A novel cluster HAR-type model for forecasting realized volatility

This paper proposes a cluster HAR-type model that adopts the hierarchical clustering technique to form the cascade of heterogeneous volatility components. In contrast to the conventional HAR-type models, the proposed cluster models are based on the relevant lagged volatilities selected by the cluster group Lasso. Our simulation evidence suggests that the cluster group Lasso dominates other alternatives in terms of variable screening and that the cluster HAR serves as the top performer in forecasting the future realized volatility. The forecasting superiority of the cluster models are also demonstrated in an empirical application where the highest forecasting accuracy tends to be achieved by separating the jumps from the continuous sample path volatility process.     

Measuring the liquidity part of volume

Based on the concept that the presence of liquidity frictions can increase the daily traded volume, we develop an extended version of the mixture of distribution hypothesis model (MDH) along the lines of Tauchen and Pitts (1983) to measure the liquidity portion of volume. Our approach relies on a structural definition of liquidity frictions arising from the theoretical framework of Grossman and Miller (1988), which explains how liquidity shocks affect the way in which information is incorporated into daily trading characteristics. In addition, we propose an econometric setup exploiting the volatility–volume relationship to filter the liquidity portion of volume and infer the presence of liquidity frictions using daily data. Finally, based on FTSE 100 stocks, we show that the extended MDH model proposed here outperforms that of Andersen (1996) and that the liquidity frictions are priced in the cross-section of stock returns.     

Does high frequency algorithmic trading matter for non-AT investors?

The extant literature has typically measured the impact of high frequency algorithmic trading (HFT) on short term outcomes, in seconds or minutes. We focus on outcomes of concern for longer term non-algorithm investors. We find in some cases HFT increases volatility arising from news relating to fundamentals. Furthermore HFT is associated with the transmission of that volatility across industries, and that transmission is based on short term correlations. Finally, we find that the period since the introduction of algorithmic trading (AT) has seen increases in both the variances and covariances of return volatility in most industries. However increases in the variances has not been uniform in that it has fallen sharply in a few industries. The magnitudes are such that, overall, AT has coincided with reduced return volatility variance.     

Volatility equicorrelation: A cross-market perspective

This paper contains the first empirical application of the Dynamic Equicorrelation (DECO) model to a cross-market dataset composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. The originality of our approach consists of examining the volatility equicorrelations, by updating the concept of ‘volatility surprise’. We document that the average volatility equicorrelation across markets is around 15%, while being time-varying with regime shifts before/after September 2005 and with a low mean-reversion level.     

An investigation of return and volatility linkages among equity markets: A study of selected European and emerging countries

This paper investigates the linkages among equity returns (based on exchange traded funds, ETF) and transmission of volatilities in the following countries: Germany, Austria, Poland, Russia and Turkey. Multivariate Autoregressive Moving Averages (MARMA) and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methodologies are utilized. The findings include the existence of significant co-movement of returns among countries in the sample. Also, Turkish and Russian markets were found to be more volatile than Austria, Germany and Poland. However, volatilities in Russia and Turkey do not persist very long. Finally, there is strong evidence of volatility spillovers. All of the countries in the sample, with the exception of Turkey, experience volatility spillovers from other markets. The presence of spillovers among return series and persistence of volatilities are useful to investors interested in diversifying their portfolios and to traders/fund managers who are interested in maximizing returns.     

Information,market power,and price volatility

We consider demand function competition with a finite number of agents and private information. We show that any degree of market power can arise in the unique equilibrium under an information structure that is arbitrarily close to complete information. Regardless of the number of agents and the correlation of payoff shocks, market power may be arbitrarily close to zero (the competitive outcome) or arbitrarily large (so there is no trade). By contrast, price volatility is always lower than the variance of the aggregate shock across all information structures. Alternative trading mechanisms lead to very distinct bounds as a comparison with Cournot competition establishes.     

The impact of the French Tobin tax

We analyze the impact of the introduction of the French Tobin tax on the turnover and measures of the liquidity and volatility of the affected stocks with nonparametric tests on individual stocks, difference-in-difference tests and other robustness checks controlling for simultaneous month-of-the-year and size effects. Our findings indicate that the tax produces a significant reduction in turnover and volatility (measured in terms of stock price volatility and the high–low price range) and inconclusive effects on liquidity when the latter is evaluated under the two dimensions of the estimated bid–ask spread and the Amihud (2002) price impact ratio.     

Oil price shocks and volatility spillovers in the Nigerian sovereign bond market

This paper investigates volatility spillover in the Nigerian sovereign bond market arising from oil price shocks, using Vector Autoregressive Moving Average ‐ Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (VARMA‐AGARCH) model. The paper covers the period March 22, 2011 to April 14, 2016 and makes use of the daily data of the Nigerian Sovereign Bond, Brent oil and West Texas Intermediate (WTI), respectively. We endogenously and sequentially detect structural break points using the test of Bai and Perron (2003) framework. In order to accurately estimate the model, we modify it by incorporating the break points into the VARMA‐AGARCH model, a process which if ignored would lead to model misspecification. The results obtained demonstrate a significant cross‐market volatility transmission between oil and sovereign bond market with ample sensitivity to structural breaks. The study also computes optimum weight portfolio and hedge ratio both with and without structural breaks and results equally indicate sensitivity to structural breaks.     

Impact of interest rate surprises on Islamic and conventional stocks and bonds

This paper studies the impact of interest rate news surprises on Islamic and conventional stock and bond indices, using a dataset which covers interest rate announcements and forecasts, as well as stock and bond indices in three Islamic and eight non-Islamic countris. We find that interest rate surprises tend to have a smaller impact on the returns and volatility of Islamic than conventional bonds because Islamic bonds are structured to avoid explicit interest rates. However, interest rate surprises have about the same or bigger impact on the returns and volatility of Islamic relative to conventional stocks, despite the low amounts of cash and debt holdings of firms comprising Islamic stock indices.     

Islamic equity market integration and volatility spillover between emerging and US stock markets

The purpose of this paper is to study the conditional correlations across the US market and a sample of five Islamic emerging markets, namely Turkey, Indonesia, Pakistan, Qatar, and Malaysia. The empirical design uses MSCI (Morgan Stanley Capital International) Islamic equity index since it applies stringent restrictions to include companies. Indeed, two main restrictions must be met: (i) the business activity must be compliant with Shari’ah (i.e., Islamic law) guidelines and (ii) interest-bearing investments and leverage ratios should not exceed upper limits. Three models are used: multivariate GARCH BEKK, CCC, and DCC. The estimation results of the three models show that the US and Islamic emerging equity markets are weakly correlated over time. No sheer evidence supports that the US market spills over into the Islamic emerging equity markets. Besides interpreting the results in terms of weak market integration, the peculiar specificities of the Islamic finance industry and the admittance conditions to the MSCI Islamic equity index contribute to explaining them. Indeed, Islamic finance bans interest-bearing investments and imposes some rules, such as asset-backing, which has sizeable impacts on volatility spillover and shocks transmissions, alongside with the close linkage between real and financial sectors. These findings suggest that investors should take caution when investing in the Islamic emerging equity markets and diversifying their portfolios in order to minimize risk.