scholarly journals TESTING OF THE BLACK SCHOLES AND GARCH MODELS IN LQ45 USING LONG STRADDLE STRATEGY IN 2009-2018

2021 ◽  
Vol 22 (1) ◽  
pp. 30-39
Author(s):  
Riko Hendrawan ◽  
Anggadi Sasmito
Keyword(s):  
Time Series Data ◽  
Mean Squared Error ◽  
Series Data ◽  
Call Option ◽  
Due Date ◽  
Average Percentage ◽  
Put Option ◽  
Option Contract ◽  
Black Scholes ◽  
Better Than

The purpose of this study is to examine the implementation of option contracts using Black Scholes and GARCH on the LQ45 index using the long straddle strategy. This study uses time-series data as a time frame for conducting research, using a sample of closing price data for the LQ 45 daily index for 2009-2018. For the test the model, we used the secondary data of the closing stock price index from February 28, 2009 to March 31, 2019The results of this study are seen by comparing the average percentage value of Average Mean Squared Error (AMSE) of Black Scholes and GARCH with the application of a long straddle strategy, where the smaller the percentage value, the better the model will be. Within one month of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 2.77% and the put option of 1.56%. Within two months of option contract due date, GARCH is better than Black Scholes, with an error value on the call option of 8.12% and the put option of 4.00%. Within three months of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 12.38% and on the put option of 5.50%. The long straddle strategy in the LQ45 index only reached a maximum of 60% of possible profits, with an average of around 30% possible profits.

scholarly journals Can The IDX Be Hegded ? : Comparison of Black Scholes Option Model And Garch Option Model Using Long Strangle Strategy

2020 ◽  
Vol 20 (3) ◽  
pp. 252
Author(s):  
Riko Hendrawan ◽  
Gede Teguh Laksana ◽  
Wiwin Aminah
Keyword(s):  
Arima Model ◽  
Garch Model ◽  
Call Option ◽  
Average Percentage ◽  
Put Option ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Option Model ◽  
The One ◽  
The Garch Model

The purpose of this research was to compare the accuracy of the Black Scholes option model and the GARCH option model on index options using IDX Composite (IHSG) data from 2009-2018 with the long strangle strategy. The Black Scholes volatility constructed by using historical volatility, while GARCH volatility constructed by using the ARIMA model and the best lag. The accuracy of options analyzed using the average percentage mean square error (AMSE) to find the best model. The results of this study showed that for the one month option, the GARCH model is more accurate for a call option with 0.26%, while the Black Scholes model is more accurate for a put option with 0.18%. For the two month option, the GARCH model is more accurate for a call option with 0.92%, while the Black Scholes model is more accurate for a put option with 0.26%. For the three month option, the Black Scholes model is more accurate for a call option and put option with 2.00% and 0.31%, respectively. The results of this study further sharpen the research conducted by Bhat and Arekar (2016)and Hendrawan(2010) Keywords : Black Scholes Options Model; GARCH Option Model; Long Strangle; ,Index Option.,

scholarly journals Is Islamic Bank More Stable Than Conventional Bank? Evidence From Islamic Rural Banks in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 294
Author(s):  
Agus Widarjono ◽  
M. B. Hendrie Anto ◽  
Faaza Fakhrunnas
Keyword(s):  
Time Series ◽  
Financial Stability ◽  
Time Series Data ◽  
Series Data ◽  
Z Score ◽  
Profit Rate ◽  
Return On Assets ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Better Than

This study investigates whether Islamic rural banks perform better than conventional rural banks as their competitor in Indonesia. To measure Islamic rural banks' financial performance, we apply financial stability using Z-score and profitability using the return on assets. We use monthly time series data from January 2009 to December 2018. The dynamic regression of the Autoregressive Distributed Lag (ARDL) model is then employed. The results report that the Z-Score of Islamic rural banks is higher than the Z-Score of conventional rural banks. This finding shows that Islamic rural banks are less risky than conventional rural banks. However, the Islamic rural banks' financial stability is very vulnerable to changes in equity, output, and inflation than conventional rural banks. Although the Islamic rural banks' profit rate is lower compared to conventional rural banks, it is considered more stable. The profit of Islamic rural banks is affected by size, equity, domestic output, and inflation.

