scholarly journals Modelling Conditional Volatility of NIFTY 50

2021 ◽  
Author(s):  
VARSHA SHRIRAM NERLEKAR ◽  
Shriram Nerlekar
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Conditional Volatility ◽  
Stock Market Index ◽  
R Software ◽  
Market Index ◽  
Estimated Parameters ◽  
Indian Stock Market ◽  
Daily Returns

Abstract The present study demonstrates modelling of conditional volatility of NIFTY 50 using GARCH (1,1) model. The daily returns data of the Indian stock market index NIFTY 50 is used for the period ranging from April 2010- March 2020. The data is analysed using R software. The study estimates and interprets the results arrived in the summary output of the R environment and demonstrates how to forecast the volatility of the returns based on the estimated parameters. Extracting the time series of conditional volatilities is also demonstrated in the study.

scholarly journals Asymmetric Volatility in the Nepalese Stock Market

2021 ◽  
Vol 22 (1) ◽  
pp. 41-59
Author(s):  
Dinesh Gajurel
Keyword(s):  
Stock Market ◽  
Spillover Effect ◽  
Equity Markets ◽  
Spillover Effects ◽  
Stock Market Index ◽  
International Equity Markets ◽  
Asymmetric Volatility ◽  
Market Index ◽  
International Equity ◽  
Indian Stock Market

This paper investigates the asymmetric volatility behavior of the Nepalese stock market including spillover effects from the US and Indian equity markets. I modeled asymmetric volatility within a generalized autoregressive conditional heteroskdasticy framework using comprehensive data for the Nepal stock market index. The results reveal a very different asymmetry compared to the results in other international equity markets: positive shocks increase volatility by more than negative shocks. The results further suggest that uninformed investors play a significant role in the Nepalese stock market. The spillover effect from the Indian stock market to the Nepalese stock market is negative. Overall, I conclude that a “fear of missing out” (FOMO) of noise traders as well as the deployment of pump and dump schemes are inherent features of the Nepalese stock market. The findings are very useful to policy makers and investors alike.

scholarly journals A Study on Affiliation between Indian Stock Market and Macro Economic Variables

2018 ◽  
Vol 5 (01) ◽  
Author(s):  
Pooja Chaturvedi Sharma
Keyword(s):  
Stock Market ◽  
Inflation Rate ◽  
Stock Market Volatility ◽  
Stock Market Index ◽  
Complex Interplay ◽  
Monthly Data ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Causality Tests ◽  
Indian Stock Market

Stock market volatility is a result of complex interplay of a host of factors. Hence, it is difficult to make a correct assessment of its movement. Macroeconomic variables have are very much influential in context of the volatility of stock market. This study inspects the association amongst stock market index and selected macroeconomic variables. For the analysis unit root, co-integration, Granger causality tests and Johansen co-integration tests were performed. Outcomes of the study showed that all the variables namely money supply, exchange rate and inflation rate are positively correlated with the stock market index except gold prices. Co-integration existed between the stock market index and macroeconomic variables. The study uses monthly data of past ten years (i.e. from April 2008 to March 2018).

scholarly journals Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory

2021 ◽  
Vol 5 (3) ◽  
pp. 456-465
Author(s):  
Harya Widiputra ◽  
Adele Mailangkay ◽  
Elliana Gautama
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Stock Market Index ◽  
Short Term ◽  
Term Memory ◽  
Market Index ◽  
Long Short Term Memory

The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.  

Effects of Intraday Patterns on a Time Series Analysis of the Korean Stock Market Index

2011 ◽  
Vol 58 (2) ◽  
pp. 396-399 ◽  
Author(s):  
Doo Hwan Kim ◽  
Seong Eun Maeng ◽  
Yu Sik Bang ◽  
Hyung Wooc Choi ◽  
Moon-Yong Cha ◽  
...  
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Stock Market Index ◽  
Series Analysis ◽  
Market Index ◽  
Korean Stock Market ◽  
Intraday Patterns
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