Forecasting Daily Variability of the S and P 100 Stock Index using Historical, Realised and Implied Volatility Measurements

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First we consider unobserved components and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility models and generalised autoregressive conditional heteroskedasticity models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard and Poor's 100 stock index series for which trading data (tick by tick) of almost seven years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility and is used for computing the forecast error. A stationary bootstrap procedure is required for computing the test statistic and its $p$-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts

Vertebroplasty and kyphoplasty: a comparative review of efficacy and adverse events

Vertebroplasty and kyphoplasty have become common surgical techniques for the treatment of vertebral compression fractures. Vertebroplasty involves the percutaneous injection of bone cement into the cancellous bone of a vertebral body with the goals of pain alleviation and preventing further loss of vertebral body height. Kyphoplasty utilizes an inflatable balloon to create a cavity for the cement with the additional potential goals of restoring height and reducing kyphosis. Vertebroplasty and kyphoplasty are effective treatment options for the reduction of pain associated with vertebral body compression fractures. Biomechanical studies demonstrate that kyphoplasty is initially superior for increasing vertebral body height and reducing kyphosis, but these gains are lost with repetitive loading. Complications secondary to extravasation of cement include compression of neural elements and venous embolism. These complications are rare but more common with vertebroplasty. Vertebroplasty and kyphoplasty are both safe and effective procedures for the treatment of vertebral body compression fractures

Focus on the management of thunderclap headache: from nosography to treatment

Thunderclap headache (TCH) is an excruciating headache characterized by a very sudden onset. Recognition and accurate diagnosis of TCH are important in order to rule out the various, serious underlying brain disorders that, in a high percentage of cases, are the real cause of the headache. Primary TCH, which may recur intermittently and generally has a spontaneous, benign evolution, can thus be diagnosed only when all other potential underlying causes have been excluded through accurate diagnostic work up. In this review, we focus on the management of TCH, paying particular attention to the diagnostic work up and treatment of the condition

Population, resources, and environment: Implications of human behavioral ecology for conservation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43481/1/11111_2005_Article_BF02207996.pd