Hedging Effectiveness of the VIX ETPs

This study introduces basic concepts about hedging and provides an overview of common hedging practices. This theoretical introduction is followed by an empirical application in which the hedging effectiveness of the VIX ETPs is evaluated. The iPath Series B S&P 500 VIX Short Term Futures ETN (VXX) and the SPDR S&P 500 Trust ETF (SPY) are taken for the empirical application. Dynamic conditional correlations between the VXX and SPY are obtained from DCC-GARCH framework. Based on the estimated conditional volatilities of the SPY and the hedged portfolio, a hedging effectiveness index is constructed. Results show that the hedging effectiveness of the VXX increases in turbulent periods such as the last three months of 2018 marked by the plummeting oil prices, increasing uncertainties about the Brexit deal, and rising federal funds rates and the month of March 2020 when the COVID-19 pandemic became a global concern.

Cryptocurrencies’ Time to shine in Tunisia

Using the DCC-GARCH (1.1) model, we investigate the dynamic conditional correlations between Tunisian indices, digital assets, and gold prices for the period ranging from 4 January 2016 to 30 April 2020. Our findings reveal that digital assets (Bitcoin, Ripple, Ethereum, and Dash) and gold can be considered as hedge and diversifier assets before the 2020 global pandemic. Contrarily to Ripple which can be a safe haven asset for the Tunisian investors in early 2020, Monero can be considered as a diversifier asset more than a hedge. Finally, our results can be useful to Tunisian investors when accounting for implementing hedging strategies.JEL classification: C22, C5, G1

Model Free Inference on Multivariate Time Series with Conditional Correlations

New results on volatility modeling and forecasting are presented based on the NoVaS transformation approach. Our main contribution is that we extend the NoVaS methodology to modeling and forecasting conditional correlation, thus allowing NoVaS to work in a multivariate setting as well. We present exact results on the use of univariate transformations and on their combination for joint modeling of the conditional correlations: we show how the NoVaS transformed series can be combined and the likelihood function of the product can be expressed explicitly, thus allowing for optimization and correlation modeling. While this keeps the original “model-free” spirit of NoVaS it also makes the new multivariate NoVaS approach for correlations “semi-parametric”, which is why we introduce an alternative using cross validation. We also present a number of auxiliary results regarding the empirical implementation of NoVaS based on different criteria for distributional matching. We illustrate our findings using simulated and real-world data, and evaluate our methodology in the context of portfolio management.

Dynamic Spillovers between Gulf Cooperation Council’s Stocks, VIX, Oil and Gold Volatility Indices

This paper analyzes the conditional correlations between the stock market returns of countries that are members of the Gulf Cooperation Council (GCC). The innovative aspects of the paper consist of focusing on three volatility indices: the oil (OVX), gold (GVZ), and S&P500 (VIX) markets (considered in log-difference). We use weekly data and resort to DCC-GARCH modeling. The novelty of the paper consists in revealing that: (i) GCC stock market returns are negatively correlated with each of the volatility measures, and the correlations are stronger during crisis periods; (ii) GCC stock returns are mostly correlated with oil shocks; and (iii) Saudi Arabia and Qatar are the most responsive to all shocks among the GCC countries, while Bahrain correlates weakly to shocks in oil, gold, and VIX. The most striking results feature extra sensitivity of Saudi Arabia and Qatar in terms of volatility indices, which should be the foremost concern of policymakers and banking analysts.

Contagion in Futures FOREX Markets for the Post- Global Financial Crisis: A Multivariate FIGARCHcDCC Approach

This paper seeks to investigate the time-varying conditional correlations to the futures FOREX market returns. We employ a dynamic conditional correlation (DCC) Generalized ARCH (GARCH) model to find potential contagion effects among the markets. The under investigation period is 2014-2019. We focus on four major futures FOREX markets namely JPY/USD, KRW/USD, EUR/USD and INR/USD. The empirical results show an increase in conditional correlation or contagion for all the pairsof future FOREX markets. Based on the dynamic conditional correlations, KRW/USD seems to be the safest futures FOREX market. The results are of interest to policymakers who provide regulations for the futures FOREX markets. JEL Classification Codes: C58, C61, G11, G15