scholarly journals Dynamics of Time Varying Volatility in Stock Returns: Evidence from Nepal Stock Exchange

2020 ◽  
Vol 5 (1) ◽  
pp. 15-34
Author(s):  
Surya Bahadur Rana
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Significant Risk ◽  
Error Distribution ◽  
Distribution Model ◽  
Time Varying ◽  
Volatility Persistence ◽  
Study Results ◽  
Daily Stock Returns ◽  
Daily Returns

This study examines the properties of time varying volatility of daily stock returns in Nepal over the period 2011-2020 using 2059 observations on daily returns of NEPSE index series. The study examines various symmetric and asymmetric GARCH family models using several specifications of error distribution. The results of symmetric GARCH (1,1) and GARCH-M (1, 1) models indicate that there is volatility persistence in daily returns on composite NEPSE index series over the sampled period. However, the estimated results for GARCH-M (1, 1) models show that the stock returns in Nepal offer no significant risk premium to hedge against risk associated with investment in stocks. The study also demonstrates that asymmetric TGARCH (1, 1) and EGARCH (1, 1) models fail to capture the leverage effects on the volatility. Finally, study results show that GARCH (1, 1) with student’s t error distribution model is the best fitted one to capture the volatility persistence of daily returns on NEPSE index series over the sampled period. The findings from this study offers an additional insight in understanding the volatility pattern of daily stock returns in Nepal for the most recent period that helps investors in forming a sound strategy to address the risk pattern of investing in stock market of Nepal.

scholarly journals Stock Returns And Volatility İn An Emerging Equity Market. Evidence From Kenya

2016 ◽  
Vol 12 (4) ◽  
pp. 79 ◽  
Author(s):  
David Ndwiga ◽  
Peter W Muriu
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Time Series Data ◽  
Stock Exchange ◽  
Significant Risk ◽  
Series Data ◽  
Volatility Clustering ◽  
Cross Listing ◽  
Daily Returns

This study investigates volatility pattern of Kenyan stock market based on time series data which consists of daily closing prices of NSE Index for the period 2ndJanuary 2001 to 31st December 2014. The analysis has been done using both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. The study provides evidence for the existence of a positive and significant risk premium. Moreover, volatility shocks on daily returns at the stock market are transitory. We do not find any significant leverage effect. Introduction of the new regulations on foreign investors with a 25% minimum reserve of the issued share capital going to local investors (in 2002), introduction of live trading, cross listing in Uganda and Tanzania stock exchange (in 2006) and change in equity settlement cycle from T+4 to T+3 (in 2011) significantly reduce volatility clustering. The onset of US tapering increase the daily mean returns significantly while reducing conditional volatility.

Market reaction to scheduled and unscheduled news announcements under varying economic conditions

2020 ◽  
Author(s):  
◽  
Parveshsingh Seeballack
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Implied Volatility ◽  
Equity Market ◽  
Economic Conditions ◽  
Time Varying ◽  
Equity Risk Premium ◽  
Macroeconomic News ◽  
Equity Risk ◽  
News Announcements

The unifying theme of this dissertation is the study of the role of macroeconomic news announcements in the context of the equity market. We focus on two important areas of the asset pricing theory, namely price discovery and equity risk premium forecasting. Chapter 2 investigates the time-varying sensitivity of stock returns to scheduled macroeconomic news announcements (MNAs) using high-frequency data. We present new insights into how efficiently stock returns incorporate the informational content of MNAs. We further provide evidence that the stock market response to MNAs is cyclical, and finally we conclude Chapter 2 with an investigation into the factors driving the time-varying sensitivity of stock return to MNAs. Chapter 3 investigates the time-varying sensitivity of stock returns in the context of unscheduled macroeconomic news announcements using high-frequency data. We investigate the speed and persistence in stock returns’ response to unscheduled macro-news announcements, and whether the reactions are dependent on the state of the economy, or general investor sentiment level. Combined, Chapters 2 and 3 provide interesting insights into how equity market participants react to the arrival of scheduled and unscheduled macro-announcements, under varying economic conditions. Chapter 4 focuses on equity risk premium forecasting. We investigate the predictive ability of option-implied volatility variables at monthly horizon, under varying economic conditions. We innovate by constructing monthly announcement and non-announcement option-implied volatility predictors and assess whether the monthly announcement option-implied volatility predictors contain additional information for better out-of-sample predictions of the monthly equity risk premium. Each of the three empirical chapters explores a unique aspect of the asset pricing theory in the context of the U.S. equity market.

