scholarly journals Impact of Global Oil Shocks on the Russian Stock Market

2021 ◽  
Vol 92 ◽  
pp. 07037
Author(s):  
Igor Lukasevich ◽  
Ludmila Chikileva
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Value Added ◽  
Stock Index ◽  
Series Data ◽  
Var Model ◽  
Oil Shocks ◽  
Russian Market ◽  
Russian Stock Market ◽  
The Impact

Research background: The study focuses on modeling assessment of oil shocks impact on the Russian stock market. Purpose of the article: The purpose of the study is to determine the impact of oil prices abrupt changes on the Russian stock market, its quantitative and temporal specifications. The study consists of two interrelated sections. The first section includes the results of statistical processing of initial data, calculation of their key characteristics and preliminary analysis. The second section of the study is devoted to modeling the assessment of the impact of oil shocks on the behavior of the Russian market RTS stock index. Methods: Based on an extensive sample of daily price values for Brent North sea oil and the Russian stock index RTS for the period from 1997 to May 2020, the study was conducted using models vector auto regression (VAR-model). Findings &Value added: The VAR model was developed and tested to assess the impact of oil shocks on the Russian stock market. Unlike the results of other studies, it is shown that the Brent oil price variance explains only about 10% of the RTS index yield variance in long-term time intervals. The low correlation of time series data and time limit of the impact of oil shocks on the Russian market have been revealed. According to the results of the study, the market recovery takes about 2 months, then the stock index returns to the ‘historical’ range of average ± standard deviation.

The impact of oil shocks on the Russian stock market: an empirical study

2020 ◽  
pp. 121-133
Author(s):  
I.Ya. Lukasevich
Keyword(s):  
Stock Market ◽  
Stock Index ◽  
Var Model ◽  
Oil Shocks ◽  
Sharp Drop ◽  
Macroeconomic Shocks ◽  
Oil Shock ◽  
Theoretical Generalization ◽  
Russian Stock Market ◽  
The Impact

The paper is devoted to the study of approaches to assessing the impact of external macroeconomic shocks on the Russian stock market. A sharp drop in oil prices (oil shock) is considered as a macroeconomic shock. The analysis of possible approaches to solving this problem is carried out, and their theoretical generalization is given. Based on an extensive sample of daily values of prices for Brent oil and the Russian stock index RTS for the period from 1998 to the first half of 2020, a study was conducted on the dependence of the domestic stock market on the global oil market. An approach to assessing the impact of oil shocks based on the use of vector autoregression models (VAR-model) is proposed. The VAR model has been developed and tested for the Russian stock market, its capabilities and limitations have been shown, and practical recommendations for its further development have been provided.

scholarly journals Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries

2019 ◽  
Vol 9 (2) ◽  
pp. 1402-1417
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Good News ◽  
Series Data ◽  
Stock Market Index ◽  
Causality Test ◽  
Lm Test ◽  
The Impact

In general, stock market indices are widely interrelated to the other global markets to detect the impact of diversification opportunities. The present research paper empirically examines randomness and long term equilibrium affiliation amongst the emerging stock market of India and Mexico, Indonesia, South Korea and Turkey from the monthly time series data during February 2008 to October 2019. The researcher employs by the way, Run test, Pearson’s correlation test, Johnsen’s multivariate cointegration test, VECM and Granger causality test with reference to post-September 2008 Global financial crisis. The test results of the above finds that Nifty 50 and BSE Sensex is significantly cointegrated either among themselves or with MIST countries particularly during the post-September Global financial crisis. No random walk is found during the study period. The ADF (Augmented DickeyFuller) and PP (Phillips Pearson) tests evidenced stationarity at the level, but converted into non-stationarity in first difference. Symmetric and asymmetric volatility behaviors are studied using GARCH, EGARCH and TARCH models in order to test which model has the best forecasting ability. Leverage effect was apparent during the study period. So the influx of bad news has a bigger shock or blow on the conditional variance than the influx of good news. The residual diagnostic test (Correlogram-Squared residuals test, ARCH LM test and Jarque-Bera test) confirms GARCH (1,1) as the best suited model for estimating volatility andforecasting stock market index.

