Asymmetric Conditional Volatility Research Articles

Nexus between asymmetric information and stock market volatility: evidence from Sri Lanka

This study examines the impact of asymmetric information behaviour and macroeconomic variability in modelling the stock market volatility. CSE market does not show the characteristics which may potentially lead to larger volatility shocks. The idiosyncratic volatility is more subjected to the irrational investment decisions with the absence of relevant market information. Therefore, the information asymmetries motivate investors to highly depend on irrational reasons which lead to irrational volatility shocks. The variance equation of the EGARCH model was applied for identifying the impact of the asymmetric information behaviour. The mean-variance equation of EGARCH has been modelled with GDP, inflation, interest rate, and money supply for recognizing macroeconomic impacts. The study finds that the CSE market was significantly experiencing asymmetric information problem. As a result, uninformed investors make their decision based on the market sentiment creating irrational price volatilities. The mean-variance equation shows that macroeconomic variability has a significant impact on explaining the future asymmetric conditional volatility. However, CSE volatility spends a few weeks to adjust the relevant macroeconomic shocks.

Modeling and assessing systematic risk in stock markets in major oil exporting countries

Introduction. This paper aims to assess time variability of beta coefficients (systematic risk) of Capital Asset Pricing Model (CAPM) using data from five key sectors in Saudi Arabia and Kuwait stock markets. Material and methods. To assess time – varying systematic risk we employed symmetric as well as asymmetric conditional volatility specifications to account for skewness and leptkurtosis of high frequency financial time series to better specify conditional higher moments. Results & discussions. The results of the paper support significant evidence of time-varying beta coefficients for all sectors included in the study, in particular the banking sector, and relatively with a lesser degree ,the food, and the service sectors in both countries. For the banking sector in Saudi Arabia, the beta coefficients variability during the sample period estimated between (0.18 to 22.1), and also for Kuwait stock market the beta coefficient of the banking sector variability estimated between (0.16 to 22.1). This result invalidates, at least in the context of the sample country’s banking sectors, the standard application of (CAPM) that assumes constant beta coefficients. Also indicated in the paper, time-varying beta estimates are consistent with a modified version of CAPM prediction that is portfolios with wider range of beta variations expected to yield higher return values and those with lower range of beta variations yield lower returns. Conclusion. In this new context, risk is no longer is a point estimate as implied by the standard CAPM model, but it is a range of values. Our findings also show the size and the range of beta variations are sensitive to skewness and fat tailedness that characterize asset returns distribution.

The response of precious metal futures markets to unconventional monetary surprises in the presence of uncertainty

AbstractThis article examines the relationship between the unconventional monetary policies and precious metal futures markets by employing an asymmetric conditional volatility specification. The results suggest that the Fed announcements that likely induced lower long‐term yields on average led to significant increases in the returns, while the effect of the ECB's unconventional monetary announcements is much weaker. Also, monetary policy surprises from both Fed and ECB have larger effects on gold return volatility. Moreover, while there are no asymmetric return responses to positive and negative surprises embedded in the monetary policy from the Euro zone, the return reactions of price returns to surprises in Fed's announcements exhibit asymmetry. Furthermore, our analysis shows that the volatility reactions are limited to negative surprises. Finally, we find evidence that the response of gold price changes to monetary announcements by the Fed becomes weaker in the presence of monetary policy uncertainty. We show also that the response of precious metal returns hinges on the sign of the unexpected component of Fed announcements.

Asymmetries in Volatility: An Empirical Study for the Peruvian Stock and Forex Markets

Asymmetric autoregressive conditional heteroskedasticity (EGARCH) models and asymmetric stochastic volatility (ASV) models are applied to daily data of Peruvian stock and Forex markets for the period of 5 January 1998–30 December 2011. Following the approach developed in [Omori, Y, S Chib, N Shephard and J Nakajima (2007). Stochastic volatility with leverage: Fast likelihood inference. Journal of Econometrics, 140, 425–449], Bayesian estimation tools are used with Normal and [Formula: see text]-Student errors in both models. The results suggest the significant presence of asymmetric effects in both markets. In the stock market, negative shocks generate higher volatility than positive shocks. In the Forex market, shocks related to episodes of depreciation create higher uncertainty in comparison with episodes of appreciation. Thus, the Central Reserve Bank faces relatively major difficulties in its intention of smoothing Forex volatility in times of depreciation. The model with the best fit in both markets is the ASV model with Normal errors. The stock market returns have greater periods of volatility; however, both markets react to shocks in the economy, as they display similar patterns and have a significant correlation for the sample period studied.

