Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure

This paper decomposes volatility proxies according to upward and downward price movements in high-frequency financial data, and uses this decomposition for forecasting volatility. The paper introduces a simple Garch-type discrete time model that incorporates such high-frequency based statistics into a forecast equation for daily volatility. Analysis of S&P 500 index tick data over the years 1988-2006 shows that taking into account the downward movements improves forecast accuracy significantly. The R2 statistic for evaluating daily volatility forecasts attains a value of 0.80, both for in-sample and out-of-sample prediction.volatility proxy; downward absolute power variation; log-Garch; volatility asymmetry; leverage effect; SP500; volatility forecasting; high-frequency data

Garch Parameter Estimation Using High-Frequency Data

Estimation of the parameters of Garch models for financial data is typically based on daily close-to-close returns. This paper shows that the efficiency of the parameter estimators may be greatly improved by using volatility proxies based on intraday data. The paper develops a Garch quasi maximum likelihood estimator (QMLE) based on these proxies. Examples of such proxies are the realized volatility and the intraday high-low range. Empirical analysis of the S&P 500 index tick data shows that the use of a suitable proxy may reduce the variances of the estimators of the Garch autoregression parameters by a factor 20.volatility estimation; quasi maximum likelihood; volatility proxy; Gaussian QMLE; log-Gaussian QMLE; autoregressive conditional heteroscedasticity

Phenology:Climate change is shifting the rhythm of nature

The chapter titled Phenology: Climate change is shifting the rhythm of nature looks at how climate change is disrupting the life cycle patterns of plant and animal species, its consequences, and the need to address this issue by restoring ecological connectivity and biological diversity and most importantly, reducing greenhouse gas emissions

Proxies for daily volatility

High frequency data are often used to construct proxies for the daily volatility in discrete time volatility models. This paper introduces a calculus for such proxies, making it possible to compare and optimize them. The two distinguishing features of the approach are (1) a simple continuous time extension of discrete time volatility models and (2) an abstract definition of volatility proxy. The theory is applied to eighteen years worth of S&P 500 index data. It is used to construct a proxy that outperforms realized volatility.volatility proxy ; realized volatility ; continuous time embedding ; intraday periodicity

Evolution:Adapting to a warming world

To be able to cope with climate change, species need to evolve. Demonstrating such evolution in wild populations is notoriously difficult. Replication of a 21-year-old experiment demonstrates that a long-distance migratory songbird has genetically adapted to climate change.</p