scholarly journals Potential Tax Reforms and Kuwait Economic Growth

2019 ◽  
pp. 42-48
Author(s):  
K. Lawler ◽  
F. Ali Al-Sayegh
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Value Added ◽  
Least Square ◽  
Series Data ◽  
Tax Reforms ◽  
Tax Revenues ◽  
Oil Revenue ◽  
The Impact

The objective of this study is to identify whether tax reforms are viable in Kuwait in order to create more government income from sources other than oil. The study examines the relationship between the changes in tax revenues, changes in oil revenue and changes in GDP in Kuwait using time series data from 1998 to 2015. The Augmented Dickey-Fuller (ADF) is used to check for the existence of a unit root. The cointegration test is applied to test for long term relationships between variables using the General Least Square (GLS) method of estimation. The results of the tests find that the impact of changes in tax revenues on changes in the GDP of Kuwait is insignificant. Therefore, Kuwait’s government could rationally implement tax reforms to have incremental sources of income other than oil revenue. Moreover, it is argued that the government might consider implementing broad based consumption taxes and value added taxes into the tax structure Kuwait, and to invest the revenues from those taxes in productive policies, to induce long term economic growth.

scholarly journals The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 299
Author(s):  
Jianguo Zheng ◽  
Yilin Wang ◽  
Shihan Li ◽  
Hancong Chen
Keyword(s):  
Stock Market ◽  
Prediction Models ◽  
Time Series Data ◽  
Bat Algorithm ◽  
Stock Index ◽  
Stationary Time Series ◽  
Series Data ◽  
Polynomial Kernel ◽  
Chinese Stock Market ◽  
Ann Model

Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

scholarly journals Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammadreza Mahmoudi ◽  
Hana Ghaneei
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Return ◽  
Stock Exchange ◽  
Markov Switching ◽  
Stock Index ◽  
Var Model ◽  
Content Type ◽  
Oil Market ◽  
The Impact

Purpose This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX). Design/methodology/approach The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model. Findings The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2. Originality/value This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

scholarly journals A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

2013 ◽  
Vol 5 (8) ◽  
pp. 379-384
Author(s):  
Seuk Wai ◽  
Mohd Tahir Ismail . ◽  
Siok Kun Sek .
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Markov Switching ◽  
Stock Index ◽  
Smooth Transition ◽  
Series Data ◽  
Var Model ◽  
Economic Time Series ◽  
Vector Autoregressive ◽  
Economic Time

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.

scholarly journals Analysis of Integration of the Indonesian Sharia Stock Index with Developing Countries

2021 ◽  
Vol 9 (2) ◽  
pp. 347
Author(s):  
Budiandru Budiandru ◽  
Deni Nuryadin ◽  
Muhammad Dika Pratama
Keyword(s):  
Developing Countries ◽  
Time Series Data ◽  
Stock Index ◽  
Series Data ◽  
Policy Reforms ◽  
Vector Error Correction ◽  
Trade Relations ◽  
Vector Error ◽  
The Impact

<p><em>Globalization is rapidly causing an integration of economic and financial systems worldwide, resulting in shocks to the Islamic stock index and reducing the benefits of diversification for investors. Therefore, this study analyzes the integration, influence, response, and contribution of shocks to each developing country’s Islamic stock index. Specifically, analyzing the effect of developing country sharia stock index shocks on Indonesia's sharia stock index. The study uses monthly time series data for 2011-2021 with samples from Indonesia, Turkey, Malaysia, Pakistan, Kuwait, and India using the Vector Error Correction Model (VECM) method. The results showed cointegration or a long-term relationship in the developing countries’ sharia stock index. The Malaysian Islamic Stock Index and the Indian Islamic Stock Index influence the Indonesian Islamic Stock Index. Furthermore, the Indonesian Islamic Stock Index stabilized the fastest in response to the Turkish Islamic Stock Index shocks. However, the Malaysian Islamic Stock Index shock contributes the most to the Indonesian Islamic Stock Index. Developing countries could improve the infrastructure of the Islamic stock index and policy reforms. This would minimize the impact of international stock index shocks and accelerate integration. Investors should consider the dominant economic strength, geographical factors, and trade relations in determining portfolio diversification in global economic conditions.</em></p><div class="notranslate" style="all: initial;"> </div>

scholarly journals Daily Stock Movements Through the Lens of Event and News: Evidence from the Pakistan Stock Exchange