A TEST OF THE GARCH(1, 1) SPECIFICATION FOR DAILY STOCK RETURNS

2010 ◽  
Vol 14 (S1) ◽  
pp. 137-144 ◽  
Author(s):  
Richard A. Ashley ◽  
Douglas M. Patterson
Keyword(s):  
Stock Returns ◽  
Model Evaluation ◽  
Stock Index ◽  
Serial Dependence ◽  
Financial Returns ◽  
New Model ◽  
Daily Stock Returns ◽  
Daily Returns

Daily financial returns (and daily stock returns, in particular) are commonly modeled as GARCH(1, 1) processes. Here we test this specification using new model evaluation technology developed by Ashley and Patterson that examines the ability of the estimated model to reproduce features of particular interest: various aspects of nonlinear serial dependence, in the present instance. Using daily returns to the CRSP equally weighted stock index, we find that the GARCH(1, 1) specification cannot be rejected; thus, this model appears to be reasonably adequate in terms of reproducing the kinds of nonlinear serial dependence addressed by the battery of nonlinearity tests used here.

scholarly journals Effects of asset frequency components on value-at-risk in emerging and developed markets

2020 ◽  
Vol 40 (1) ◽  
pp. 145
Author(s):  
Milton Biage ◽  
Pierre Joseph Nelcide
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Value At Risk ◽  
Financial Assets ◽  
Back Testing ◽  
Window Technique ◽  
Rolling Window ◽  
Frequency Components ◽  
Daily Stock Returns ◽  
Daily Returns

<p>Value-at-Risk was estimated using the technique of wavelet decomposition with goal to analyze the frequency components' impacts on variances of daily stock returns, and on  forecasts. Daily returns of twenty-one shares of the Ibovespa and daily returns of twenty-two shares of the DJIA were used. The  model was applied to the reconstructed returns to model and establish the prediction of conditional variance, applying the rolling window technique. The Value-at-Risk was then estimated, and the results showed that the DJIA shares showed more efficient market behavior than those of Ibovespa. The differences in behavior induces to affirm that VaRs, used in the analysis of financial assets from different markets with different governance premises, should be estimated by series of returns reconstructed by aggregations of components of different frequencies. A set of back-testing was applied to confront the estimated , which demonstrated that the estimation of  models are consistent.</p>

scholarly journals Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets

2016 ◽  
Vol 41 (3) ◽  
pp. 234-246 ◽  
Author(s):  
Sanjay Sehgal ◽  
Vidisha Garg
Keyword(s):  
South Africa ◽  
Asset Pricing ◽  
South Korea ◽  
Stock Returns ◽  
Risk Premium ◽  
Emerging Market ◽  
Significant Risk ◽  
Residual Variance ◽  
Cross Sectional ◽  
Risk Premiums

