scholarly journals PREDICTIVE ACCURACY OF FUTURES OPTIONS IMPLIED VOLATILITY: THE CASE OF THE EXCHANGE RATE FUTURES MEXICAN PESO-US DOLLAR

2017 ◽  
Vol 5 (9) ◽  
pp. 41
Author(s):  
Guillermo Benavides
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Implied Volatility ◽  
Predictive Accuracy ◽  
Research Effort ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Coefficient Of Determination ◽  
Mexican Peso ◽  
Volatility Forecast

There has been substantial research effort aimed to forecast futures price return volatilities of financial assets. A significant part of the literature shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of several volatility forecast models for the case of the Mexican peso-USD exchange rate futures returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), two option implied volatility models and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. Different to other works in the literature, in this paper there is a more rigorous analysis of the option implied volatilities calculations. The results show that the option implied models are superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-squared-errors. However, the results should be taken with caution given that the coefficient of determination in the regressions was relatively low. According to these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied.

Construction and Application of WD-GA-SVR Model - CSI 300 Index Forecast

2012 ◽  
Vol 263-266 ◽  
pp. 1619-1622
Author(s):  
Li Jing Zhang ◽  
Zheng Ming Qian ◽  
Heng Zhang
Keyword(s):  
Time Series ◽  
Predictive Accuracy ◽  
Financial Time Series ◽  
Optimal Parameter ◽  
Forecast Accuracy ◽  
Policy Makers ◽  
Original Sequence ◽  
Financial Time ◽  
Learning Machine ◽  
Parameter Values

In order to improve the predictive accuracy of SVM on financial time series, we propose a WD-GA-SVR model in this paper. First, do wavelet denoising (WD) to financial time series to eliminate the interference noise in the original sequence; and use the genetical gorithm (GA) for parameter optimization to make up selecte defects of the parameters c and g; use the obtained optimal parameter values to train to formate the optimal SVM learning machine. The forecast of the order time delay data of CSI 300 index and the comparative analysis of the SVR model, GA-SVR and the WD-GA-SVR model predictions indicate that the WD-GA-SVR model significantly improve the robustness of the prediction model and forecast accuracy, can provide a reference for policy makers and investors.

scholarly journals APPLYING M-ESTIMATES TO DEFINE START VALUE OF EXPONENTIAL AVERAGE IN BRAUN’S FORECAST MODEL OF ZERO LEVEL

2021 ◽  
pp. 150-165
Author(s):  
Александр Анатольевич Васильев
Keyword(s):  
Time Series ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Short Term ◽  
Simple Arithmetic ◽  
Zero Level ◽  
One Step ◽  
Definition Of ◽  
M Estimate ◽  
M Estimates

В экономическом прогнозировании коротких временных рядов часто применяется модель Брауна нулевого порядка. К одной из проблем использования этой модели на первых шагах прогнозирования относится оценка начального значения экспоненциальной средней. Как правило, в качестве такой оценки используется простое среднее арифметическое значение первых уровней ряда, которое является неустойчивой статистической оценкой. Поэтому в данном исследовании предложено для оценки начального значения экспоненциальной средней использовать робастные М-оценки Тьюки, Хампеля, Хьюбера и Эндрюса. Цель исследования заключается в определении целесообразности применения М-оценок для определения начального значения экспоненциальной средней в модели Брауна при прогнозировании коротких временных рядов экономических показателей. В результате проведенного экспериментального исследования установлено: а) к наиболее значимым факторам, влияющим на точность прогноза с использованием модели Брауна, относятся вид временного ряда, значение постоянной сглаживания, отбраковка аномальных уровней и вид весов; б) вид оценки начального значения экспоненциальной средней и число итераций при вычислении М-оценки являются менее значимыми факторами (в связи с этим обоснована целесообразность применения одношаговых М-оценок); в) на начальных шагах прогнозирования при ограниченном количестве уровней временного ряда, когда невозможно достоверно определить вид ряда и когда отсутствуют основания для отбраковки аномальных уровней, предпочтительнее использовать модель Брауна с весами Вейда и определять начальное значение экспоненциальной средней на основе одношаговых робастных М-оценок (в остальных случаях целесообразно применять простое среднее арифметическое значение). In economic forecasting of short-term time series Braun’s model of zero level is often applied. One of issues of usage of this model from the very beginning of forecasting is estimation of start value of exponential average. As usual, simple arithmetic mean of first levels of series, used as such estimate, is volatile statistical estimate. That’s why in this investigation it’s suggested to use Tukey’s, Hampel’s, Huber’s and Andrews’ robust M-estimates for estimation of start value of exponential average. Purpose of research is definition of reasonability of M-estimates application to define start value of exponential average in Braun’s model during forecasting of short-term time series of economic indicators. The results of conducted experimental research are as follows: a) the most important factors, that have significant impact on forecast accuracy with usage of Braun’s model, are type of time series, value of smoothing constant, removal of abnormal levels and type of weights; b) type of estimate of start value of exponential average and quantity of iterations in process of calculation of M-estimate are less significant factors; c) consequently, reasonability of usage of one-step M-estimates is justified; d) on the first steps of forecasting with limited quantity of levels of time series, when it’s impossible to define with certainty type of series and when there is no reasons for removal of abnormal levels, it’s preferable to use Braun’s model with Wade’s weights and define start value of exponential average based on one-step robust M-estimates (in other cases it’s better to use simple arithmetical mean).