2021 ◽  
Author(s):  
Shahid Raza ◽  
Baiqing Sun ◽  
Pwint Kay Khine
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
News Daily ◽  
Stock Market Index ◽  
Daily Newspaper ◽  
Monthly Basis ◽  
Market Index ◽  
The Impact

This study will investigate different signals and events/news that determined the stock market's movements. As we know, many factors affect the stock market on a daily, weekly, and monthly basis, e.g., rate of interest, exchange rate, and oil prices, etc. Our research will investigate the impact of daily events/news in the KSE-100 index due to several policies announced and events/news in the country because the daily movements in the stock market can be determined only by different signals and events/news. Time series data is collected daily for particular reasons from "The News" (Daily Newspaper, Sunday edition) from 2010 to 2019. The results of this study show that political and global news affects the stock market index ferociously. For investors, the investment in blue chips is not less than a safe haven. When day-to-day transactions are concerned, there is always a higher panic attack than the herd behaviour in the stock exchange. Investors tend to make prompt responses to negative rather than positive news, which makes them risk averters. Our finding also confirmed that the ARCH/GARCH model is better than the simple OLS method concerning stock market upheaval.

Russian Stock Index volatility: Oil and sanctions

2020 ◽  
pp. 86-100
Author(s):  
Artem D. Aganin
Keyword(s):  
Stock Market ◽  
Price Volatility ◽  
Oil Prices ◽  
Stock Index ◽  
Sharp Drop ◽  
Oil Price Volatility ◽  
Russian Stock Market ◽  
The Garch Model ◽  
The Impact ◽  
Parametric Estimate

Since 2014, the Russian stock market has been under pressure due to both sanctions and a sharp drop in oil prices, which led to its increased volatility. This paper analyzes the impact of the price volatility of Brent oil and sanctions on the volatility of the Russian stock index RTS. Under volatility the paper understands both its parametric estimate obtained from the GARCH model estimation as well as non-parametric estimate — realized volatility. To estimate the effect of oil price volatility and sanctions, several cointegrated regressions were analyzed. The robustness of the results in relation to the choice of volatility assessment is demonstrated. The results show that RTS index volatility still depends on oil prices volatility in 2007—2018. This dependence is most pronounced in the periods of crisis. The paper also demonstrates the adjustment of the Russian stock market to the previous sanctions, which calls into question their long-term efficiency.

scholarly journals Futures trading and the underlying stock volatility: A case of the FTSE/JSE TOP 40

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Mr. Rabson Magweva ◽  
Mrs. Magret Munyimi ◽  
Mr. Justine Mbudaya
Keyword(s):  
Time Series Data ◽  
Financial Time Series ◽  
Garch Model ◽  
Stock Index ◽  
Futures Contracts ◽  
Series Data ◽  
Policy And Practice ◽  
Stock Volatility ◽  
Top 40 ◽  
The Impact

Purpose: This study analyzed the impact of listing and trading futures contracts on the underlying stock index volatility behavior. The FTSE/JSE TOP 40 index was the index of interest.Methodology: To capture the non-constant variance of the residuals, a modified Generalised Autoregressive Conditionally Heteroscedasticity (GARCH) model was adopted. This model was used was adopted given that financial time series data exhibited ARCH effects. The GARCH model was estimated after dividing the sample period into pre-and post-futures eras.Findings: The research findings point towards stabilization effects on underlying stock volatility and refute the suggestion that futures markets improve the dissemination of information to the corresponding spot markets. On the same note, the introduction of futures increased the volatility persistence of index returns.Unique contribution to theory, policy, and practice: This paper applied a modified-GARCH by incorporating a dummy variable to the traditional GARCH model. The study used an emerging economy as a case study which makes the results and conclusions more specific and applicable. On the same note, the study covered the pre-and post-global crisis of 2007/8 in a Sub-Saharan nation. In practice, stock markets are encouraged to introduce futures contracts on highly volatile spot market assets.

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