Neda Todorova

Volatility estimators based on daily price ranges versus the realized range

This study investigates the relative performance of alternative extreme-value volatility estimators based on daily and intraday ranges of the German index DAX 30. As a benchmark, the two-scales realized volatility is used. Intraday data from 6 years and 4 months are divided into two periods of different liquidity and volatility levels. The empirical results show that all range-based estimators are superior compared to the classical estimator but are negatively biased due to the discreteness of the price process. The estimation accuracy of all volatility proxies depends on the drift of the price process. The performance of the estimators based on daily price ranges is furthermore very sensitive to the level of volatility. The realized range, an estimator obtained from intraday ranges is more efficient and less biased than the daily ranges. The main determinant of its properties appears to be the liquidity level. The adjustments according to Christensen and Podolskij (2007) and Martens and van Dijk (2007) perform significantly better than the Parkinson estimator and thus provide conclusive support for the relative advantage of the realized range for measuring equity index volatility.

Economic significance of oil price changes on Russian and Chinese stock markets

This study discusses the economic significance of the relationship between oil price changes and emerging markets equity returns. It extends the literature by obtaining significant Granger causalities and impulse response functions for the daily returns over the last decade on the emerging markets of Russia and China. Furthermore, it is shown that a trading rule based on a bivariate Vector Autoregresive (VAR(p)) model outperforms the Russian and Chinese stock index in terms of risk and return, even when transaction costs are taken into account. Implementing the bootstrap methodology to test the results, it is proved that oil price fluctuations significantly contribute to the risk profile of the trading strategy for Russian market and improve the risk-return characteristics for Chinese stock trading.

Relative scarcity and convenience yield: evidence from non-ferrous metals

ABSTRACT We study the relationship between convenience yield and relative scarcity in the non-ferrous metal market for the period January 2000–March 2015. We identify various sets of economic relationships for six major base metals, namely, aluminium, copper, lead, nickel, tin and zinc. Our bivariate and multivariate VARs and associated Granger-causality test results generally support the existence of a positive relationship between convenience yields of base metals and our relative scarcity measure. Furthermore, the time-varying characteristics observed in the results, especially during contango and backwardation periods, provide useful information to market players in developing inventory strategies.

Volatility Forecasting of Non-Ferrous Metal Futures: Covariances, Covariates or Combinations?

This is the first comprehensive study on the forecasting of the realized volatility of non-ferrous metal futures. Based on 8.5 years of intraday data on copper, zinc, nickel, lead and aluminum, we explore a variety of extensions of the univariate heterogeneous autoregressive (HAR) model and seek to harness the economic linkages among these metals to improve forecasts. A simple approach that augments the models with shocks in other metals’ series appears to outperform more sophisticated specifications, which explicitly model covariances. The results suggest that the information inherent in the volatility series of aluminum is most useful in enhancing the accuracy of forecasts for other metals. While consistently outperforming the original HAR model with an individual model is difficult, combination forecasts, especially with univariate specifications or Bayesian model averaging, are found to conclusively outperform the benchmark.

Dynamics of volatility transmission between the U.S. and the Chinese agricultural futures markets

ABSTRACT The U.S. and China are two of the biggest players in the world agricultural market. The literature documents that volatility in the U.S. agricultural futures market spills over significantly to that of China. This article provides further insights into the spillovers from China to the U.S. as well as the time horizon and dynamics of the bidirectional spillovers through the application of a multivariate extension of the heterogeneous autoregressive model, in relation to four commodities – soybean, wheat, corn and sugar. The results confirm the existence of significant spillovers from the U.S. to China for four commodities, which are primarily generated by the shorter-term volatility components in the U.S., and provide evidence for the increasing pricing power of the Chinese market. The findings are robust against various specifications and have important investment and policy implications.

Implied volatility smirk and future stock returns: evidence from the German market

Purpose – The purpose of this paper is to investigate the relationship between option’s implied volatility smirk (IVS) and excess returns in the Germany’s leading stock index Deutscher-Aktien Index (DAX) 30. Design/methodology/approach – The study defines the IVS as the difference in implied volatility derived from out-of-the-money put options and at-the-money call options. This study employs the ordinary least square regression with Newey-West correction to analyse the relationship between IVS and excess DAX 30 index returns in Germany. Findings – The authors find that the German market adjusts information in an efficient way. Consequently, there is no information linkage between option volatility smirk and market index returns over the nine years sample period after considering the control variables, global financial crisis dummies, and the subsample test. Research limitations/implications – This study finds that the option market and the DAX 30 index are informationally efficient. Implications of the f...

The calm after the storm: implied volatility and future stock index returns

This article explores the predictive power of five implied volatility indices for subsequent returns on the corresponding underlying stock indices from January 2000 through October 2013. Contrary to previous research, very low volatility levels appear to be followed by significantly positive average returns over the next 20, 40 or 60 trading days. Rolling trading simulations show that positive adjusted excess returns can be achieved when long positions in the stock indices are taken on days of very low implied volatility. This may be a hint that market inefficiencies exist in some markets, especially outside the USA. The excess returns measured against a buy and hold benchmark are significant for the German and Japanese market when tested with a bootstrap methodology. The results are robust against a broad spectrum of specifications.

Volatility Transmission in Energy Futures Markets

This study is novel in its application of a multivariate heterogeneous autoregressive model to studying volatility transmission patterns in energy futures markets. In particular, the nature of volatility spillovers between futures on crude oil, natural gas and gasoil is examined by using range-based volatility proxies and splitting volatility in components defined over different time horizons. The results provide evidence that crude oil futures carry significant information for the volatility evolution of other energy futures traded on the Intercontinental Exchange (ICE) and reveal interesting insights into the sources of the documented volatility interrelations. Short-term shocks in Brent oil volatility significantly affect the volatility of gasoil futures, while the impact of oil and gasoil on natural gas is driven by the long-term volatility component. Additionally, Brent oil and gasoil ICE futures volatilities exhibit strong positive dynamic correlation, whereas the remaining pairwise correlation curves fluctuate around zero.