scholarly journals Pengaruh tingkat inflasi terhadap nilai tukar rupiah (Studi pada Bank Indonesia periode tahun 2011-2015)

2017 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Yati Wijayanti Sudarmiani
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Exchange Rate ◽  
Inflation Rate ◽  
Time Series Data ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Simple Linear Regression

<p><em>This study aimed to analyze the influence of the inflation rate of the Rupiah. Population and samples used in this study are all monthly time series data rate of inflation and the Rupiah during the period January 2011-December 2015 as many as 60. The data used are secondary data obtained from the official website of Bank Indonesia<a href="http://www.bi.co.id/"> (www.bi.co.id).</a> The analytical method used in this study is a simple linear regression analysis. The result of the coefficient of determination (r2) which shows that the percentage of the effect of the inflation rate to changes in the rupiah exchange rate of 7,9%. From the calculations, the equation Y = 3.941 + 0,073X , it can be concluded that the level of inflation is positive and significant effect on the rupiah.</em></p>

FORECASTING MEXICAN PESO - US DOLLAR EXCHANGE RATE AFTER THE 2020 PANDEMIC WITH GARCH AND XGBOOST TIME SERIES MODELS

2020 ◽  
Vol 65 (1) ◽  
pp. 89-106
Author(s):  
Alejandro Ruiz-Olivares ◽  
Martha Elva Ramírez-Guzmán ◽  
Sandy Yaredd Trujano-Ramos
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Models ◽  
Dollar Exchange Rate ◽  
Us Dollar ◽  
Mexican Peso

scholarly journals Faktor-Faktor yang Mempengaruhi Ekspor Kopi di Aceh

2020 ◽  
Vol 5 (2) ◽  
pp. 192-202
Author(s):  
Mardhiah Mardhiah ◽  
Akhmad Baihaqi ◽  
Safrida Safrida
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Secondary Data ◽  
Coefficient Of Determination ◽  
Analysis Model ◽  
Coffee Production ◽  
Data Source ◽  
Independent Variable ◽  
The Exchange Rate ◽  
Coffee Price

 Penelitian ini bertujuan untuk melihat faktor-faktor yang mempengaruhi ekspor kopi di Aceh. Sumber data yang digunakan adalah data sekunder yang berupa time series dari tahun 2001 – 2017. Model analisis yang digunakan adalah regresi linear berganda, uji F, uji t dan uji R2. Hasil analisis regresi diperoleh Y = -9,365 - 2,825NT + 2,616HKDN – 1,734HKLN + 1,077PK. Hasil uji-F variabel dependen dengan variabel independen diperoleh nilai Fcari=3,605 sedangkan Ftabel=3,41. Hasil Uji-t menunjukkan nilai tukar mata uang Dollar terhadap Rupiah tcari=2,622 sedangkan ttabel= 2,160 dimana Ha ditolak H0 diterima artinya nilai tukar mata uang Dollar terhadap Rupiah berpengaruh nyata terhadap volume ekspor kopi di Aceh. Hasil analisis terhadap harga kopi dalam negeri tcari=2,348 sedangkan ttabel=2,160 artinya harga kopi dalam negeri berpengaruh secara nyata terhadap volume ekspor kopi di Aceh. Hasil analisis terhadap harga kopi luar negeri tcari=-3,543 sedangkan ttabel=2,160 artinya harga kopi di luar negeri berpengaruh secara nyata terhadap volume ekspor kopi di Aceh. Hasil analisis terhadap produksi kopi tcari=1,313 sedangkan ttabel=2,160 dimana Ha diterima dan H0 ditolak artinya produksi kopi tidak berpengaruh secara nyata terhadap volume ekspor kopi di Aceh. Nilai koefisien determinasi (R2) menunjukkan bahwa 54,6% ekspor kopi di Aceh dipengaruhi oleh nilai tukar, harga kopi dalam negeri, harga kopi luar negeri dan produksi kopi sedangkan sisanya sebesar 45,4% dipengaruhi faktor-faktor lain.Kata kunci : Ekspor Kopi, Nilai Tukar, Harga Kopi Dalam Negeri, Harga Kopi Luar Negeri, dan Produksi Kopi Abstract. This study aims to look at the factors that influence coffee exports in Aceh. The data source used is secondary data in the form of time series from 2001 - 2017. The analysis model used is multiple linear regression, Ftest, ttest, and R2 test. Regression analysis results obtained Y = -9,365 - 2,825NT + 2,616HKDN – 1,734HKLN + 1,077PK. F-test results for the dependent variable with the independent variable obtained Ftest = 3.605 while Ftable = 3.41. The ttest results show the exchange rate of the Dollar against Rupiah ttest = 2.622 while ttable = 2.160 where Ha is rejected and H0 is accepted meaning the exchange rate of the Dollar against Rupiah has a significant effect on the volume of coffee exports in Aceh. The results of an analysis of the domestic coffee price ttest= 2,348 while ttable = 2,160 means that the domestic coffee price significantly affects the volume of coffee exports in Aceh. The results of an analysis of overseas coffee prices ttest = -3.543 while ttable = 2.160 means that the price of coffee abroad has a significant effect on the volume of coffee exports in Aceh. The results of the analysis of coffee production ttest = 1,313 while ttable = 2.160 where Ha is accepted and H0 is rejected, meaning that coffee production has no significant effect on the volume of coffee exports in Aceh. The coefficient of determination (R2) shows that 54.6% of coffee exports in Aceh are influenced by the exchange rate, domestic coffee prices, foreign coffee prices and coffee production while the remaining 45.4% is influenced by other factors.

