experimental results show that the actual demand of drugs predicted by the general moving average method is relatively insufficient, with the maximum error of 41%, the minimum of 5%, and the average error of 19.8%; the maximum error of drug demand predicted by the weighted moving average method is 24%, the minimum is 2%, and the average is 15.4%. It can be concluded that the prediction effect of the weighted moving average method is better than that of the ordinary moving average method, which plays a good and effective role in the prediction of medical cost. The activity-based costing method is more detailed and organized for the cost calculation and classification of medical services. It provides a certain value for the effective management and control of medical service cost.

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