scholarly journals Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk

2020 ◽  
Vol 12 (21) ◽  
pp. 8849
Author(s):  
Zhouwei Wang ◽  
Qicheng Zhao ◽  
Min Zhu ◽  
Tao Pang
Keyword(s):  
Quantile Regression ◽  
Stock Prices ◽  
Value At Risk ◽  
Stock Price ◽  
Garch Model ◽  
Jump Diffusion ◽  
Support Vector ◽  
Garch Models ◽  
Bank Stock ◽  
Better Than

Extreme financial events usually lead to sharp jumps in stock prices and volatilities. In addition, jump clustering and stock price correlations contribute to the risk amplification acceleration mechanism during the crisis. In this paper, four Jump-GARCH models are used to forecast the jump diffusion volatility, which is used as the risk factor. The linear and asymmetric nonlinear effects are considered, and the value at risk of banks is estimated by support vector quantile regression. There are three main findings. First, in terms of the volatility process of bank stock price, the Jump Diffusion GARCH model is better than the Continuous Diffusion GARCH model, and the discrete jump volatility is significant. Secondly, due to the difference of the sensitivity of abnormal information shock, the jump behavior of bank stock price is heterogeneous. Moreover, CJ-GARCH models are suitable for most banks, while ARJI-R2-GARCH models are more suitable for small and medium sized banks. Thirdly, based on the jump diffusion volatility information, the performance of the support vector quantile regression is better than that of the parametric quantile regression and nonparametric quantile regression.

scholarly journals Penerapan Model ARIMA-GARCH Untuk Memprediksi Harga Saham Bank BRI

Jurnal MIPA ◽  
2017 ◽  
Vol 6 (2) ◽  
pp. 92
Author(s):  
Natasya Bella Yolanda ◽  
Nelson Nainggolan ◽  
Hanny A.H. Komalig
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Garch Model ◽  
Time Series Model ◽  
Garch Models ◽  
Model Time Series ◽  
Model Determination ◽  
Research Show

Model time series yang dapat mengakomodasi sifat heteroskedastik adalah model ARCH atau GARCH. Penelitian ini bertujuan untuk menerapkan model ARIMA-GARCH dalam memprediksi harga saham bank BRI. Hasil penelitian menunjukkan bahwa pada harga saham bank BRI terdapat unsur heteroskedastik. Model terbaik yang didapat pada harga saham bank BRI yaitu ARIMA(2,1,1)-GARCH(2,2). Model tersebut memiliki nilai koefisien determinasi atau  (R-squared) yaitu sebesar 0.99916 atau 99,91%Time series model which can accommodate heteroscedasticity is the ARCH or GARCH model. This study aims to apply and determine the ARIMA-GARCH models in predicting stock prices of bank BRI. The result of this research show that in bank BRI stock price there is heteroscedasticity element. The best model obtained in bank BRI stock price that is ARIMA (2,1,1)-GARCH (2,2). The model determination or (R-squared) 0.99916 or 99.91%

scholarly journals IntelliFin: Advanced Stock Prediction using Hybrid ML and LSTM Model with Financial Indicators powered by Sentiment Determination using NLP

2020 ◽  
Vol 9 (5) ◽  
pp. 428-433
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Majority Voting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Financial History ◽  
The Right

Stock Trading has been one of the most important parts of the financial world for decades. People investing in the share market analyze the financial history of a corporation, the news related to it and study huge amounts of data so as to predict its stock price trend. The right investment i.e. buying and selling a company stock at the right time leads to monetary benefits and can make one a millionaire overnight. The stock market is an extremely fluctuating platform wherein data is produced in humongous quantities and is influenced by numerous disparate factors such as socio-political issues, financial activities like splits and dividends, news as well as rumors. This work proposes a novel system “IntelliFin” to predict the share market trend. The system uses the various stock market technical indicators along with the company's historical market data trends to predict the share prices. The system employs the sentiment determination of a company's financial and socio-political news for a more accurate prediction. This system is implemented using two models. The first is a hybrid LSTM model optimized by an ADAM optimizer. The other is a hybrid ML model which integrates a Support Vector Regressor, K-Nearest Neighbor classifier, an RF classifier and a Linear Regressor using a Majority Voting algorithm. Both models employ a sentiment analyzer to account for the news impacting the stock prices which is powered by NLP. The models are trained continuously using Reinforcement Learning implemented by the Q-Learning Algorithm to increase the consistency and accuracy. The project aims to support the inexperienced investors, who don't have enough experience in investing in the stock market and help them maximize their profit and minimize or eliminate the losses. The developed system will also serve as a tool for professional investors to help and aid their decision making.

scholarly journals Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 10
Author(s):  
Ravi Summinga-Sonagadu ◽  
Jason Narsoo
Keyword(s):  
Loss Function ◽  
Value At Risk ◽  
Garch Model ◽  
Innovation Process ◽  
Expected Shortfall ◽  
Error Distribution ◽  
Garch Models ◽  
Intraday Data ◽  
Distributional Assumptions ◽  
T Distribution

