Estimating the volatility of S&amp;P 500 futures prices using the extreme‐value method

This study evaluates the forecasting performance of extreme-value volatility estimators for the equity-based Nifty Index using two-scale realized volatility. This benchmark mitigates the effect of microstructure noise in the realized volatility. Extreme-value estimates with relatively simple forecasting methods provide substantially better short-term and long-term forecasts, compared to historical volatility. The higher efficiency of extreme-value estimators is primarily responsible for this improvement. The extent of possible improvement in forecasts is likely to be economically significant for applications like options pricing. By including extremevalue estimators, the forecasting performance of generalized autoregressive conditional heteroscedasticity (GARCH) can also be improved.

Section: Introductionmentioning

confidence: 91%

Forecasting performance of extreme‐value volatility estimators

Vipul

Jacob

2007

“…Wiggins (1992) demonstrates that this estimator is more efficient than estimates of price volatility based on closing prices.…”

Section: Further Testsmentioning

confidence: 96%

The impact of electronic trading on bid‐ask spreads: Evidence from futures markets in Hong Kong, London, and Sydney

Aitken

Frino

Hill

et al. 2004

During 1999 and 2000, three major futures exchanges transferred trading in stock index futures from open outcry to electronic markets: the London International Financial Futures and Options Exchange (LIFFE); the Sydney Futures Exchange (SFE); and the Hong Kong Futures Exchange (HKFE). These changes provide unique natural experiments to compare relative bid‐ask spreads of open outcry vs. electronically traded markets. This paper provides evidence of a decrease in bid‐ask spreads following the introduction of electronic trading, after controlling for changes in price volatility and trading volume. This provides support for the proposition that electronic trading can facilitate higher levels of liquidity and lower transaction costs relative to floor traded markets. However, bid‐ask spreads are more sensitive to price volatility in electronically traded markets, suggesting that the performance of electronic trading systems deteriorates during periods of information arrival. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:675–696, 2004

“…The reduced form of the GarmanKlass volatility estimator can be written as 5 (4) where P t,H , P t,L , P t,O , and P t,C are the high, low, opening, and closing futures prices at date t, respectively. Wiggins (1992) and Daigler and Wiley (1999) showed that the Garman-Klass volatility estimator is more efficient than using close-to-close prices. To match the weekly COT data, the daily volatility estimate is averaged over the WednesdayTuesday interval, and used as the dependent variable in equation (1).…”

Section: Volatility Estimationsmentioning

confidence: 99%

The effect of net positions by type of trader on volatility in foreign currency futures markets

Wang

2002

We investigate the effect of net positions by type of trader on return volatility in six foreign currency futures markets using the weekly Commitments of Traders (COT) data. When net positions are decomposed into expected and unexpected components, we find that expected net positions by type of trader generally do not co-vary with volatility. However, volatility is positively associated with shocks (in either direction) in net positions of speculators and small traders, and negatively related to shocks (in either direction) in net positions of hedgers. This evidence suggests that changes in speculative positions destabilize the market. Consistent with dispersion of beliefs models and noise trading theories, hedgers appear to possess private information, whereas speculators and small traders are less informed in these markets.