Narrow Results

This study attempts to examine the response of stock markets amid the COVID-19 pandemic on prominent stock markets of the BRICS nation and compare it with the 2008 financial crisis by employing the GARCH and EGARCH model. First, average and variance of stock returns are tested for differences before and after the pandemic, t-test and F-test were applied. Further, OLS regression was applied to study the impact of COVID-19 on the standard deviation of returns using daily data of total cases, total deaths, and returns of the indices from the date on which the first case was reported till June 2020. Second, GARCH and EGARCH models are employed to compare the impact of COVID-19 and the 2008 financial crisis on the stock market volatility by using the data of respective stock indices for the period 2005–2020. The results suggest that the increasing number of COVID-19 cases and reported death cases hurt stock markets of the five countries except for South Africa in the latter case. The findings of the GARCH and EGARCH model indicate that for India and Russia, the financial crisis of 2008 has caused more stock volatility whereas stock markets of China, Brazil, and South Africa have been more volatile during the COVID-19 pandemic. The study has practical implications for investors, portfolio managers, institutional investors, regulatory institutions, and policymakers as it provides an understanding of stock market behavior in response to a major global crisis and helps them in taking decisions considering the risk of these events.

Time series prediction is of primary importance in a variety of applications from several science fields, like engineering, finance, earth sciences, etc. Time series prediction can be divided in to two main tasks, point and interval estimation. Estimating prediction intervals, is in some cases more important than point estimation mainly because it indicates the likely uncertainty in the prediction process. Recently, the sieve bootstrap method has been successfully used in prediction of nonlinear time series. In this work, we study the performance of the prediction intervals based on the sieve bootstrap technique, which does not require the distributional assumption of normality as most techniques that are found in the literature. The construction of prediction intervals in the presence of different types of outliers is not robust from a distributional point of view, leading to an undesirable increase in the length of the prediction intervals. In the analysis of time series, it is common to have irregular observations that have different types of outliers. For this reason, we propose the construction of prediction intervals for returns based on the winsorized residual and bootstrap techniques for time series prediction. We propose a novel, simple and distribution free resampling technique for developing robust prediction intervals for returns and volatilities for GARCH models. The proposed procedure is illustrated by an application to known synthetic and real-time series.

Forecasting volatility is an essential task in the financial market, especially in portfolio optimization. To improve the prediction accuracy of the volatilities of assets we use a hybrid ANN-EGARCH model then combining with extreme value theory and Copula models to perform out-of-sample forecasting returns for six indices in Asia stock markets then we simulate one-day-ahead returns of these indices. We use EGARCH model to capture the leverage of return shocks due to COVID-19. Based on ANN-EGARCH-EVT-Copula models, we solve our portfolio optimization consisting of these six indices with different copula models. Using different performance measures to evaluate the efficiency of the models we show that under the Sharpe ratio and Sortino ratio the Gumbel copula gives better performance whereas with Average Drawdown and Max Drawdown measures, the Gaussian copula model is a best model for optimizing the portfolio.

We study high frequency Nikkei stock index series and investigate what certain wavelet transforms suggest in terms of volatility features underlying the observed returns process. Several wavelet transforms are applied for exploratory data analysis. One of the scopes is to use wavelets as a pre-processing smoothing tool so to de-noise the data; we believe that this procedure may help in identifying, estimating and predicting the latent volatility. Evidence is shown on how a non-parametric statistical procedure such as wavelets may be useful for improving the generalization power of GARCH models when applied to de-noised returns.

It has been empirically verified that the strength of dependence in stock markets usually rises with volatility. In this paper we exploit this stylized fact combined with local maximum likelihood estimation of copula models to analyze the dynamic joint behavior of series of financial log returns. Explanatory variables based on the estimated GARCH volatilities are considered as potential regressors for explaining the dynamics in the copula parameters. The proposed model can assess and discriminate how much of the strength of dependence is due just to the time-varying volatility. The final local-parametric estimates may be used to compute risk measures, to simulate portfolio behavior, and so on. We illustrate our methods using two American indexes. Results indicate that volatility does affect the strength of dependence. The in-sample Value-at-Risk based on the dynamic model outperforms those based on the empirical estimates.