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five distributional assumptions for the innovation process are used to analyse their effects on the modelling and forecasting performance. The high-frequency volatility models were validated in terms of in-sample fit based on various statistical and graphical tests. A more rigorous validation procedure involves testing the predictive power of the models. Therefore, three backtesting procedures were used for the VaR, namely, the Kupiec’s test, a duration-based backtest, and an asymmetric VaR loss function. Similarly, three backtests were employed for the ES: a regression-based backtesting procedure, the Exceedance Residual backtest and the V-Tests. The validation results show that non-normal distributions are best suited for both model fitting and forecasting. The MC-GARCH(1,1) model under the Generalised Error Distribution (GED) innovation assumption gave the best fit to the intraday data and gave the best results for the ES forecasts. However, the asymmetric Skewed Student’s-t distribution for the innovation process provided the best results for the VaR forecasts. This paper presents the results of the first empirical study (to the best of the authors’ knowledge) in: (1) forecasting the intraday Expected Shortfall (ES) under different distributional assumptions for the MC-GARCH model; (2) assessing the MC-GARCH model under the Generalised Error Distribution (GED) innovation; (3) evaluating and ranking the VaR predictability of the MC-GARCH models using an asymmetric loss function.

Estimating value at risk with semiparametric support vector quantile regression

2011 ◽  
Vol 27 (4) ◽  
pp. 685-700 ◽  
Author(s):  
Jooyong Shim ◽  
Yongtae Kim ◽  
Jangtaek Lee ◽  
Changha Hwang
Keyword(s):  
At Risk ◽  
Quantile Regression ◽  
Value At Risk ◽  
Support Vector

scholarly journals FUNDAMENTALS OF INTEGRATED COMMERCIAL BANK IN MACROECONOMIC AND SHARIA PERSPECTIVE IN INDONESIA

2018 ◽  
Vol 3 (2) ◽  
pp. 349-387
Author(s):  
Kumara Jati ◽  
Aziza Rahmaniar Salam
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Commercial Bank ◽  
Stock Prices ◽  
Stock Price ◽  
Vector Autoregression ◽  
Share Price ◽  
Fasting Period ◽  
Goods And Services ◽  
Bank Stock

This research analyses the fundamentals of integrated commercial bank in macroeconomic and sharia perspective in Indonesia. Based on the calculation of Vector Autoregression (VAR), the impact of macroeconomic variables (Jakarta Stock Islamic Index / JKSII, Indonesian Stock Price Composite Index / JKSE, Crude Oil Price, and Exchange Rate)  on stock prices of commercial banks vary. These shocks indicate an indirect price transmission through exchange rate channels and economic growth. From the Structrural Time Series Model (STSM), JKSII, JKSE, and commercial bank share price prediction will generally increase at the end of 2017 and 2018. This will generate hope and benefit for policy maker and business actors in the banking, finance and sharia sectors. In general, the ARMA-ARCH/GARCH model with dummy variables found negative impact of “Fasting Period and Eid Al-Fitr” on return of JKSII, JKSE, and commercial bank stock price. This indicates a cycle of stock price decline that occurs when consumers spend more money to purchase goods and services. However, this cycle of stock price declines is only temporary because the recovery of the world economy and the increase in demand for goods and services in the future can be a pull factor for stock prices (demand factor). Policy makers and stakeholders related to the financial system, banking and capital markets, especially the sharia sector need to see the movement of conventional bank stocks and “Fasting Period and Eid Al-Fitr” as they move in the opposite direction for a certain period.   Keywords: Stock Price of Commercial Bank, Macroeconomic and Sharia Perspective, Vector Autoregression (VAR), Structural Time-Series Models (STSM), ARMA-ARCH/GARCH   JEL Classification Codes: F31, F47, G15, G21

scholarly journals Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 578
Author(s):  
Sangyeol Lee ◽  
Chang Kyeom Kim ◽  
Sangjo Lee
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Price ◽  
Real Data ◽  
Support Vector ◽  
Garch Models ◽  
Time Varying ◽  
Tuning Parameters ◽  
Scope Of Application ◽  
Validation Set

This study considers the problem of detecting a change in the conditional variance of time series with time-varying volatilities based on the cumulative sum (CUSUM) of squares test using the residuals from support vector regression (SVR)-generalized autoregressive conditional heteroscedastic (GARCH) models. To compute the residuals, we first fit SVR-GARCH models with different tuning parameters utilizing a time series of training set. We then obtain the best SVR-GARCH model with the optimal tuning parameters via a time series of the validation set. Subsequently, based on the selected model, we obtain the residuals, as well as the estimates of the conditional volatility and employ these to construct the residual CUSUM of squares test. We conduct Monte Carlo simulation experiments to illustrate its validity with various linear and nonlinear GARCH models. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and Korean won/U.S. dollar (KRW/USD) exchange rate datasets is provided to exhibit its scope of application.

