Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants.

Dynamics of Volatility Spillover and Connectedness Among Sectors of Indian Stock Market

This paper investigates the dynamic volatility spillover and connectedness among different sectors of the Indian stock market during the Covid-19 pandemic. The study considers 18 sectors listed on the National Stock Exchange. Diebold-Yilmaz Volatility spillover model and Baruník and Křehlík frequency connectedness methodology are used to investigate the time varying dynamics of the spillover during the turbulence period. Daily market prices of 18 sectors, from March 15, 2019, to February 28, 2022, are considered for the present study. The results reveal that the spillover from infra, commodities, services, MNC, oil and gas, financial services, private bank, energy, and PSE is more, and this clearly indicates that during the pandemic, these sectors mostly had a spillover effect on other sectors.

WTI Crude Oil Options Market Prior to and During the COVID-19 Pandemic

The COVID-19 pandemic has caused turbulence in many areas of the global economy. It also contributed to an increase in volatility on the energy commodities market. This spilled over into the derivatives market, particularly the crude oil futures market. The aim of the article is to compare the costs and effectiveness of using options on WTI oil from before and after the pandemic. The analyzes took into account the value of option premiums and final results obtained by buyers of call options from March 1, 2018 to April 14, 2022. The results showed that buyers of call options during the pandemic, despite paying much higher option premiums, experienced significantly higher payouts and rates of return. They were the highest for options with the longest expiry periods of 21-30 days. Research also showed that during the pandemic, options with strike prices set at a level higher than the price of oil on the contract date had particularly high rates of return, while the highest payout values were achieved by buyers of call options with low strike prices.

A Metric Space for Point Process Excitations

A multivariate Hawkes process enables self- and cross-excitations through a triggering matrix that behaves like an asymmetrical covariance structure, characterizing pairwise interactions between the event types. Full-rank estimation of all interactions is often infeasible in empirical settings. Models that specialize on a spatiotemporal application alleviate this obstacle by exploiting spatial locality, allowing the dyadic relationships between events to depend only on separation in time and relative distances in real Euclidean space. Here we generalize this framework to any multivariate Hawkes process, and harness it as a vessel for embedding arbitrary event types in a hidden metric space. Specifically, we propose a Hidden Hawkes Geometry (HHG) model to uncover the hidden geometry between event excitations in a multivariate point process. The low dimensionality of the embedding regularizes the structure of the inferred interactions. We develop a number of estimators and validate the model by conducting several experiments. In particular, we investigate regional infectivity dynamics of COVID-19 in an early South Korean record and recent Los Angeles confirmed cases. By additionally performing synthetic experiments on short records as well as explorations into options markets and the Ebola epidemic, we demonstrate that learning the embedding alongside a point process uncovers salient interactions in a broad range of applications.