Modelling oil price volatility

In this paper, we examine the volatility of crude oil price using daily data for the period 1991–2006. Our main innovation is that we examine volatility in various sub-samples in order to judge the robustness of our results. Our main findings can be summarised as follows: (1) across the various sub-samples, there is inconsistent evidence of asymmetry and persistence of shocks; and (2) over the full sample period, evidence suggests that shocks have permanent effects, and asymmetric effects, on volatility. These findings imply that the behaviour of oil prices tends to change over short periods of time.

Do retail option traders know better about market volatility?

This paper examines the informational content and predictive power of implied volatility over different forecasting horizons in a sample of European covered warrants traded in the Hong Kong and Singapore markets. The empirical results show that time-series-based volatility forecasts outperform implied volatility forecast as a predictor of future volatility. The finding also suggests that implied volatility is biased and informationally inefficient. The results are attributable to the fact in Hong Kong and Singapore the covered warrants markets are dominated by retail investors, who tend to use covered warrants' leverage to speculate on the price movements of the underlying rather than to express their view on volatility.

Dynamic interdependence and volatility transmission of Asian stock markets: evidence from the Asian crisis

This paper examines dynamic interdependence, volatility transmission, and market integration across selected stock markets during the Asian financial crisis periods 1997 and 1998. Using a vector autoregressive–exponential generalized autoregressive conditional heteroskedasticity (VAR-EGARCH) model, it is found that reciprocal volatility transmission existed between Hong Kong and Korea, and unidirectional volatility transmission from Korea to Thailand. This suggests that Hong Kong played a significant role in volatility transmission to the other Asian markets. The data also indicate market integration in that each market reacted to both local news and news originating in the other markets, particularly adverse news.

Intraday periodicity and long memory volatility processes in the Korean bond futures market

This article examines long memory volatility processes in the 5 minute Korean Treasury bond futures data using two different techniques: the GPH test and the EML test. Two proxies of volatility returns are used: absolute returns and square returns. We found intraday periodicity and autocorrelation persistence in the volatility for the 5 minute returns. The empirical results from both techniques indicate that there is little evidence of long memory in the level of returns, but there is significant evidence of long memory in the absolute returns and the square returns. This implies that volatility is best characterised by long memory processes. Additionally, we found that the absolute returns are the most appropriate indicator to represent the long memory volatility processes.

Modelling regulatory change V's volume of trading effects in HSIF and HSI volatility

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

Multifractality and long-range dependence of asset returns: the scaling behavior of the Markov-switching multifractal model with lognormal volatility components

In this paper, we consider daily financial data from various sources (stock market indices, foreign exchange rates and bonds) and analyze their multiscaling properties by estimating the parameters of a Markov-switching multifractal (MSM) model with Lognormal volatility components. In order to see how well estimated models capture the temporal dependency of the empirical data, we estimate and compare (generalized) Hurst exponents for both empirical data and simulated MSM models. In general, the Lognormal MSM models generate "apparent" long memory in good agreement with empirical scaling provided that one uses sufficiently many volatility components. In comparison with a Binomial MSM specification [11], results are almost identical. This suggests that a parsimonious discrete specification is flexible enough and the gain from adopting the continuous Lognormal distribution is very limited.

Do retail option traders know better about market volatility?

This paper examines the informational content and predictive power of implied volatility over different forecasting horizons in a sample of European covered warrants traded in the Hong Kong and Singapore markets. The empirical results show that time-series-based volatility forecasts outperform implied volatility forecast as predictors of future volatility. The finding also suggests that implied volatility is biased and informationally inefficient and that covered warrants are typically overvalued. The results are attributable to the fact that, in Hong Kong and Singapore, the covered warrants markets are dominated by retail investors who tend to use covered warrants' leverage to speculate on the price movements of the underlying assets rather than to express their view on volatility. Arbitrage is not possible in the markets as short-selling of covered warrants is prohibited.

The forecast quality of CBOE implied volatility indexes

We examine the forecast quality of Chicago Board Options Exchange (CBOE) implied volatility indexes based on the Nasdaq 100 and Standard and Poor's 100 and 500 stock indexes. We find that the forecast quality of CBOE implied volatilities for the S&P 100 (VXO) and S&P 500 (VIX) has improved since 1995. Implied volatilities for the Nasdaq 100 (VXN) appear to provide even higher quality forecasts of future volatility. We further find that attenuation biases induced by the econometric problem of errors in variables appear to have largely disappeared from CBOE volatility index data since 1995.

Forecasting stock index volatility : comparing implied volatility and the intraday high-low price range

The intraday high–low price range offers volatility forecasts similarly efficient to high-quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in-sample and out-of-sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.

Estimating expected excess returns using historical and option-implied volatility

We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward-to-risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option-implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option-implied volatility risk measure yields a positive, but statistically insignificant, risk-return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal-to-noise ratio in the latter subperiod.

Modelling Fiji-US exchange rate volatility

In this article, we examine the Fiji-US exchange rate volatility using daily data for the period 2000 to 2006. Our modelling framework is based on the EGARCH model. We find robust evidence of conditional shocks having a positive effect on exchange rate volatility, shocks having asymmetric effects on exchange rate volatility and shocks having a transitory effect on exchange rate volatility.