Narrow Results

Stock market reactions to financial reports have been extensively studied in previous years and for various markets. However, not much research has been conducted regarding the reaction of global financial markets to integrated reporting. This study examines the market reaction to the publication of integrated reports for a sample of 316 global companies for the reporting year 2018 by using an event study methodology. The results of the applied event study indicate significant cumulative average abnormal returns (CAARs) after the publication date. To ensure robust estimation results, we use a modern asset pricing model, namely, the three-factor model according to Fama and French (1993). For comparability purposes, we also estimate the average cumulative abnormal returns using a market-adjusted model, a capital asset pricing model (CAPM), and a Fama-French model taking generalized autoregressive conditional heteroskedasticity (GARCH effects) into account. In addition, a cross-sectional analysis is conducted. We find a significant positive CAAR on days one to four after the publication day of the integrated report compared to a negative CAAR for financial information disclosure. Our results suggest that investors react to information provided in the integrated report and that they react differently to the release of integrated report information than to financial information. Furthermore, our cross-sectional analysis confirms that companies with a significant positive CAAR around the publication date show certain characteristics. It was found that European companies have a higher likelihood to experience a stronger significant positive market reaction to their integrated report publication. It was also found that firms in the consumer defensive, financial, industrial, and real estate sectors are more likely to experience a positive market reaction. No significant differences were found for companies of a larger size or with a higher profit margin. Ultimately, this confirms that integrated reporting affects company value. This is the first event study for a multi-country sample applying multi-factor models for nonfinancial disclosure event studies including a cross-sectional analysis.

In this paper, we review some of the properties of financial and other spatio-temporal time series generated from coupled map lattices, GARCH(1,1) processes and random processes (for which analytical results are known). We use the Hurst exponent (R/S analysis) and detrended fluctuation analysis as the tools to study the long-time correlations in the time series. We also compare the eigenvalue properties of the empirical correlation matrices, especially in relation to random matrices.

Analyzing the statistical features of fluctuation is remarkably significant for financial risk identification and measurement. In this study, the asymmetric detrended fluctuation analysis (A-DFA) method was applied to evaluate asymmetric multifractal scaling behaviors in the Shanghai and New York gold markets. Our findings showed that the multifractal features of the Chinese and international gold spot markets were asymmetric. The gold return series persisted longer in an increasing trend than in a decreasing trend. Moreover, the asymmetric degree of multifractals in the Chinese and international gold markets decreased with the increase in fluctuation range. In addition, the empirical analysis using sliding window technology indicated that multifractal asymmetry in the Chinese and international gold markets was characterized by its time-varying feature. However, the Shanghai and international gold markets basically shared a similar asymmetric degree evolution pattern. The American subprime mortgage crisis (2008) and the European debt crisis (2010) enhanced the asymmetric degree of the multifractal features of the Chinese and international gold markets. Furthermore, we also make statistical tests for the results of multifractatity and asymmetry, and discuss the origin of them. Finally, results of the empirical analysis using the threshold autoregressive conditional heteroskedasticity (TARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models exhibited that good news had a more significant effect on the cyclical fluctuation of the gold market than bad news. Moreover, good news exerted a more significant effect on the Chinese gold market than on the international gold market.

In this paper we investigate the properties of daily returns arising from inventory effects. We therefore use the well established framework of inventory-based models from market microstructure theory. It is shown using simulation studies that from this model daily returns exhibit excess volatility, negative first-order autocovariances and the volatility has a positive first-order autocovariance, which is consistent with a GARCH-process. An empirical investigation shows that a substantial part of the properties of daily returns in stock market data can be explained by inventory effects.

Monte Carlo estimators of sensitivity indices and the marginal density of the price dynamics are derived for the Hobson-Rogers stochastic volatility model. Our approach is based mainly upon the Kolmogorov backward equation by making full use of the Markovian property of the dynamics given the past information. Some numerical examples are presented with a GARCH-like volatility function and its extension to illustrate the effectiveness of our formulae together with a clear exhibition of the skewness and the heavy tails of the price dynamics.

The stationary distribution of a GARCH(1,1) process has a power law decay, under broadly applicable conditions. We study the change in the exponent of the tail decay under temporal aggregation of parameters, with the distribution of innovations held fixed. This comparison is motivated by the fact that GARCH models are often fit to the same time series at different frequencies. The resulting models are not strictly compatible so we seek more limited properties we call forecast consistency and tail consistency. Forecast consistency is satisfied through a parameter transformation. Tail consistency leads us to derive conditions under which the tail exponent increases under temporal aggregation, and these conditions cover most relevant combinations of parameters and innovation distributions. But we also prove the existence of counterexamples near the boundary of the admissible parameter region where monotonicity fails. These counterexamples include normally distributed innovations.

