The cross-sectional relation between conditional heteroskedasticity, the implied volatility smile, and the variance risk premium

Modelling Conditional Correlations in the Volatility of Asian Rubber Spot and Futures Returns

This paper employs quantile autoregression to investigate the influence of ‘market size’ and ‘industry’ effects on the South African equity market volatility response to return shocks. It is now well documented that equity market volatility exhibits asymmetric response to positive and negative return shocks. This paper provides empirical evidence which shows that the South African equity market asymmetric volatility response is significantly a large company phenomenon and with the exception of the Resources 10 and Financials 15 Indices, there is generally no volatility asymmetric response heterogeneity at the sector level. These results have important implications for investors and fund managers in relation to portfolio construction, risk management and optimal equity risk premium determination.

PurposeThe purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance.Design/methodology/approachThe authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums.FindingsThere are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity.Practical implicationsCommodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced.Originality/valueThe empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.

This paper examines the contemporaneous and lead–lag relationships between economic variables and implied volatility smiles for three major European currency options. We find that cross economic determinants are at least as important as own economic variables in explaining the dynamics of implied volatility smiles. Out‐of‐sample tests also suggest that cross economic variables are important in predicting an economy's currency option smile. These findings suggest that the price impact from cross economic determinants may help fill the gap between the theoretical and the practical implied volatility skews.

Shifts in Volatility Driven by Large Stock Market Shocks

The authors explore the separate momentum effect in the positive and negative return environments at both the time domain and the frequency domain. Using the power spectrums and daily returns of sixteen representative exchange-traded funds (ETFs), the authors attempt to answer whether the momentum strategies have equal endurance gain and loss markets with assets that represent different market capitalizations, value, and industry. The authors’ results provide evidence that the US equity market carries strong negative-return resistance. The probability of realizing positive return is increasingly higher when the market carries longer consecutive negative returns. Small-cap stocks exhibit momentum effect in positive return environment but mean reversion effect in negative returns. For value stocks, the probabilities of continuing positive returns are 16.83% higher; for growth stocks, the probabilities of flipping over from negative returns are 16.4% higher. The power spectrums from the Merlot wavelet transformation suggest that the density of transaction activities is high at the high-frequency level, proving that the momentum strategy is less effective within a month-wide window. TOPICS:Exchange-traded funds and applications, mutual fund performance Key Findings • The average probability of the equity market realizing another positive return after the previous positive return is 51.80%, the probability of the third positive return after two positive days is 50.64%, 47.98% after three days, and 45.98% after four days. • The US equity market carries negative return resistance. The average probabilities of the market realizing positive return after one, two, three, four, and five days of consecutive negative returns are 52.88%, 54.63%, 56.55%, 57.17%, and 60.09% across the 11 sectors. • Small-cap stocks exhibit momentum effect in positive return environment but mean reversion effect in negative returns. The probabilities of continuing positive trend are higher for value stocks; yet the probabilities of stopping negative trend are higher for growth stocks.

This study explores the connectedness between selected emerging equity markets (BRIC-T) and the Emerging Markets Financial Stress Index (EMFSI). We aim to reveal the extent of spillovers from stock market indices to aggregated financial tension in these countries. Empirical investigations are executed through Quantile Vector Autoregression analysis. Results show that spillovers occur mainly during extreme negative and positive return periods. When we focus on four important phases, namely Global Financial Crisis (GFC), the Euro Area Sovereign Debt Crisis, the COVID-19 pandemic, and the Russia-Ukraine war, three countries come to the fore. While Brazil has had a substantial and persistent impact across the years, during the GFC, two other countries, Russia and Türkiye, seem to induce positive return spillovers toward emerging markets’ stress. This impact becomes bilateral in Russia (both in positive and negative returns) during the pandemic and the Russia-Ukraine war. Thus, we conclude that among the examined market economies, negative or positive return transmissions to emerging market stress are led mainly by Brazil and Russia. The importance of energy sources and political factors can account for this result.

Variance risk premium and equity returns

We measure “good” and “bad” variance premia that capture risk compensations for the realized variation in positive and negative market returns, respectively. The two variance premium components jointly predict excess returns over the next one and two years with statistically significant positive (negative) coefficients on the good (bad) component. The R2s reach about 10% for aggregate equity and portfolio returns and 20% for corporate bond returns. To explain the new empirical evidence, we develop a model that highlights the differential impact of upside and downside risk on equity and variance risk premia. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2890 . This paper was accepted by Neng Wang, finance.

Variance risk in aggregate stock returns and time-varying return predictability

Credit default swap spreads and variance risk premia

This article examines the characteristics of key measures of volatility for different types of futures contracts to provide a better foundation for modeling volatility behavior and derivative values. Particular attention is focused on analyzing how different measures of volatility affect volatility persistence relationships. Intraday realized measures of volatility are found to be more persistent than daily measures, the type of GARCH procedure used for conditional volatility analysis is critical, and realized volatility persistence is not coherent with conditional volatility persistence. Specifically, although there is a good fit between the realized and conditional volatilities, no coherence exists between their degrees of persistence, a counterintuitive finding that shows realized and conditional volatility measures are not a substitute for one another

Examining Asymmetric Relationship between India VIX, Nifty 50 Returns and Trading Volume: A case of Quantile Regression

Does Bitcoin still own the dominant power? An intraday analysis

Variance trading and market price of variance risk