scholarly journals Time-Varying Relationship of News Sentiment, Implied Volatility and Stock Returns

2012 ◽  
Author(s):  
Lee A. Smales
2020 ◽  
Author(s):  
◽  
Parveshsingh Seeballack

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.


2005 ◽  
Vol 30 (2) ◽  
pp. 27-46 ◽  
Author(s):  
Ajay Pandey

Estimation and forecasting of volatility of asset returns is important in various applications related to financial markets such as valuation of derivatives, risk management, etc. Till early eighties, it was commonly assumed that the volatility of an asset is constant and estimation procedures were based on this assumption even though some of the pioneering studies on property of stock market returns did not support this assumption. Following the pioneering work of Engle and Bollerslev in eighties on developing models (ARCH/GARCH type models) to capture time-varying characteristics of volatility and other stock return properties, extensive research has been done world over in modeling volatility for estimation and forecasting. There are broadly four possible approaches for estimating and forecasting volatility. These are: Traditional Volatility Estimators— These estimators assume that ‘true’ volatility is unconditional and constant. The estimation is based on either squared returns or standard deviation of returns over a period. Extreme Value Volatility Estimators— These estimators are similar to traditional estimators except that these also incorporate high and low prices observed unlike traditional estimators which are based on closing prices of the asset. Conditional Volatility Models— These models (ARCH/GARCH type models) take into account the time-varying nature of volatility. There have been quite a few extensions of the basic conditional volatility models to incorporate ‘observed’ characteristics of asset/stock returns. Implied Volatility— In case of options, most of the parameters relevant for their valuation can be directly observed or estimated, except volatility. Volatility is, therefore, backed out from the observed option values and is used as volatility forecast. The empirical research across countries and markets has not been equivocal about the effectiveness of using these approaches. This study compares the result of the first three approaches in estimating and forecasting Nifty returns. Based on four different criteria related to bias and efficiency of the various estimators and models, this study analysed the estimation and forecasting ability of three different traditional estimators, four extreme value estimators, and two conditional volatility models. As a benchmark, it used ‘realized’ volatility estimates. The findings of this study are as follows: For estimating the volatility, the extreme value estimators perform better on efficiency criteria that the conditional volatility models. In terms of bias, conditional volatility models perform better than the extreme value estimators. As far as predictive power is concerned, extreme value estimators estimated from sample of length equal to forecast period perform better than the conditional volatility estimators in providing five-day and month ahead volatility forecasts.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Boubekeur Baba ◽  
Güven Sevil

AbstractThis study discusses the trading behavior of foreign investors with respect to economic uncertainty in the South Korean stock market from a time-varying perspective. We employ a news-based measure of economic uncertainty along with the model of time-varying parameter vector autoregression with stochastic volatility. The empirical analysis reveals several new findings about foreign investors’ trading behaviors. First, we find evidence that positive feedback trading often appears during periods of high economic uncertainty, whereas negative feedback trading is exclusively observable during periods of low economic uncertainty. Second, the foreign investors’ feedback trading appears mostly to be well-timed and often leads the time-varying economic uncertainty except in periods of global crises. Third, lagged negative (positive) response of net flows to economic uncertainty is found to be coupled with lagged positive (negative) feedback trading. Fourth, the study documents an asymmetric response of foreign investors with regard to negative and positive shocks of economic uncertainty. Specifically, we find that they instantly turn to positive feedback trading after a negative contemporaneous response of net flows to shocks of economic uncertainty. In contrast, they move slowly toward negative feedback trading after a positive response of net flows to uncertainty shocks.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Begüm Yurteri Kösedağlı ◽  
Gül Huyugüzel Kışla ◽  
A. Nazif Çatık

AbstractThis study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020. The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study. Moreover, the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market. The results further suggest that, except for Indonesia, oil prices have a positive impact on the sectoral returns of all markets, whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.


2020 ◽  
Vol 136 (2) ◽  
pp. 444-470 ◽  
Author(s):  
Martijn Boons ◽  
Fernando Duarte ◽  
Frans de Roon ◽  
Marta Szymanowska

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 394
Author(s):  
Adeel Nasir ◽  
Kanwal Iqbal Khan ◽  
Mário Nuno Mata ◽  
Pedro Neves Mata ◽  
Jéssica Nunes Martins

This study aims to apply value at risk (VaR) and expected shortfall (ES) as time-varying systematic and idiosyncratic risk factors to address the downside risk anomaly of various asset pricing models currently existing in the Pakistan stock exchange. The study analyses the significance of high minus low VaR and ES portfolios as a systematic risk factor in one factor, three-factor, and five-factor asset pricing model. Furthermore, the study introduced the six-factor model, deploying VaR and ES as the idiosyncratic risk factor. The theoretical and empirical alteration of traditional asset pricing models is the study’s contributions. This study reported a strong positive relationship of traditional market beta, value at risk, and expected shortfall. Market beta pertains its superiority in estimating the time-varying stock returns. Furthermore, value at risk and expected shortfall strengthen the effects of traditional beta impact on stock returns, signifying the proposed six-factor asset pricing model. Investment and profitability factors are redundant in conventional asset pricing models.


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