Distributional characteristics of interday stock returns and their asymmetric conditional volatility: Firm-level evidence

This paper is a pioneering effort to jointly analyze the characteristics of interday distributions of stock returns and their asymmetric time varying volatility using firm-level data in local currency from an emerging stock market, namely, the Bourse Istanbul, for the period January 1996 to December 2015. Using a modified Threshold Generalized Autoregressive Conditional Heteroscedasticity-in-Mean [TGARCH(1,1)-M] model; these distributional characteristics statistically assess in a unique framework (i) the weak-form informational efficiency based on the stylized facts of day of the week effects on stock returns and their conditional volatility; (ii) volatility persistence and asymmetry in conditional volatility; and (iii) the conditional total risk–return relationship, and the impact of systematic risk as an asset pricing factor. It is found that at firm level there are statistically significant positive or negative day of the week effects on either stock returns or their conditional volatility, or both. However, for a sample of 120 firms, a full and cross-sectional analysis of the interday distributions does not lead to a systematic pattern of return differences across days of the week. The average volatility is found to be highest on Mondays and the lowest on Wednesdays. It is reported that – as a proxy of total risk in a mean–variance framework – the estimated conditional standard deviation does not have a significant impact on stock returns for the great majority of the sample firms. With reference to the total risk–return relationship and the asymmetry in volatility, there are no significant differences between the industrial and financial sector companies. It is reported that the systematic risk is always priced; and the results are highly significant with a high explanatory power. Volatility is decidedly persistent for all firms investigated; while, a significant asymmetry in the conditional volatility cannot be reported for most of the firms. Contributing to the existing literature as a first time analysis of firm-level distributional characteristics of interday stock returns and their asymmetric conditional volatility with an additional proper risk-impact investigation, the empirical results are of importance primarily for asset pricing and risk management research and practice.

Intraday and interday distribution of stock returns and their asymmetric conditional volatility: Firm-level evidence

This paper is a pioneering effort to jointly analyze the intraday and interday distribution of stock returns and their asymmetric time varying volatility using firm level data from an emerging stock market, namely, the Bourse Istanbul. The joint intraday and interday distribution analysis is based on the two trading sessions with a two-hour lunch break and the trading days of week. The asymmetric time varying volatility is based on the Threshold Generalized Autoregressive Conditional Heteroscedasticity-in-Mean [TGARCH(1,1)-M] model with systematic risk entering the return generating process. This is a unique framework to simultaneously test (i) the weak-form informational efficiency, (ii) the conditional total risk-return relationship and the systematic risk effects, and (iii) volatility persistence and asymmetry in volatility. The firm level intraday data sets are used for the period 1995 to 2015. Firstly, a strong result can be pronounced for a positive return effect for the second trading session of Thursdays followed by the second session of Fridays. The first session of Wednesdays is more associated with a stock price decline. The volatility is the highest in the second session of Mondays. Although the second session of Thursdays sees a volatility increase referring to a positive risk-return relationship, Fridays do not have any significant volatility increase. This can be seen as a true anomaly where we could observe higher returns with lower volatility. Secondly, it can be concluded that only the systematic risk is priced for the great majority of the selected firms. In addition, we cannot observe a significant asymmetry in the volatility in most cases. Finally, it is found that the financial companies have a significantly higher systematic risk than the industrial companies.

Corporate Governance and Indian Stock Market: A Study with Asymmetric Conditional Volatility Models

An efficient capital market needs improved quality as well as the quantity of information available to investors, which is possible by raising the principles of good corporate governance. Numerous studies showed its importance in the development of the stock market. To strengthen the trust of investors in companies Securities Exchange Board of India (SEBI) revised Clause 49 of the Listing Agreement of the Indian Stock Market which gets operationalized in October 2014. This study investigates the impact of revised Clause 49 on Indian stock market volatility using asymmetric conditional volatility models. For analysis, author considered return pattern of five indexes; S P Pre-CG regulation period from January 1st, 2006 to September 30th, 2014 and Post-CG regulation period from October 1st, 2014 to March 31st, 2017. Results show that the revised Clause 49, 2013 helps to build the trust of investors for Small-Cap companies in Indian Stock Market.

Asymmetric Conditional Volatility Estimation of Stock Prices in India

This study was aimed at estimating the conditional variance of stock price returns in India using PGARCH model. Ten companies with highest market capitalization were chosen from National Stock Exchange (NSE). The data for a period from 2006 to 2016 were collected from PROWESS database. Volatility clustering feature was found in the estimated volatility of stock returns. The stock return volatility was also found with leverage or asymmetric effect. The residual diagnostic test (ARCH LM test) after conditional volatility estimation confirms the efficiency of PGARCH model.

Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes

The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation. For this purpose, we use an underlying vector random coefficient autoregressive process, for which we show the equivalent representation for the asymmetric multivariate conditional volatility model, to derive asymptotic theory for the quasi-maximum likelihood estimator. As an extension, we develop a new multivariate asymmetric long memory volatility model, and discuss the associated asymptotic properties.

Can asymmetric conditional volatility imply asymmetric tail dependence?

In this article, we investigate two types of asymmetries, that is, the asymmetry of conditional volatility and the asymmetry of tail dependence in the crude oil markets. We employ the two different sample datasets in which each dataset covers the time period of stable and unstable oil prices, individually. A variety of different copulas and three asymmetric GARCH regression models are used in order to capture the two types of asymmetries. In particular, we extend the TBL-GARCH model proposed by Choi et al. (2012) to the asymmetric GARCH regression type model. The findings from the two different approaches are congruent, in that there is no asymmetry of tail dependence and no asymmetric conditional volatility in crude oil returns over the two different sample periods. Our study reconfirms the findings of Aboura and Wagner (2016) by showing that asymmetric conditional volatility relates to asymmetric tail dependence.

News and sovereign CDS markets: evidence from the Euro area

The purpose of this paper is to trace the impact of news announcements on sovereign five-year credit default swaps (CDS) spreads of four distressed countries in the euro area (Ireland, Italy, Portugal, and Spain) with daily data spanning the period May 17, 2012 to May 23, 2014. We apply an analysis based on asymmetric conditional volatility modelling methodology. The empirical analysis provides interesting evidences. The paper finds that the influence of country-specific news variable on the dynamics of the sovereign CDS spreads was confirmed. Moreover, when considering that news originated only from distressed countries, the domestic and cross-border effects of news are restricted to unfavourable news. In particular, we find that more bad news in one country leads to an increase in its own CDS spreads and those associated with other countries confirming thus the presence of a news spillover phenomenon. In contrast, we make evidence of the spillovers of good news from the non-distressed countries onto the other countries. Clearly, the sovereign CDS markets' responses indicate that the split into bad and good news has largely been done correctly.

Impact of News on Indian Stock Market: A Periodic Study with Asymmetric Conditional Volatility Models

The article analyzes the leverage effect for Indian stock market. The effect of good and bad news on volatility during the period of growth, recession and the post-recession period is investigated taking American recession of 2008–2009 as the benchmark. Bombay Stock Exchange (BSE) 30 index was used as the proxy for Indian stock market. Using suitable asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models based on information criterions to study the asymmetry for the period and sub-periods, it was found that Indian stock market reacts differently to positive and negative news confirming leverage effect in all periods considered. However, the leverage effect was seen prominent during the period of gloom than other periods shown by the news impact curve.

Media sentiment and CDS spread spillovers: Evidence from the GIIPS countries

This study explores the role of newswire messages during the European debt crisis. It quantifies how this news metric, revealed by statements recorded by newspapers articles, affects CDS spillovers across five European countries with sovereign debt problems and strict bail-out programs, i.e. Greece, Ireland, Italy, Portugal, and Spain with daily data spanning the period 2009–2012. Using panel ARDL and asymmetric conditional volatility modeling methods, the empirical findings document that the news variable generates significant spillover effects across the underlined CDS markets. These findings cast a cloudy doubt on the effectiveness of economic modeling on which CDS spreads are based.

On the invertibility of EGARCH(p, q)

ABSTRACTOf the two most widely estimated univariate asymmetric conditional volatility models, the exponential GARCH (or EGARCH) specification is said to be able to capture asymmetry, which refers to the different effects on conditional volatility of positive and negative effects of equal magnitude, and leverage, which refers to the negative correlation between the returns shocks and subsequent shocks to volatility. However, the statistical properties of the (quasi-)maximum likelihood estimator (QMLE) of the EGARCH(p, q) parameters are not available under general conditions, but only for special cases under highly restrictive and unverifiable sufficient conditions, such as EGARCH(1,0) or EGARCH(1,1), and possibly only under simulation. A limitation in the development of asymptotic properties of the QMLE for the EGARCH(p, q) model is the lack of an invertibility condition for the returns shocks underlying the model. It is shown in this article that the EGARCH(p, q) model can be derived from a stochastic process, for which sufficient invertibility conditions can be stated simply and explicitly when the parameters respect a simple condition.11Using the notation introduced in part 2, this refers to the cases where α ≥ |γ| or α ≤ − |γ|. The first inequality is generally assumed in the literature related to the invertibility of EGARCH. This article provides (in the Appendix) an argument for the possible lack of invertibility when these conditions are not met. This will be useful in reinterpreting the existing properties of the QMLE of the EGARCH(p, q) parameters.