Executive Summary Cross-sectional volatility measures dispersion of security returns at a particular point of time. It has received very little focus in research. This article studies the cross-section of volatility in the context of economies of Brazil, Russia, India, Indonesia, China, South Korea, and South Africa (BRIICKS). The analysis is done in two parts. The first part deals with systematic volatility (SV), that is, cross-sectional variation of stock returns owing to their exposure to market volatility measure ( French, Schwert, & Stambaugh, 1987 ). The second part deals with unsystematic volatility (UV), measured by the residual variance of stocks in a given period by using error terms obtained from Fama–French model. The study finds that high SV portfolios exhibit low returns in case of Brazil, South Korea, and Russia. The risk premium is found to be statistically significantly negative for these countries. This finding is consistent with Ang et al. and is indicative of hedging motive of investors in these markets. Results for other sample countries are somewhat puzzling. No significant risk premiums are reported for India and China. Significantly positive risk premiums are observed for South Africa and Indonesia. Further, capital asset pricing model (CAPM) seems to be a poor descriptor of returns on systematic risk loading sorted portfolios while FF is able to explain returns on all portfolios except high SV loading portfolio (i.e., P1) in case of South Africa which seems to be an asset pricing anomaly. It is further observed that high UV portfolios exhibit high returns in all the sample countries except China. In the Chinese market, the estimated risk premium is statistically significantly negative. This negative risk premium is inconsistent with the theory that predicts that investors demand risk compensation for imperfect diversification. The remaining sample countries show significantly positive risk premium. CAPM does not seem to be a suitable descriptor for returns on UV sorted portfolios. The FF model does a better job but still fails to explain the returns on high UV sorted portfolio in case of Brazil and China and low UV sorted portfolio in South Africa. The findings are relevant for global fund managers who plan to develop emerging market strategies for asset allocation. The study contributes to portfolio management as well as market efficiency literature for emerging economies.

Volatility in the Indian Stock Market: Evidence from S&P CNX 500

2020 ◽  
Vol 17 (4) ◽  
pp. 1826-1830
Author(s):  
V. Shanthaamani ◽  
V. B. Usha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Conditional Variance ◽  
Leverage Effect ◽  
Volatility Clustering ◽  
Expected Stock Returns ◽  
High Volatility ◽  
Indian Stock Market ◽  
Daily Returns

This paper uses the Generalized Autoregressive Conditional Heteroskedastic models to estimate volatility (conditional variance) in the daily returns of the S&P CNX 500 index over the period from April 2007 to March 2018. The models include both symmetric and asymmetric models that capture the most common stylized facts about index returns such as volatility clustering and leverage effect. The empirical results show that the conditional variance process is highly persistent and provide evidence on the existence of risk premium for the S&P CNX 500 index return series which support the positive correlation hypothesis between volatility and the expected stock returns. Our findings also show that the asymmetric models provide better fit than the symmetric models, which confirms the presence of leverage effect. These results, in general, explain that high volatility of index return series is present in Indian stock market over the sample period.

scholarly journals Modelling & Forecasting Volatility of Daily Stock Returns Using GARCH Models: Evidence from Dhaka Stock Exchange

2021 ◽  
Author(s):  
Tuhin Ahmed ◽  
Nurun Naher
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Error Distribution ◽  
Good News ◽  
Estimation Accuracy ◽  
Asymmetric Effect ◽  
Forecasting Accuracy ◽  
Daily Stock Returns ◽  
Student’S T ◽  
T Distribution

Modelling volatility has become increasingly important in recent times for its diverse implications. The main purpose of this paper is to examine the performance of volatility modelling using different models and their forecasting accuracy for the returns of Dhaka Stock Exchange (DSE) under different error distribution assumptions. Using the daily closing price of DSE from the period 27 January 2013 to 06 November 2017, this analysis has been done using Generalized Autoregressive Conditional Heteroscedastic (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedastic (APARCH), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH), Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) and Integrated Generalized Autoregressive Conditional Heteroscedastic (IGARCH) models under both normal and student’s t error distribution. The study finds that ARMA (1,1)- TGARCH (1,1) is the most appropriate model for in-sample estimation accuracy under student’s t error distribution. The asymmetric effect captured by the parameter of ARMA (1,1) with TGARCH (1,1), APARCH (1,1) and EGARCH (1,1) models shows that negative shocks or bad news create more volatility than positive shocks or good news. The study also provides evidence that student’s t distribution for errors improves forecasting accuracy. With such an error distribution assumption, ARMA (1,1)-IGARCH (1,1) is considered the best for out-of-sample volatility forecasting.