This paper examines the equilibrium implications of the Expectations Hypothesis of term structure to different maturities of high-grade Australian dollar denominated Eurobonds and Australian Government bonds (AGBs) using the Canonical Cointegrating Regression (CCR) technique developed by Econometrica 60 (1992) 119. Our findings provide evidence only for equilibrium relationships between each group of bonds based on credit class, but not between any of the subsets of AGBs and the Eurobonds. Furthermore, the error correction model supports theory with the most liquid, long-term 10-year AGB driving the AGB term structure, with short-term yields adjusting to movements in the long-run yields, though the opposite is true for Eurobonds. The lesson for markets is to simplify the risk management task.

Managers are advised to treat portfolios of equivalent credit class separately for hedging and risk management.

In 1994–1995, Jardine Matheson Group delisted its five major group members from the Stock Exchange of Hong Kong, so that their trading was transferred to Singapore. We document that the trading volume of these five stocks fell after the delisting, and that they became less correlated with the Hong Kong market. We use a multivariate GARCH framework, which also allows us to present the correlations dynamically. We argue that this is evidence in favor of international market segmentation, since the delisting was not associated with a change in the business strategy of the Group.

We investigate the effectiveness of two recent regulatory policy changes on market efficiency in the Chinese A- and B-share markets. Overall, the opening of the B-share market to domestic Chinese investors and the limited opening of the A-share market to foreign investors increase market efficiency. The opening of the B-share market significantly reduces the price differential between A- and B-shares. Furthermore, there is no longer feedback in returns between the two markets in recent years. Our results provide evidence that there is no detrimental effect to market efficiency by integrating Chinese investors to international markets and foreign investors to the Chinese stock markets.

In this study, the impact of volatility regime shifts on volatility persistence and hedge ratio estimation is determined for four major currencies using an iterated cumulative sums of squares (ICSS)-GARCH model. Employing a standard GARCH (1,1) model as the benchmark, within-sample results demonstrate that the inclusion of volatility shifts substantially reduces volatility persistence and the significance of the ARCH and GARCH coefficients. In terms of hedging effectiveness, the ICSS-GARCH model outperforms the standard GARCH model for all four currencies. In comparison to two constant volatility models, the standard GARCH model yields the lowest performance, whereas the ICSS-GARCH model performs at least as well as these models. In out-of-sample analysis, the GARCH model provides substantial variance reductions relative to the constant volatility models. Moreover, the ICSS-GARCH model yields positive variance reductions relative to all competing models, including the standard GARCH model. The results suggest that in cases where dynamic hedging is important, sudden shifts in volatility should not be ignored.

Using returns of 4,916 stocks from 22 developed countries and 15 developing countries, this study examines the relative magnitude of conditional volatility and the international market systematic risk of stock prices in countries at different developmental stages and in various geographical areas. Consistent with the finding of Bekaert et al. (2008), the results of non-parametric Mann-Whitney tests suggest that the stock prices in emerging markets are riskier than the ones in developed countries, measured by both conditional volatility and global beta. Our empirical findings also support the geographical variation in stock risks. Specifically, the equity values in Southeast Asia, South Europe, and Latin America are more volatile than the rest of the world. Similar results can be found in the country-level tests. The time-series analysis suggests that the stock returns in high risk countries tend to be less volatile but the conditional volatility of stock return in less risky countries leans to increase.

Based upon the theory of the "arrival of news", the main purpose of this paper is to investigate the impact of non-trading periods on the measurement of volatility for the S&P 500, FTSE 100, and TAIEX indices. Using an adaptation of the GJR (1,1) model, we find that both weekday holiday periods and half-day trading periods have significant impacts on the estimation of volatility for the S&P 500 and FTSE 100 indices. On the other hand, weekends have significant impacts for the TAIEX index. Our findings imply that for the UK and US markets, much less relevant information is produced during weekends, while more relevant information continues to be produced during other types of non-trading periods. However, the weekend volatility of the Taiwan market is specially driven because the US macro news is announced on Fridays and the trading time of the US market is later than that of the Taiwan market without any overlapping.