Moments of standardized Fernandez–Steel skewed distributions: Applications to the estimation of GARCH-type models

We provide general expressions for obtaining raw, absolute and conditional moments for a standardized version of the class of skewed distributions proposed by Fernandez and Steel (1998). We show that these expressions are readily programmable in addition of greatly reducing the computational cost. We discuss several applications that are relevant for the purpose of estimating asymmetric conditional volatility models under skewed distributions.

Moments of Standardized Fernandez-Steel Skewed Distributions: Applications to the Estimation of GARCH-Type Models

We provide general expressions for obtaining raw, absolute and conditional moments for a standardized version of the class of skewed distributions proposed by Fernandez and Steel (1998). We show that these expressions are readily programmable in addition of greatly reducing the computational cost. We discuss several applications that are relevant for the purpose of estimating asymmetric conditional volatility models under skewed distributions.

A One Line Derivation of EGARCH

One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH (or EGARCH) specification. In addition to asymmetry, which captures the different effects on conditional volatility of positive and negative effects of equal magnitude, EGARCH can also accommodate leverage, which is the negative correlation between returns shocks and subsequent shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator of the EGARCH parameters are not available under general conditions, but rather only for special cases under highly restrictive and unverifiable conditions. It is often argued heuristically that the reason for the lack of general statistical properties arises from the presence in the model of an absolute value of a function of the parameters, which does not permit analytical derivatives, and hence does not permit (quasi-) maximum likelihood estimation. It is shown in this paper for the non-leverage case that: (1) the EGARCH model can be derived from a random coefficient complex nonlinear moving average (RCCNMA) process; and (2) the reason for the lack of statistical properties of the estimators of EGARCH under general conditions is that the stationarity and invertibility conditions for the RCCNMA process are not known.

CONDITIONAL VOLATILITY ASYMMETRY OF BUSINESS CYCLES: EVIDENCE FROM FOUR OECD COUNTRIES

Most studies of business cycle exclude the dimension of asymmetric conditional volatility. In this paper, we propose three bivariate asymmetric GARCH models to capture the properties of conditional volatility and time-varying conditional correlations of business cycle indicators in four OECD countries. Our study extends the constant conditional correlation framework proposed by Bollerslev (1990) and the time-varying conditional correlation approach by Tse and Tsui (2002), respectively. Using indices of industrial production as proxies for business cycles indicators, we detect statistically significant evidence of asymmetric conditional volatility in the UK and US. Additionally, we find that the conditional correlations are significantly time-varying, and that the strength of varying correlations may be linked to the degree of economic integration between the countries.

Modeling the Volatility in Short and Long Haul Japanese Tourist Arrivals to New Zealand and Taiwan

Long haul international travel is more expensive than short haul international travel. This paper estimates the effects of short and long haul volatility (or risk) in monthly Japanese tourist arrivals to Taiwan and New Zealand, respectively. The primary purpose in attracting international tourists is to encourage international tourist spending. Variations in international tourist arrivals are equivalent to variations in international tourism expenditures if the rate of growth in tourism spending is constant, as is likely. In this sense, international tourist arrivals are equivalent to the prices of financial assets in that changes in both lead to positive or negative financial returns. We use symmetric and asymmetric conditional volatility models that are commonly used in financial econometrics, namely the GARCH (1,1), GJR (1,1) and EGARCH (1,1) models. The data series are for the period January 1997 to December 2007. The volatility estimates for the monthly growth in Japanese long haul tourists to New Zealand and short haul tourists to Taiwan indicate that the former has an asymmetric, as well as a leverage, effect on risk, whereby negative shocks increase volatility but positive shocks of similar magnitude decrease volatility, while the latter has a symmetric effect. These empirical results seem to be similar to a wide range of financial stock market returns, so that the models used in financial econometrics are also applicable to international tourist arrivals. The empirical results may be used to recommend whether long haul or short haul international tourists should be encouraged for Taiwan and New Zealand.

Modelling and forecasting daily international mass tourism to Peru

Peru is a South American country that is divided into two parts by the Andes Mountains. The rich historical, cultural and geographic diversity has led to the inclusion of ten Peruvian sites on UNESCO's World Heritage List. For the potentially negative impacts of mass tourism on the environment, and hence on future international tourism demand, to be managed appropriately require modelling growth rates and volatility adequately. The paper models the growth rate and volatility (or the variability in the growth rate) in daily international tourist arrivals to Peru from 1997 to 2007. The empirical results show that international tourist arrivals and their growth rates are stationary, and that the estimated symmetric and asymmetric conditional volatility models all fit the data extremely well. Moreover, the estimates resemble those arising from financial time series data, with both short and long run persistence of shocks to the growth rate in international tourist arrivals.