Application of Machine Learning Algorithms in Stock Market Prediction

2020 ◽  
pp. 153-180
Author(s):  
Sumit Kumar ◽  
Sanlap Acharya
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

scholarly journals ANALYSIS OF MACHINE LEARNING METHODS FOR PREDICTIONS OF STOCK EXCHANGE SHARE PRICES

2021 ◽  
pp. 94-100
Author(s):  
V. Serbin ◽  
U. Zhenisserov
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Price Patterns

Since the stock market is one of the most important areas for investors, stock market price trend prediction is still a hot subject for researchers in both financial and technical fields. Lately, a lot of work has been analyzed and done in the field of machine learning algorithms for analyzing price patterns and predicting stock prices and index changes. Currently, machine-learning methods are receiving a lot of attention for predicting prices in financial markets. The main goal of current research is to improve and develop a system for predicting future prices in financial markets with higher accuracy using machine-learning methods. Precise predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and machine learning, forecasting methods have become more effective at predicting stock prices. In this article, we looked at the machine learning techniques that have been used to trade stocks to predict price changes before an actual rise or fall in the stock price occurs. In particular, the article discusses in detail the use of support vector machines, linear regression, and prediction using decision stumps, classification using the nearest neighbor algorithm, and the advantages and disadvantages of each method. The paper introduces parameters and variables that can be used to recognize stock price patterns that might be useful in future stock forecasting, and how the boost can be combined with other learning algorithms to improve the accuracy of such forecasting systems.

scholarly journals Stock price volatility during the COVID-19 pandemic: The GARCH model

2021 ◽  
Vol 18 (4) ◽  
pp. 12-20
Author(s):  
Endri Endri ◽  
Widya Aipama ◽  
A. Razak ◽  
Laynita Sari ◽  
Renil Septiano
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Practical Implication ◽  
Abnormal Returns ◽  
Garch Model ◽  
Price Volatility ◽  
Efficient Market Hypothesis ◽  
Empirical Literature ◽  
Stock Price Volatility ◽  
The Garch Model

This study examined the response of stock prices on the Indonesia Stock Exchange (IDX) to COVID-19 using an event study approach and the GARCH model. The research sample is the closing price of the Composite Stock Price Index (JCI) and companies that are members of LQ-45 in the 40-day period before the COVID-19 incident, 1 day during the COVID-19 incident (March 2, 2020) and 10 days after, January 6, 2020 – March 16, 2020. Empirical findings prove that abnormal returns react negatively to COVID-19, JCI volatility fluctuates widely during the COVID-19 event, and the GARCH(1,2) model can be used to assess volatility and predict stock abnormal returns in IDX in market conditions infected with COVID-19. The practical implication of the study’s findings for investors is that the COVID-19 event caused stock price volatility, which affects abnormal returns. Therefore, to face the conditions of uncertainty and increased volatility in the future, several lines of risk management are needed in managing a stock portfolio. In addition, it also opens up opportunities for speculators to profit in an inefficient market environment. This study is based on the empirical literature currently being developed to investigate the phenomenon of stock price volatility behavior during COVID-19 on the IDX. The GARCH model used proves that during the COVID-19 pandemic, stock price volatility increases and leads to a decrease in abnormal returns. The empirical findings also validate the efficient market hypothesis theory related to the study of events and the theory of financial behavior related to uncertainty.

scholarly journals Stock Market Prices Prediction using Random Forest and Extra Tree Regression

2019 ◽  
Vol 8 (3) ◽  
pp. 1224-1228
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Area ◽  
Support Vector ◽  
Market Prices ◽  
Machine Learning Approach ◽  
Innovative Work

Prediction of Stock price is now a day’s an existing and interesting research area in financial and academic sectors to know the scale of economies. There did not exists any significant set of rules to estimate and predict the scale of share in the stock exchange. Many evolutionary technologies are existing such as technical, fundamental, time, statistical and series analysis which help us to attempt the prediction process, but none of the methods are proved as reliable and accurate tool to the society in the estimation of stock exchange or share market scales. Here in this paper we attempted to do innovative work through Machine Learning approach to predict or sense the behaviour tracking of the stock market sensex. Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and define the activity between the exchanges the securities between the buyers and sellers. We predicted the price of the stock based on the closing value and stock price. An algorithm with high accuracy we do the process of comparison for the accuracy of each of the model and finally is considered as better algorithm for predicting stock price. As share market is a vague domain we cannot predict the conditions occur, and also share market can never be predicted, this job can be done easily and technically through this work and the main aim of this paper is to apply algorithms in Machine Learning in predicting the stock prices.

Sign in / Sign up

Export Citation Format

Share Document