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Generative Adversarial Neural nets (GANs) are a new branch of machine learning techniques. A GAN learns to generate new data from the training data set. We examine the characteristics of the fake financial data using GANs trained on samples of daily S&P 500 and FTSE 100 index values. GANs feature two competing neural networks in a game theoretic context. The Generator net generates pseudo data that is presented to the discriminator net which then attempts to distinguish between the real and the fake data. This facilitates unsupervised learning on the dataset. The generative network generates data sets, while the discriminative network evaluates them. Equilibrium is reached when the generator can fool the discriminator half the time. Potential convergence difficulties led to the development of Wasserstein GANs, which we use in the analysis. We examine the characteristics of the generated fake S&P500 and FTSE 100 data sets. A key issue is how closely does the fake series mimic the real series? We explore this using a variety of metrics including regression analysis, applications of moments and characteristic functions, plus Random Forest analysis. We provide a practical application using the fake data to calculate Value at Risk (VaR).

This paper features an analysis of the relative effectiveness, in terms of the Adjusted R-Square, of a variety of methods of modelling realized volatility (RV), namely the use of Gegenbauer processes in Auto-Regressive Moving Average format, GARMA, as opposed to Heterogenous Auto-Regressive HAR models and simple rules of thumb. The analysis is applied to two data sets that feature the RV of the S&P500 index, as sampled at 5 min intervals, provided by the OxfordMan RV database. The GARMA model does perform slightly better than the HAR model, but both models are matched by a simple rule of thumb regression model based on the application of lags of squared, cubed and quartic, demeaned daily returns.

This paper features an analysis of cryptocurrencies and the impact of the COVID-19 pandemic on their effectiveness as a portfolio diversification tool and explores the correlations between the continuously compounded returns on Bitcoin, Ethereum and the S&P500 Index using a variety of parametric and non-parametric techniques. These methods include linear standard metrics such as the application of ordinary least squares regression (OLS) and the Pearson, Spearman and Kendall’s tau measures of association. In addition, non-linear, non-parametric measures such as the Generalised Measure of Correlation (GMC) and non-parametric copula estimates are applied. The results across this range of measures are consistent. The metrics suggest that, whilst the shock of the COVID-19 pandemic does not appear to have increased the correlations between the cryptocurrency series, it appears to have increased the correlations between the returns on cryptocurrencies and those on the S&P500 Index. This suggests that investments in cryptocurrencies are not likely to offer key diversification strategies in times of crisis, on the basis of evidence provided by this crisis.

This paper features a statistical analysis of the monthly three factor Fama/French return series. Rolling OLS regressions explore the relationship between the 3 factors, using data from July 1926 to June 2018, available on French’s website. The results suggest there are significant and time-varying relationships between the factors. A sub-sample from July 1990 to July 2018 is used to analyze the three series using two-stage least squares and the Hausman test to check for issues related to endogeneity. The empirical results suggest that the factors, when combined in OLS regression analysis, as suggested by Fama and French (2018), are likely to suffer from endogeneity. Ramsey’s RESET tests suggest a nonlinear relationship exists between the three series. We use two instruments to estimate the market betas, and compare them to betas estimated not using instruments. Non-parametric tests of the two sets of betas suggest significant differences. The results suggest that using these factors in linear regression analysis, as recommended by Fama and French [(2018). Choosing factors. Journal of Financial Economics, 128(2), 234–252] is problematic in that the estimated coefficients are highly sensitive to the correct model specification.

The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short-and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a daily FTSE adjusted price series, commencing in April 2009 and terminating in March 2021, and a corresponding daily S&P500 Index adjusted-price series obtained from Yahoo Finance. The data period includes all the gyrations caused by the Brexit vote in the UK, beginning with the vote to leave in 2016 and culminating in the actual agreement to withdraw in January 2020. It was then followed by the impact of the global spread of COVID-19 from the beginning of 2020. The results of the analysis suggest that movements in the contemporaneous levels of daily S&P500 Index levels have very significant effects on the behaviour of the levels of the daily FTSE 100 Index. They also suggest that negative movements have larger impacts than do positive movements in S&P500 levels, and that long-term multiplier impacts take about 10 days to take effect. These effects are supported by the results of quantile regression analysis. A key result is that weak form market efficiency does not apply in the second period.

School of Mathematics and Statistics, University of Sydney, Department of Finance, Asia University, Taiwan,The paper presents a novel analysis of the US spread of the SARS-CoV-2 causes the COVID-19 disease across 50 States and 2 Territories. Simple cross-sectional regressions are able to predict quite accurately both the total number of cases and deaths, which cast doubt on measures aimed at controlling the disease via lockdowns. Population density appears to play a significant role in transmission. This throws in sharp relief the relative e_ectiveness of the at-tempts to risk manage the spread of the virus by flattening the curve' (aka planking the curve) of the speed of transmission, and the effcacy of lockdowns in terms of the spread of the disease and death rates. The algorithmic tech-niques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers, and risk management and deficision making of healthcare by state, regional and national governments in all countries.