VOLATILITY S
231699263
Unraveling S&P500 stock volatility and networks An encoding and decoding approach
We extend the Hierarchical Factor Segmentation(HFS) algorithm for discovering multiple volatility states process hidden within each individual S&P500 stock's return time series. Then we develop an associative measure to link stocks into directed networks of various scales of associations. Such networks shed lights on which stocks would likely stimulate or even promote, if not cause, volatility on other linked stocks. Our computing endeavors starting from encoding events of large return on the original time axis to transform the original return time series into a recurrence time process on discretetime axis. By adopting BIC and clustering analysis, we identify potential multiple volatility states, and then apply the extended HFS algorithm on the recurrence time series to discover its underlying volatility state process. Our decoding approach is found favorably compared with Viterbi's in experiments involving both light and heavy tail distributions. After recovering the volatility state process back to the original time axis, we decode and represent stock dynamics of each stock. Our measurement of association is measured through overlapping concurrent volatility states upon a chosen window. Consequently, we establish data driven associative networks for S&P500 stocks to discover their global dependency relational groupings with respect to various strengths of links.
Xiaodong Wang Fushing Hsieh
219075911
COVID 19 and Stock Market Volatility
I investigate the impact of COVID 19 cases and related deaths on the US stock market (Dow Jones and S&P500 indices) allowing for changes in trading volume and volatility expectations, as well as day of the week effects. The results, based a GARCH(1,1) model and data from April 8, 2019 to April 9, 2020, suggest that changes in the number of cases and deaths in the US and six other countries majorly affected by the COVID 19 crisis do not have an impact on the US stock market returns, apart from the number of reported cases for China. However, there is evidence of a positive impact, for some countries, on the conditional heteroscedasticity of the Dow Jones and S&P500 returns. VAR models suggest that the number of reported deaths in Italy and France have a negative impact on stock market returns, and a positive impact on the VIX returns. Finally, Markov Switching models suggest that at the end of February 2020 the magnitude of the negative impact of the VIX on stock market returns increased threefold.
Enrico Onali
222210930
On the dependence structure between S&P500, VIX and implicit Interexpectile Differences
We study the dependence structure between the S&P500, the VIX Index, and implicit Interexpectile Differences, that are an alternative measure of implied volatility based on the notion of implicit expectile, recently introduced in Bellini et al. [Implicit expectiles and measures of implied volatility. Quant. Finance, 2018a, 18, 1851 1864] After filtering the time series of the marginals by ARMA (E)GARCH models, we fit several parametric families of copulas to the pairwise joint distribution of the residuals, in order to investigate the presence of radial asymmetry and asymptotic tail dependence. We find a negative dependence between S&P500 and both implied volatility indices and a positive dependence between VIX and Interexpectile Differences. The best fitting copulas seem relatively stable over time and display both asymmetry and strong tail dependence, in accordance with the leverage effect.
Fabio Bellini Lorenzo Mercuri Edit Rroji
202233504
Mapping US presidential terms with S&P500 index: Time series analysis approach
This paper maps the US presidential terms with price dynamics in US stock markets by focusing on the S&P500 index. Fractional integration techniques, which are more general than other standard methods, are used and the results obtained produce interesting findings. It was found that during the second presidential terms, S&P500 stock market is less efficient and present higher degrees of persistence in its volatility. This is observed independently of the political affiliations of the president in power. The volatility, in general, reflects the spillover of economic excesses at the end of the first presidential term when seeking re election into the second term in office. Expansionary monetary and fiscal policies at the end of the first term may create disequilibria in the economy which are amplified in the second term through a transmission mechanism resulting in contractionary interventionist policies in a situation where no incentive for re election exists by the incumbent.
Luis Alberiko Gil-Alana Robert Mudida OlaOluwa Simon Yaya Kazeem Adewale Osuolale Ephraim A Ogbonna
229507251
Is the Variance Swap Rate Affine in the Spot Variance? Evidence from S&P500 Data
ABSTRACT We empirically investigate the functional link between the variance swap rate and the spot variance. Using S&P500 data over the period 2006 2018, we find overwhelming empirical evidence supporting the affine link implied by exponential affine stochastic volatility models. Tests on yearly subsamples suggest that exponential mean reverting variance models provide a good fit during periods of extreme volatility, while polynomial modelsare suited for years characterized by more frequent price jumps.
Maria Elvira Mancino Simone Scotti Giacomo Toscano
158577190
Oil shocks and volatility jumps
In this paper, we analyse the role of oil price shocks, derived from expectations of consumers, economists, financial market, and policymakers, in predicting volatility jumps in the S&P500 over the monthly period of 1988:01 2015:02, with the jumps having been computed based on daily data over the same period. Standard linear Granger causality tests fail to detect any evidence of oil shocks causing volatility jumps. But given strong evidence of nonlinearity and structural breaks between jumps and oil shocks, we next employed a nonparametric causality in quantiles test, as the linear model is misspecified. Using this data driven robust approach, we were able to detect overwhelming evidence of oil shocks predicting volatility jumps in the S&P500 over its entire conditional distribution, with the strongest effect observed at the lowest considered conditional quantile. Interestingly, the predictive ability of the four oil shocks on volatility jumps is found to be both qualitatively and quantitatively similar.