scholarly journals A Multivariate Analysis of the Effects of Structural Breaks on Stock Return Volatility Persistence: The Case of the US and Japan

2019 ◽  
Vol 10 (3) ◽  
pp. 39
Author(s):  
Chikashi Tsuji
Keyword(s):  
Multivariate Analysis ◽  
Stock Returns ◽  
Stock Return ◽  
Structural Breaks ◽  
Structural Break ◽  
Return Volatility ◽  
Time Varying ◽  
Stock Return Volatility ◽  
Volatility Persistence ◽  
The Us

This paper quantitatively inspects the effects of structural breaks in stock returns on their volatility persistence by using the stock return data of the US and Japan. More concretely, applying the diagonal BEKK-MGARCH model with and without structural break dummies to the returns of S&P 500 and TOPIX, we reveal the following interesting findings. (1) First, we clarify that for both the US and Japanese stock returns, the values of the GARCH parameters, namely, the values of the volatility persistence parameters in the diagonal BEKK-MGARCH models decrease when we include the structural break dummies in the models. (2) Second, we further find that interestingly, during the Lehman crisis in 2008, the estimated time-varying volatilities from the diagonal BEKK-MGARCH model with structural break dummies are slightly higher than those from the no structural break dummy model. (3) Third, we furthermore reveal that also very interestingly, the estimated time-varying correlations from the diagonal BEKK-MGARCH model with no structural break dummy are slightly higher than those from the structural break dummy model.

scholarly journals Is There A Difference Between Domestic And Foreign Risk Premium? The Case Of China Stock Market

2014 ◽  
Vol 30 (5) ◽  
pp. 1287
Author(s):  
Frederic Teulon ◽  
Khaled Guesmi ◽  
Salma Fattoum
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Market Price ◽  
Time Varying ◽  
Expected Return ◽  
Market Price Of Risk ◽  
Market Prices ◽  
Price Of Risk ◽  
Show Evidence ◽  
The Difference

This article studies the dynamic return and market price of risk for Chinese stocks (A-B shares). A Multivariate DCC-GARCH model is used to capture the feature of time-varying volatility in stock returns. We show evidence of different pricing mechanisms explained by the difference in the expected return and market price of risk between A and B shares. However, the significance of the difference between market prices of risk disappears if GARCH models are used.

scholarly journals NOISE TRADER RISK: EVIDENCE FROM VIETNAM STOCK MARKET

2019 ◽  
Vol 128 (5C) ◽  
pp. 5
Author(s):  
Phan Khoa Cuong ◽  
Tran Thi Bich Ngoc ◽  
Bui Thanh Cong ◽  
Vo Thi Quynh Chau
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Positive Impact ◽  
Moving Average ◽  
Noise Trader ◽  
Daily Stock Returns ◽  
Fundamental Value ◽  
The Impact ◽  
Daily Returns

<p><strong>Abstract: </strong>This paper investigates the existence of noise trader risk in Vietnam’s stock market and its effect on the daily returns of stock prices. The methodologies contain the estimation of GARCH (1,1) model to filter the residuals using the moving average method to calculate the impact of information traders. Noise trader risk or the risk that is caused by noise traders is derived by subtracting the residuals by the rational traders’ impact. We find that the noise trader risk does exist in Vietnam’s stock market and its impact on daily returns of stocks is unpredictable. Meanwhile, we find a positive impact of information traders on the stock returns. It increases the daily stock returns, and in turn, helps the market to correct itself because the stock prices move back to its fundamental value.</p><p><strong>Keywords</strong>: noise trader risk, GARCH (1,1), Vietnam’s stock market</p>

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