The correlation structure amongst selected Asia-Pacific equity markets is examined using the Constant Correlation multivariate GARCH (CC-MGARCH) model, the Dynamic Conditional Correlation multivariate GARCH (DCC-MGARCH) model, and an Exponentially-Weighted Moving Average (EWMA) correlation measure. The markets of Australia, Hong Kong, Japan and Singapore are analyzed from 1990 to 2001 and dynamic nature of the correlation is captured and explained. We find that global as well as regional factors contribute to the correlation spikes. Extreme volatility does not necessarily result in extreme correlations between some markets and there is higher comovement between markets since the Asian financial crisis. We also find that despite common periods of high volatility, there is still economic justification for diversification within this region.

This paper investigates the behavior of stock returns and volatility in 10 emerging markets and compares them with those of developed markets under different measures of frequency (daily, weekly, monthly and annual) over the period January 1, 2002 to December 31, 2006. The ratios of mean return to volatility for emerging markets are found to be higher than those of developed markets. Sample statistics for stock returns of all emerging and developed markets indicate that return distributions are not normal and return volatility shows clustering. In most cases, GARCH (1, 1) specification is adequate to describe the stock return volatility. The significant lag terms in the mean equation of GARCH specification depend on the frequency of the return data. The presence of leverage effect in volatility behavior is examined using the TAR-GARCH model and the evidence indicates that is not present across all markets under all measures of frequency. Its presence in different markets depends on the measure of frequency of stock return data.

In order to improve the forecasting accuracy of the volatilities of the markets, we propose the hybrid models based on artificial neural networks with multi-hidden layers in this paper. Specifically, the hybrid models are built using the estimated volatilities obtained from GARCH family models and Google domestic trends (GDTs) as input variables. We further carry out many experiments varying the number of layers and activation functions to obtain the accurate hybrid model for forecasting volatility. The proposed models are applied to forecast weekly and monthly volatilities of S&P 500 index to verify their accuracy. The performance comparison results show that the hybrid models with GDTs outperform clearly the predicted results with GARCH family models and the hybrid models without GDTs in forecasting the volatility of actual market. We also provide the experiment results with graphs to illustrate the efficiency of models.

This paper explores the relationship between volume and volatility in the Australian Stock Market in the context of a generalized autoregressive conditional heteroskedasticity (GARCH) model. In contrast to other studies who only examine the interaction of GARCH and volume effects on a small number of stocks, we examine these effects on the entire available data for the Australian All Ordinaries Index. We also emphasize on the impact of firm size and trading volume. Our results indicate that GARCH model testing and estimation is impacted by firm size and trading volume. Specifically, our analysis produces the following major findings. First, generally, daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns. Second, the actively traded stocks which may have a larger number of information arrivals per day have a larger impact of volume on the variance of daily returns. Third, we find that low trading volume and small firm lead to a higher persistence of GARCH effects in the estimated models. Fourth, unlike the elimination effect for the top most active stocks, in general, the elimination of both autoregressive conditional heteroskedasticity (ARCH) and GARCH effects by introducing the volume variable on all other stocks on average is not as much as that for the top most active stocks. Fifth, the elimination of both ARCH and GARCH effects by introducing the volume variable is higher for stocks in the largest volume and/or the largest market capitalization quartile group. Our findings imply that the earlier findings in the literature were not a statistical fluke and that, unlike most anomalies, the volume effect on volatility is not likely to be eliminated after its discovery. In addition, our findings reject the pure random walk hypothesis for stock returns.

The financial crisis that erupted during 2007–2009 brought to light the fact that systemically important financial institutions (SIFIs) can transmit risk to other financial institutions through risk spillovers that continue to propagate and spread throughout the financial system. At the same time, several widely used risk measures still underestimate systemic risk and spillovers, which implies that standard measures of risk should be modified to measure risk exposures more efficiently. This chapter aims to discover the risk spillover effects of online finance, using a GARCH-CoVaR model for six representative Chinese securities firms. By comparing the risk spillover effects using %CoVaR, it was determined that the risk spillover of Internet finance to securities firms varies by firm type and firm size. In particular, the risk spillover from Internet finance to Internet brokerage firms was higher, while the risk spillover to traditional brokerage firms was lower, with larger brokerage firms having a higher risk spillover than smaller brokerage firms.

In this chapter, we examine the integration of European government bond markets using daily returns over the 1998–2003 period to assess the time-varying level of financial integration. We find evidence of strong contemporaneous and dynamic linkages between the Euro zone bond markets with that of Germany. However, there is much weaker evidence outside of the Euro zone for the three new EU markets of the Czech Republic, Hungary and Poland, and the UK. In general, the degree of integration for these markets is weak and stable, with little evidence of further deepening despite the increased political integration associated with further enlargement of the EU.