Predictability of the Realised Volatility of International Stock Markets Amid Uncertainty Related to Infectious Diseases

In the context of the great turmoil in the financial markets caused by the COVID-19 pandemic, the predictability of daily infectious diseases-related uncertainty (EMVID) for international stock markets volatilities is examined using heterogeneous autoregressive realised variance (HAR-RV) models. A recursive estimation approach in the short-, medium- and long-run out-of-sample predictability is considered and the main findings show that the EMVID index plays a significant role in forecasting the volatility of international stock markets. Furthermore, the results suggest that the most vulnerable stock markets to EMVID are those in Singapore, Portugal and The Netherlands. The implications of these results for investors and portfolio managers amid high levels of uncertainty resulting from infectious diseases are discussed.

Modelling and forecasting stock volatility and return: a new approach based on quantile Rogers–Satchell volatility measure with asymmetric bilinear CARR model

This paper proposes quantile Rogers–Satchell (QRS) measure to ensure robustness to intraday extreme prices. We add an efficient term to correct the downward bias of Rogers–Satchell (RS) measure and provide scaling factors for different interquantile range levels to ensure unbiasedness of QRS. Simulation studies confirm the efficiency of QRS measure relative to the intraday squared returns and RS measures in the presence of extreme prices. To smooth out noises, QRS measures are fitted to the CARR model with different asymmetric mean functions and error distributions. By comparing to two realised volatility measures as proxies for the unobserved true volatility, results from Standard and Poor 500 and Dow Jones Industrial Average indices show that QRS estimates using asymmetric bilinear mean function provide the best in-sample model fit based on two robust loss functions with heavier penalty for under-prediction. These fitted volatilities are then incorporated into return models to capture the heteroskedasticity of returns. Model with a constant mean, Student-t errors and QRS estimates gives the best in-sample fit. Different value-at-risk (VaR) and conditional VaR forecasts are provided based on this best return model. Performance measures including Kupiec test for VaRs are evaluated to confirm the accuracy of the VaR forecasts.

Stand Still and Do Nothing: COVID-19 and Stock Returns and Volatility

We examine the intraday returns and volatility in the US equity market amid the COVID-19 pandemic crisis. Our empirical results suggest an increase in volatility over time with mostly negative returns and higher volatility in the last trading session of the day. Our Univariate analysis reveals structural break(s) since the first trading halt in March 2020 and that failure to account for this may lead to biased and unstable conditional estimates. Allowing for time-varying conditional variance and conditional correlation, our dynamic conditional correlation tests suggest that COVID-19 cases and deaths are jointly related to stock returns and realised volatility.

Sailing in rough waters: examining volatility of fMRI noise

1.AbstractBackgroundFunctional resonance magnetic imaging (fMRI) noise is usually assumed to have constant volatility. However this assumption has been recently challenged in a few studies examining heteroscedasticity arising from head motion and physiological noise. However, to our knowledge no studies have studied heteroscedasticity in scanner noise. Thus the aim of this study was to estimate the smoothness of fMRI scanner noise using latest methods from the field of financial mathematics.MethodsA multi-echo fMRI scan was performed on a phantom using two 3 tesla MRI units. The echo times were used as intra-time point data to estimate realised volatility. Smoothness of the realised volatility processes is examined by estimating the Hurst parameter, a parameter H ∈ (0, 1) governing the roughness (Hölder continuity) of paths in the rough Bergomi model, introduced in [2]. The rough Bergomi model a member of the family of rough stochastic volatility models. A family of models which was recently popularised in mathematical finance by observations indicating that volatility in financial markets is best described by stochastic models where volatility can be modulated by the Hurst parameter H, which usually calibrates to values H ∈ (0, 0.5) (the rough case), hence inspiring the name of the model family. In this work, calibration of the Hurst parameter H is performed pathwise, using recently developed neural network calibration tools.ResultsIn all experiments the volatility calibrates to values well within the rough case H < 0.5 and on average fMRI scanner noise was very rough with H ≈ 0.03. Substantial variability was also observed, which was caused by edge effects, whereby H was larger near the edges of the phantoms.DiscussionThe findings challenge the assumption that fMRI scanner noise has constant volatility (in fact, the lower the value of H, the more pronounced the oscillations of the volatility, and hence the more “severe” is the violation of the constant volatility assumption) and add to the steady accumulation of studies suggesting implementing methods to model heteroscedasticity may improve fMRI data analysis. Additionally, the present findings add to previous work showing that the mean and normality of fMRI noise processes show edge effects, such that signal near the edges of the images is less likely to meet the assumptions of current modelling methods.

Interrelation and spillover effects between stocks and bonds: cross-market and cross-asset evidence

Purpose This paper aims to examine the behaviour, both contemporaneous and causal, of stock and bond markets across four major international countries. Design/methodology/approach The authors generate volatility and correlations using the realised volatility approach and implement a general vector autoregression approach to examine causality and spillovers. Findings While results confirm that same asset-cross country return correlations and spillovers increase over time, the same in not true with variance and covariance behaviour. Volatility spillovers across countries exhibit a substantial amount of time variation; however, there is no evidence of trending in any direction. Equally, cross asset – same country correlations exhibit both negative and positive values. Further, the authors report an inverse relation between same asset – cross country return correlations and cross asset – same country return correlations, i.e. the stock return correlation across countries increases at the same time the stock and bond return correlation within each country declines. Moreover, the results show that the stock and bond return correlations exhibit commonality across countries. The results also demonstrate that stock returns lead movement in bond returns, while US stock and bond returns have predictive power other country stock and bond returns. In terms of the markets analysed, Japan exhibits a distinct nature compared with those of Germany, the UK and USA. Originality/value The results presented here provide a detailed characterisation of how assets interact both with each other and cross-countries and should be of interest to portfolio managers, policy-makers and those interested in modelling cross-market behaviour. Notably, the authors reveal key differences between the behaviour of stocks and bonds and across different countries.