Discussions on the spurious hyperbolic memory in the conditional variance and a new model

This paper studies the spurious hyperbolic memory in the conditional variance caused by the Markov Regime-Switching GARCH (MRS-GARCH) process. We firstly propose an illustrative cause of this spuriousness and provide simulation evidence. An MRS Hyperbolic GARCH (MRS-HGARCH) model is then developed to successfully address it. Related statistical properties including the stationarity conditions and asymptotic behaviours of the maximum likelihood estimators of the MRS-HGARCH process are also investigated. An empirical study of the S&P 500 and TOPIX indexes returns is then conducted which demonstrates that our MRS-HGARCH model can provide a more reliable estimator of the hyperbolic-memory parameter and outperform both the HGARCH and MRS-GARCH models.     

Environmental,social, and governance practices and perceived tail risk

Using the implied volatility smirk on individual equity securities to measure perceived tail risk, we find that better environmental, social and governance (ESG) practices significantly reduce ex-ante expectations of a left-tail event. Our findings are robust to using multiple model specifications and to adjusting for potential endogeneity concerns. We also show that, while practices in each ESG pillar are important in reducing perceived tail risk, the environmental pillar plays the most important role. Our results indicate that investors consider strong ESG practices to be insurance against left-tail events rather than wasteful investment borne out of managers’ own values or self-interest.     

A product-centric data mining algorithm for targeted promotions

Targeted promotions in retail are becoming increasingly popular, particularly in UK grocery retail sector, where competition is stiff and consumers remain price sensitive. Given this, a targeted promotion algorithm is proposed to enhance the effectiveness of promotions by retailers. The algorithm leverages a mathematical model for optimising items to target and fuzzy c-means clustering for finding the best customers to target. Tests using simulations with real life consumer scanner panel data from the UK grocery retailer sector show that the algorithm performs well in finding the best items and customers to target whilst eliminating “false positives” (targeting customers who do not buy a product) and reducing “false negatives” (not targeting customers who could buy a product). The algorithm also shows better performance when compared to a similar published framework, particularly in handling “false positives” and “false negatives”. The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields.     

Economic uncertainty,oil prices,hedging and U.S. stock returns of the airline industry

This paper examines the impacts of economic policy uncertainty and oil price shocks on stock returns of U.S. airlines using both industry and firm-level data. Our empirical approach considers a structural vector-autoregressive model with variables recognized to be important for airline returns including jet fuel price volatility. Empirical results confirm that oil price increase, economic uncertainty and jet fuel price volatility have significantly adverse effect on real stock returns of airlines both at industry and at firm level. In addition, we also find that hedging future fuel purchase has statistically positive impact on the smaller airlines. Our results suggest policy implications for practitioners, managers of airline industry and commodity investors.     

The role of oil futures intraday information on predicting US stock market volatility

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.     

Profiling high-frequency equity price movements in directional changes

Market prices are traditionally sampled in fixed time intervals to form time series. Directional change (DC) is an alternative approach to record price movements. Instead of sampling at fixed intervals, DC is data driven: price changes dictate when a price is recorded. DC provides us with a complementary way to extract information from data. It allows us to observe features that may not be recognized in time series. The argument is that time series and DC-based analysis complement each other. With data sampled at irregular time intervals in DC, however, some of the time series indicators cannot be used in DC-based analysis. For example, returns must be time adjusted and volatility must be amended accordingly. A major objective of this paper is to introduce indicators for profiling markets under DC. We analyse empirical high-frequency data on major equities traded on the UK stock market, and through DC profiling extract information complementary to features observed through time series profiling.     

Quadratic Hawkes processes for financial prices

We introduce and establish the main properties of QHawkes (‘Quadratic’ Hawkes) models. QHawkes models generalize the Hawkes price models introduced in Bacry and Muzy [Quant. Finance, 2014, 14(7), 1147–1166], by allowing feedback effects in the jump intensity that are linear and quadratic in past returns. Our model exhibits two main properties that we believe are crucial in the modelling and the understanding of the volatility process: first, the model is time-reversal asymmetric, similar to financial markets whose time evolution has a preferred direction. Second, it generates a multiplicative, fat-tailed volatility process, that we characterize in detail in the case of exponentially decaying kernels, and which is linked to Pearson diffusions in the continuous limit. Several other interesting properties of QHawkes processes are discussed, in particular the fact that they can generate long memory without necessarily being at the critical point. A non-parametric fit of the QHawkes model on NYSE stock data shows that the off-diagonal component of the quadratic kernel indeed has a structure that standard Hawkes models fail to reproduce. We provide numerical simulations of our calibrated QHawkes model which is indeed seen to reproduce, with only a small amount of quadratic non-linearity, the correct magnitude of fat-tails and time reversal asymmetry seen in empirical time series.     

24-Hour realized volatilities and transatlantic volatility interdependence

This paper proposes an innovative econometric approach for the computation of 24-h realized volatilities across stock markets in Europe and the US. In particular, we deal with the problem of non-synchronous trading hours and intermittent high-frequency data during overnight non-trading periods. Using high-frequency data for the Euro Stoxx 50 and the S&P 500 Index between 2003 and 2011, we combine squared overnight returns and realized daytime variances to obtain synchronous 24-h realized volatilities for both markets. Specifically, we use a piece-wise weighting procedure for daytime and overnight information to take into account structural breaks in the relation between the two. To demonstrate the new possibilities that our approach opens up, we use the new 24-h volatilities to estimate a bivariate extension of Corsi et al.’s [Econom. Rev., 2008, 27(1–3), 46–78] HAR-GARCH model. The results suggest that the contemporaneous transatlantic volatility interdependence is remarkably stable over the sample period.     

引入国债期货合约能否发挥现货市场稳定效应?——基于中国金融周期的研究视角

我国国债期货市场能否发挥稳定现货市场功能,金融周期风险是否会改变国债期货市场对现货市场波动的影响,是投资者实施风险管理和监管部门构建市场稳定机制的重要依据。本文通过信息传递机制和交易者行为两个维度探析国债期货市场发挥稳定功能的微观机理,分析金融周期风险对衍生工具稳定功能的影响,解析引入国债期货合约能否缓解金融周期波动对国债市场冲击,同时关注我国国债期货交易机制改进与现券波动关系。研究发现:(1)我国国债期货市场已实现抑制现货市场波动的功能,金融周期风险会引发现货价格波动,国债期货市场能够降低金融周期的波动冲击;(2)改善现货市场深度和套保交易是国债期货市场发挥稳定功能的微观路径,国债期货市场增进国债预期交易量流动性、减弱非预期交易量干扰,金融周期低波动区间套保交易稳定作用受到抑制;(3)国债期货投机交易和波动溢出效应助长现货市场波动,正负期现基差对国债波动影响具有非对称特征。     

The influence of intraday seasonality on volatility transmission pattern

Using data on a five-minute interval basis, this article analyses the effects of intraday seasonality on volatility transmission between the spot and futures markets of the CAC40, DAX30 and FTSE100. Remarkable differences in the impulse response analysis and in the dynamic and directional measurement of volatility spillovers are encountered depending on whether the intraday periodic component is considered. Thus, the convenience of removing intraday seasonality seems to be critical to reduce the risk of spurious causality when employing high-frequency data in volatility transmission. Moreover, the impact of market microstructure noise seems negligible when using an optimal frequency of observations.