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

The Comparison of GARCH and ANN Model for Forecasting Volatility: Evidence based on Indian Stock Markets

2020 ◽  
Vol 14 (2) ◽  
pp. 103-121
Author(s):  
MUNEER SHAIK ◽  
Aditya Sejpal
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Absolute Error ◽  
Series Data ◽  
Ann Model ◽  
Squared Error ◽  
Out Of Sample ◽  
Indian Stock Market ◽  
Forecasting Volatility ◽  
Better Than

In this paper, we study the performance of the Artificial Neural Networks (ANNs) and GARCH modelsto predict the volatility of the Indian stock market indices namely, NIFTY 50, NIFTY Bank and NIFTYFMCG. We have used the GARCH (1,1) and Recurrent Neural Network, a type of neural network whichis widely used for predicting time series data. The purpose of the study is to investigate if the ArtificialNeural Networks perform better than the traditional GARCH (1,1) model. An out of sample testingmethodology is applied to the most recent 20 percent of the observations for all the three indices. Wehave used Root Means Squared Error (RMSE) and Mean Absolute Error (MAE) as metrics to evaluatethe volatility predicting performances of the models. The results show no clear evidence of ANN modelperforming better than GARCH model for any of the three indices. ANNs may prove to be betterindicators in periods with low volatility while its performance deteriorated in periods with highvolatility.

PENENTUAN HARGA OPSI DENGAN MODEL BLACK-SCHOLES MENGGUNALKAN METODE BEDA HINGGA FORWARD TIME CENTRAL SPACE

2018 ◽  
Vol 1 (1) ◽  
pp. 45
Author(s):  
Werry Febrianti
Keyword(s):  
Finite Difference ◽  
Finite Difference Method ◽  
Difference Method ◽  
Option Price ◽  
Call Option ◽  
Put Option ◽  
Daily Data ◽  
Black Scholes ◽  
One Year ◽  
The Right

Option can be defined as a contract between two sides/parties said party one and party two. Party one has the right to buy or sell of stock to party two. Party two can invest by observe the put option price or call option price on a time period in the option contract. Black-Scholes option solution using finite difference method based on forward time central space (FTCS) can be used as the reference for party two in the investment determining. Option price determining by using Black-Scholes was applied on Samsung stock (SSNLF) by using finite difference method FTCS. Daily data of Samsung stock in one year was processed to obtain the volatility of the stock. Then, the call option and put option are calculated by using FTCS method after discretization on the Black-Scholes model. The value of call option was obtained as $1.457695030014260 and the put option value was obtained as $1.476925604670225.

scholarly journals A Soft Computing Model to Predict the Rice Production in India

2019 ◽  
Vol 8 (6) ◽  
pp. 796-800
Keyword(s):  
Predictive Model ◽  
Soft Computing ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Rice Production ◽  
Series Data ◽  
Average Error ◽  
Optimum Result ◽  
Future Data ◽  
Artificial Neural Network Ann

India, which has the most rice tillage area in the world, is one of the massive cultivators of this white crop. Besides, rice is the main staple food of many Indians. The main purpose of this study is to develop a predictive model on Indian rice production. In this, we have used different types of soft computing models like Fuzzy Logic, Statistical Equations, Artificial Neural Network (ANN) and Genetic Algorithm (GA) and developed a hybrid model to get the optimum result. The vital aspect of this predictive model is the accuracy of the future data prediction on the basis of past time series data. The Prediction performance has been assessed by using error finding equations like Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Average Error.

scholarly journals A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

2021 ◽  
Vol 7 ◽  
pp. e534
Author(s):  
Kristoko Dwi Hartomo ◽  
Yessica Nataliani
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Forecasting ◽  
Series Data ◽  
Improvement Rate ◽  
New Model ◽  
Average Improvement ◽  
Proposed Model

This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

scholarly journals Hybrid Models for Adaptive Allocation of Electricity for Households

2019 ◽  
Vol 9 (2S) ◽  
pp. 369-376
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Active Power ◽  
Sensor Data ◽  
Series Data ◽  
Adaptive Allocation ◽  
Forecasting Methods ◽  
Better Than

In this paper, we analyze, model, predict and cluster Global Active Power, i.e., a time series data obtained at one minute intervals from electricity sensors of a household. We analyze changes in seasonality and trends to model the data. We then compare various forecasting methods such as SARIMA and LSTM to forecast sensor data for the household and combine them to achieve a hybrid model that captures nonlinear variations better than either SARIMA or LSTM used in isolation. Finally, we cluster slices of time series data effectively using a novel clustering algorithm that is a combination of density-based and centroid-based approaches, to discover relevant subtle clusters from sensor data. Our experiments have yielded meaningful insights from the data at both a micro, day-to-day granularity, as well as a macro, weekly to monthly granularity.

scholarly journals FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 90 ◽  
Author(s):  
Ana Pano-Azucena ◽  
Esteban Tlelo-Cuautle ◽  
Sheldon Tan ◽  
Brisbane Ovilla-Martinez ◽  
Luis de la Fraga
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Series Data ◽  
Support Vector ◽  
Maximum Lyapunov Exponent ◽  
Inference System

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

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