The model in this paper is similar to Brailsford and Faff (1997), using a conditional CAPM model with the GARCH-M framework, but with a significant additional dummy term (in the conditional mean of the share return) that will help explain the models better in both economic and statistical sense. The relatively simpler asymmetric model in this paper is compatible to other more complex asymmetric models and hence should be easier to model and explain for practical purposes. The model in this paper is also a more effective model, in both economical and statistical terms, as compared to some other models in the GARCH family as it captures the asymmetric effect in the modeling process in both the conditional first and second moments. The findings in this paper have contributed in re-evaluating the nature and process of time varying behavior of time series of stock returns and will provide researchers and practitioners additional options and incentives to explore for future research. We have also provided statistical and practical reasons to support these findings.

In this paper, we compare the performance of Islamic stock indices (ISI) and conventional stock indices (CSI) from FTSE, DJ, MSCI, S&Ps and Jakarta series using common risk-return metrics. The sample consists of 64 ISI and CSI, and covers the period from 2002 to 2017. The majority of the stock indices are from the Pacific Rim countries’ stock markets. Additionally, we employ the GARCH-M model to examine the impact of past volatility on spot returns. Findings suggest that the ISI are less sensitive to the average market movements compared to the CSI, but surprisingly offer similar raw returns suggesting primary support for the low risk-high return paradox. On further examination, results reveal that M², Omega, Sharpe and Treynor measures indicate that ISI underperform CSI while Jensen’s alpha and Sortino ratio put ISI ahead of CSI. Moreover, findings show that pre-crisis winners (CSI) were losers during the 2008 crisis but subsequently recovered and ended up with higher returns than ISI. Findings also show that the previous volatility of stock returns can be potentially used for predicting future returns.

It is shown empirically that likelihood function of the GARCH is multi-modal. Hence, the maximum likelihood estimates at local and global maxima will be quantitatively different. Therefore, it is important to start an estimation method with consistent starting value that converge to global maxima. This study compares two estimation methods, BFGS and DE, on the basis of simulation and surface constructed by changing the value of GARCH $(1,1)$ model. DE is superior and consistent throughout the surface, and across distributions. PSX is used as real-world application and it has been found that the estimates obtained from DE are best and unbiased.

The paper provides an updated evidence of the linkage between stock market and macroeconomic factors in Pakistan. The sample period is from January 2011 to November 2017. Macroeconomic variables used are money supply, exchange rate, treasury bill rate, inflation and industrial production. Generalized autoregressive conditional heteroscedasticity (GARCH) models have been used to examine the impact of macroeconomic factors on stock market return and stock market volatility. Findings suggest that macroeconomic factors have an impact on stock market volatility. The fluctuations in inflation and money supply negatively influence the volatility of stock market returns. In contrast, industrial production positively affects the fluctuations of stock market returns. The findings are important for shareholders, investors, regulatory authorities and policymakers.

The aim of this paper is to shed new light on hedging discrete volatilities, in particular when using the generalized autoregressive conditional heteroskedasticity (thereafter GARCH) model. Despite its elegance, GARCH does not account for (i) correlation coefficients of debt and equity, (ii) equity parameter, (iii) risk premium, (iv) interest rates, and (v) shocks-stock markets. The unaccounted listed parameters are included into the GARCH(1,1) and the paper inverts a new model, expanded GARCH, called the capitalized GARCH. The results show that the capitalized GARCH convergences in a similar manner to the GARCH(1,1) in modeling volatility of bonds, commodities, equities, and real estate indices.

This paper studies patterns of volatilities and their spillovers across six major segments of Indian financial market applying univariate GARCH model and estimating the Diebold and Yilmaz (DY) volatility spillover index. The study found increasing integration of financial market segments over time and that equity, bank index and corporate bond segments are net contributors while money, gsec and forex segments are net receivers of volatility. The study highlights the need for a healthy banking sector and the increasing significance of corporate bond market volatility and suggests segment specific policy responses to tackle financial market volatilities.

This paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk.

Since 2013, China has become the world’s largest gold producer and consumer. To gain the corresponding global pricing power in gold, many actions have been taken by China in recent years, including the International Board at Shanghai Gold Exchange, Shanghai-Hong Kong Gold Connect and Shanghai Gold Fix. Our work studies the dependence structure between China’s and international gold price and examines whether these moves are changing the dependence structure. We use GARCH-copula models to detect the dynamic dependence and tail dependence. The research period is set to contain the Financial Crisis in 2008, the dramatical plunge of gold price in 2013 and a series of black swan events in 2016. The empirical study shows that some event driven dependence structure breaks are statistically insignificant. And the time-varying Symmetrized Joe-Clayton copula is the best copula to model the dependence structure based on AIC value. Finally, an example of applications of this dependence structure is given by estimating the VaR of an equally weighted portfolio with a simulation-based method.