Author: Zamani Calvin MasingaPublisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 134
Book Description
Modeling and Forecasting Stock Return Volatility in the JSE Securities Exchange
Author: Zamani Calvin MasingaPublisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 134
Book Description
Modeling and Forecasting Stock Return Volatility
Author: Pia GrammigModelling and forecasting stock return volatility and the term structure of interest rates
Author: Michiel de PooterPublisher: Rozenberg Publishers
ISBN: 9051709153
Category :
Languages : en
Pages : 286
Book Description
This dissertation consists of a collection of studies on two areas in quantitative finance: asset return volatility and the term structure of interest rates. The first part of this dissertation offers contributions to the literature on how to test for sudden changes in unconditional volatility, on modelling realized volatility and on the choice of optimal sampling frequencies for intraday returns. The emphasis in the second part of this dissertation is on the term structure of interest rates.
Volatility Modeling and Forecasting for NIFTY Stock Returns
Author: Gurmeet SinghPublisher:
ISBN:
Category :
Languages : en
Pages : 24
Book Description
In this paper, an attempt has been made to model the volatility of NIFTY index of National Stock Exchange (NSE) and forecast the NIFTY stock returns for short term by using daily data ranging from January, 2000, to December, 2014, which comprises 3736 data points for the analysis by using Box-Jenkins or ARIMA model. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. It is shown that ARCH family models outperform the conventional OLS models. ADF test and unit root testing is done to know the stationarity of the series, later the AR(p) and MA(q) orders are identified with the help of minimum information criterion as suggested by Hannan-Rissanen. As per the analysis, ARIMA (1,0,1) model was found to be the best fit to forecast the volatility of NIFTY stock returns. The model can be used by the investors to forecast the short run NIFTY stock returns and for making more profitable and less risky investments decision.
Can Market Volume Help in Predicting Share Market Volatility?
Author: Dorbor HagbaPublisher: LAP Lambert Academic Publishing
ISBN: 9783844313284
Category :
Languages : en
Pages : 84
Book Description
This book explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the Johannesburg Stock Exchange (JSE). The study is largely inspired by the work of Chris BrookThe volume of shares traded might be as important as the change in a market index since substantial price increases and decreases are often accompanied by heavy trading activitys (1998).The results of this study project indicate that augmenting models of volatility with measures of lagged volume leads only to fairly small improvements in forecasting performance. The report also shows that the Johannesburg Stock Exchange is vulnerable to financial turmoil in other major markets.
Modeling and Forecasting Daily Stock Return Volatility with Intra-day Price Fluctuation Information
Author: Yansong LuModelling and Forecasting the Volatility of JSE Returns
Author: Oratile Ame KgosietsilePublisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 90
Book Description
Modeling Return Volatility on the JSE Sectors
Author: Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 149
Book Description
Modelling -- Volatility -- Johannesburg Stock Exchange -- ARCH -- GARCH -- EGARCH -- TGARCH -- JSE sectors -- Models.
Modeling Stock Return Volatility, a Comparative Approach
Author: Robert KrimetzPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
The application of machine learning and probabilistic programming methods on stock return prediction has grown in tandem with the availability of high frequency stock data. With well recorded heteroskedasticity in historical stock returns, modeling attempts have evolved from making general assumptions about the underlying data generating distribution to predicting changes in the underlying distribution of returns. The increase in popularity of 'tradable volatility' through derivative contacts and VIX futures over the past three decades has motivated research efforts to model the variance of daily returns. Along this line of research, three schools of thought have emerged to model return volatility; Time Series Models, Stochastic Models, and Bayesian Models. Given that the preliminary assumptions underlying these models differ, the nature of their results and the varying metrics used to calculate their respective accuracy makes it difficult to directly compare them. Accordingly, the currently available pool of research has diverged along these three separate paths making it unclear the advantages of each. Notably, Bayesian models have largely been neglected in the current pool of research due to their computational intensity. In this paper I derive ten time series and Bayesian models then provide a comprehensive comparative study of the results on real stock data. I found that Bayesian models with intractable posterior distributions significantly outperform time series models at predicting directional change in future volatility, while the GARCH and FIGARCH time series models generate the most accurate point predictions for future volatility. I hope the results outlined in this paper better contextualize different volatility predictions and motivate the creation of more accurate tradeable volatility models.
Estimating Stock Return Volatility in Indian and Chinese Stock Market
Author: Vanita TripathiPublisher:
ISBN:
Category :
Languages : en
Pages : 13
Book Description
Investors step into the stock market with the objective of earning smart returns on their investments. The stock market can help in realising these goals of the investors, however, all investments are subject to risks. The origin of the risk is the uncertainty of realising the desired returns on the investment. This aspect is known as risk of the investment. This paper aims to search the best model to estimate and forecast volatility of Indian and Chinese stock market. The data for the paper is related to the two main indices of Indian Stock Market namely, SENSEX and NIFTY and two indices of Chinese stock market, namely, Shenzhen composite index and Shanghai composite index for the period July 2003 to June 2013. We applied symmetrical as well as asymmetrical GARCH models to the data. Among all the three models i.e. GARCH, EGARCH and TARCH, we found the GARCH (1,1) model as the best model to estimate and forecast the volatility of Chinese stock market for both the daily and weekly return series. For the Indian stock market, the recommended volatility estimation and forecasting model is EGARCH model that captures the leverage effect. We did not find volatility clustering and leverage effect for the monthly return series for both Indian and Chinese stock market. Thus, it is suggested to use the traditional time invariant volatility models for the monthly return series.