Konstantinos Gkillas Rangan Gupta Mark E Wohar
211407726
Asymmetric volatility spillovers between international economic policy uncertainty and the U.S. stock market
Abstract This paper studies the asymmetric spillover effect of important economic policy uncertainty (EPU) on the S&P500 index. We use monthly EPU indexes from Australia, Canada, China, Japan, the U.K. and the U.S. and the realized volatility of the U.S. stock market to study the asymmetric pairwise directional spillovers on the U.S. stock market from 2000 to 2019. We find that S&P500 index volatility is a net recipient of spillovers from important EPU indexes. Japanese EPU has the strongest spillover effect on the U.S. stock markets, while EPU from the U.K. plays a very limited role. By decomposing the volatility into good and bad volatility, we find that the relationship between bad stock market volatility and EPU is stronger than between good volatility and EPU. Time varying spillover characteristics show that bad volatility reacts more strongly to shocks in EPU following the debt crisis and trade negotiations. Several robustness checks are provided to verify the novelty of these findings.
Feng He Ziwei Wang Libo Yin
219781801
Using Machine Learning Models to Predict S&P500 Price Level and Spread Direction
Pairs Trading is an investment strategy applied by many buy side financial institutions. In pairs trading, instead of making investment decision based on future stock price level, it is based on prediction of the spread between two stocks in the future. Consider two stocks moving in the similar trend, following the same market dynamic and trading at a spread that is mean reverting. If the spread widens between the two stocks, then we can short the overpriced stock and buy the underpriced stock. Later on as the spread between the stocks mean reverts to the equilibrium level, the profit will be realized. Traditionally, the methodology applied in pairs trading typically lies in the linear regression, Time Series Analysis such as ARIMA, SARIMA as well as GARCH model what captures the stochasticity of the volatility. These classical methods have proved to be quite effective in the old regime of the financial world. However, as the world of finance shifted into a new regime, particularly after the dramatic improvement in the computing speed, faster diffusion of the news and more transparent market, the quantitative trading industry has started employing more innovative strategies, such as Machine Learning, Deep Learning and Reinforcement Learning algorithms. In this project, we seek to explore how to best apply learning algorithms in the pairs trading by implementing various machine learning models and compared against each other. We are inspired by the paper from (van der Have [2017] and (Wu [2015] and the models implemented by Alex Dai (Dai) where they modeled the spread between two stocks assuming Ornstein Uhlenbeck (OU) process. Following their ideas, we selected co integrated S&P 500 stock timeseries using cointegration test, and created input features using OU process, this process will be detailed in section 5.1. To make our model more realistic, instead of using the absolute spread level as the label, we convert the problem into a classification problem that predicts the direction of the future spread move, as it is much more feasible and easier to predict the general trend of the spread rather than the absolute level. Therefore, the label is 1 when the spread between the two stocks will tighten in the future and 0 otherwise, so if we can correctly predict a label then it is guaranteed to realize the profit. Our baseline model is the traditional time series model, then logistic regression, Gaussian Discriminant Analysis, Support Vector Machine (SVM) and Neural Network will be applied. Eventually the accuracy across different models are compared and analyzed.
Alex Fuster
215415816
Is the Variance Swap Rate Affine in the Spot Variance? Evidence From S&P500 Data
We empirically investigate the functional link between the variance swap rate and the spot variance. Using S\&P500 data over the period 2006 2018, we find overwhelming empirical evidence supporting the affine link analytically found by Kallsen et al. (2011) in the context of exponentially affine stochastic volatility models. Tests on yearly subsamples suggest that exponentially mean reverting variance models provide a good fit during periods of extreme volatility, while polynomial models, introduced in Cuchiero (2011) are suited for years characterized by more frequent price jumps.
Maria Elvira Mancino Simone Scotti Giacomo Toscano
219481265
On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks
We introduce Infinite Hidden Markov (IHM) models to forecasting realized volatilities of crude oil futures markets with exogenous factors. With these IHM models, we lift the restriction of a pre defined number of regimes and allow for an unknown number of different parameter regimes and breakpoints. We employ two types of infinite hidden Markov models to accommodate structural breaks incurred by policy changes, exogenous shocks, and other factors. We find that IHM HAR models outperform all other non switching variants. In regard to forecasting performance, IHM HAR models with exogenous factors such as realized volatilities of competing futures markets and the S&P500 are superior model choices for short term forecasts. For longer term forecasts, the equity channel shows only little positive impact. Evidence of economic gains in portfolio construction based on IHM HAR forecasts is provided.
Jiawen Luo Qiang Ji Tony Klein Neda Todorova Dayong Zhang