Early news is good news: the effects of market opening on market volatility (original) (raw)
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Early News is Good News: The E ects of Market Opening on Market Volatility
In this paper we examine the characteristics of market opening news and its impact on the estimated coe cients of the conditional volatility models of the GARCH class. We nd that the di erences between the opening price of one day and the closing price of the day before have di erent characteristics when considering various stock market indices on which options are actively traded. The impact of a suitable positive-valued transformation of these di erences has the e ect of modifying the direct impact of daily innovations on volatility, ...
Estimating stock market volatility using asymmetric GARCH models
Applied Financial Economics, 2008
A comprehensive empirical analysis of the mean return and conditional variance of Tel Aviv Stock Exchange (TASE) indices is performed using various GARCH models. The prediction performance of these conditional changing variance models is compared to newer asymmetric GJR and APARCH models. We also quantify the day-of-the-week effect and the leverage effect and test for asymmetric volatility. Our results show that the asymmetric GARCH model with fat-tailed densities improves overall estimation for measuring conditional variance. The EGARCH model using a skewed Student-t distribution is the most successful for forecasting TASE indices.
GARCH-based Volatility Forecasts for Market Volatility Indices
2002
Volatility forecasting is one of the main issues in the financial econometrics literature. Volatility measures may be derived from statistical models for conditional variance, or from option prices. In recent times, indices have been suggested which summarize the implied volatility of widely traded market index options. One such index is the so-called VXN, an average of 30-day ahead implied volatilities of the options written on the NASDAQ-100 Index. In this paper we show how forecasts obtained with traditional GARCH-type models can be used to forecast the volatility index VXN.
Modelling the Impact of Overnight Surprises on Intra-daily Volatility
Australian Economic Papers, 2001
In this paper we evaluate the impact that stock returns recorded between market closing and opening the next business day have on intra-daily volatility. A simple test shows that the estimated volatility clustering of the intra-daily returns may be affected by a market opening surprise bias. An extension of the standard GARCH model is suggested here to include the effect of this surprise and is applied on a sample of largely traded US stocks. The performance of two speci®cations in which this effect is included is evaluated in an out-of-sample forecasting exercise relative to their standard counterparts.
Impact of information arrival on volatility of intraday stock returns
2011
In this empirical study we have considered the impact of information flow on the volatility of a particular stock using high frequency return and news data on the Eu- rostoxx 50 market. In addition to using volume as a proxy for information flow, we have included company specific announcements, to the conditional variance of the Gen- eralized Autoregressive Conditional Heteroscedastic model (GARCH). For this purpose we have constructed five measures of the impact of public information flow in the mar- ket transforming commonly available news scores through dierent techniques such as linear and exponential decreasing weight, impact function etc. We have analyzed the behaviour of volatility, estimated by squared returns for the next 4 hours after arrival of a non overlapping news, having a significant impact on the firm's stock return. A signif- icant impact of the information flow accessed by the news score coecient is observed for majority of in our analysis. Furthermore, the in...
ANNALS OF THE “CONSTANTIN BRÂNCUȘI” UNIVERSITY OF TÂRGU JIU LETTER AND SOCIAL SCIENCE SERIES, 2024
IN THE STOCK MARKET, VOLATILITY IS A TERM USED TO DESCRIBE THE DEGREE TO WHICH THE PRICES OF ASSETS FLUCTUATE AND DETERMINES THE DEGREE OF RISK OR UNCERTAINTY. THE MAIN AIM OF THE PRESENT STUDY IS TO MODELING THE BEHAVIOR OF THE SWITZERLAND STOCK MARKET USING DATA FROM 4TH JANUARY, 2000 TO 9TH NOVEMBER, 2023. THROUGH THE APPLICATION OF GARCH FAMILY MODELS WHICH, INCLUDE GARCH/TARCH, EGARCH, COMPONENT ARCH (1,1), AND PARCH. THE STUDY USED A SAMPLE NUMBER OF 5994 DAILY OBSERVATIONS FOR SWISS STOCK INDEX REPRESENTING THE SWITZERLAND STOCK MARKET. WE USED SOME STATISTICAL TECHNIQUES SUCH AS PHILLIPS-PERRON AND AUGMENTED DICKEY FULLER TESTS STATISTIC. THE ARCH LAGRANGE MULTIPLIER (LM) TEST, PARCH MODEL. WE UTILIZED THE EVIEWS 12 ECONOMETRICS PACKAGE. THIS STUDY HIGHLIGHTS THE SIGNIFICANCE OF ACCURATELY AND METICULOUSLY SIMULATING STOCK MARKET BEHAVIOR IN ADDITION TO ADDING TO THE CORPUS OF KNOWLEDGE IN FINANCIAL ECONOMETRICS. THE CONCLUSIONS AND METHODS DISCUSSED IN THIS STUDY PROVIDE A STRONG BASIS FOR FURTHER RESEARCH, ENHANCING OUR CAPACITY TO PREDICT MARKET MOVEMENTS AND MAKE WISE CHOICES IN A VOLATILE FINANCIAL ENVIRONMENT.
On forecasting daily stock volatility: The role of intraday information and market conditions
International Journal of Forecasting, 2009
Several recent studies advocate the use of nonparametric estimators of daily price variability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample fit analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Forecast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t−1t−1 to forecast volatility on day tt is most advantageous when day tt is a low volume or an up-market day. These results have implications for option pricing, asset allocation and value-at-risk.
The paper presents factor and predictive GARCH(1,1) models of the Warsaw Stock Exchange (WSE) main index WIG. An approach where the mean equation of the GARCH model includes a deterministic part is applied. The models incorporate such explanatory variables as volume of trade and major international stock market indices. The paper exploits the direction quality measures that can be used as alternative measures to evaluate model goodness of fit. Finally, the in-sample versus the out-of-sample forecasts from the estimated models are compared and model forecasting performance is discussed.