Time Series Features Extraction and Forecast from Multi-feature Stocks with Hybrid Deep Neural Networks.

2020 ◽  
Vol 13 ◽  
Author(s):  
Ye Xu ◽  
Xun Yuan
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Hybrid Model ◽  
Deep Neural Networks ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Closing Price ◽  
Massive Information ◽  
Better Than

Background: Forecasting of time series stock data is important in financial related works. Stock data usually have multifeatures such as opening price, closing price and so on. The traditional forecast methods, however, is mainly applied to one feature – closing price, or a few, like four or five features. The massive information hidden in the multi-feature data is not thoroughly discovered and used. Objective: Find a method to make used of all information of multi-features and get a forecast model. Method: LSTM based models are introduced in this paper. For comparison, three models are used and they are single LSTM model, hybrid model of LSTM-CNN, and traditional ARIMA model. Results: Experiments with different models are performed on stock data with 50 and 230 features, respectively. Results show that MSE of single LSTM model is 2.4% lower than ARIMA model and MSE of LSTM-CNN model is 12.57% lower than that of single LSTM model on 50 features data. On 230 features data, LSTM-CNN model is found to be improved by 23.41% in forecast accuracy. Conclusion: In this paper, we use three different models – ARIMA, single LSTM and LSTM-CNN hybrid model – to forecast rise and fall of multi-features stock data. It’s found that single LSTM model is better than traditional ARIMA model on the average, and LSTM-CNN hybrid model is better than single LSTM model on 50-feature stock data. What’s more, we use LSTM-CNN model to perform experiments on stock data with 50 and 230 features, respectively. And is found that results of the same model on 230 features data is better than that on 50 features data. It’s proved in our work that the LSTM-CNN hybrid model is better than other models and experiments on stock data with more features could result in better outcomes. We’ll do more works on hybrid models next.

scholarly journals COMPARING THE PREDICTION ACCURACY OF LSTM AND ARIMA MODELS FOR TIME-SERIES WITH PERMANENT FLUCTUATION

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Ghahreman Abdoli ◽  
Mohsen MehrAra ◽  
Mohammad Ebrahim Ardalani
Keyword(s):  
Time Series ◽  
Economic System ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Economic Time Series ◽  
Intraday Data

In developing countries with an unstable economic system, permanent fluctuation in historical data is always a concern. Recognizing dependency and independency of variables are vague and proceeding a reliable forecast model is more complex than other countries. Although linearization of nonlinear multivariate economic time-series to predict, may give a result, the nature of data which shows irregularities in the economic system, should be ignored. New approaches of artificial neural network (ANN) help to make a prediction model with keeping data attributes. In this paper, we used the Tehran Stock Exchange (TSE) intraday data in 10 years to forecast the next 2 months. Long Short-Term Memory (LSTM) from ANN chooses and outputs compared with the autoregressive integrated moving average (ARIMA) model. The results show, although, in long term prediction, the forecast accuracy of both models reduce, LSTM outperforms ARIMA, in terms of error of accuracy, significantly.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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