Minimum Capital Requirement Calculations for UK Futures

Key to the imposition of appropriate minimum capital requirements on a daily basis requires accurate volatility estimation. Here, measures are presented based on discrete estimation of aggregated high frequency UK futures realisations underpinned by a continuous time framework. Squared and absolute returns are incorporated into the measurement process so as to rely on the quadratic variation of a diffusion process and be robust in the presence of fat tails. The realized volatility estimates incorporate the long memory property. The dynamics of the volatility variable are adequately captured. Resulting rescaled returns are applied to minimum capital requirement calculations.

Forecasting Stock Index Volatility: The Incremental Information in the Intraday High-Low Price Range

We compare the incremental information content of implied volatility and intraday high-low range volatility in the context of conditional volatilityforecasts for three major market indexes: the S&P 100, the S&P 500, and the Nasdaq 100. Evidence obtained from out-of-sample volatility forecasts indicates that neither implied volatility nor intraday high-low range volatility subsumes entirely the incremental information contained in the other. Our findings suggest that intraday high-low range volatility can usefully augment conditional volatility forecasts for these market indexes.

Are Price Limits on Futures Markets That Cool? Evidence from the Brazilian Mercantile and Futures Exchange

This paper investigates the impact of price limits on the Brazilian futures markets using high frequency data. The aim is to identify whether there is a cool-off or a magnet effect. For that purpose, we examine a tick-by-tick data set that includes all contracts on the S�o Paulo stock index futures traded on the Brazilian Mercantile and Futures Exchange from January 1997 to December 1999. The results indicate that the conditional mean features a floor cool-off effect, whereas the conditional variance significantly increases as the price approaches the upper limit. We then build a trading strategy that accounts for the cool-off effect in the conditional mean so as to demonstrate that the latter has not only statistical, but also economic significance. The in-sample Sharpe ratio indeed is way superior to the buy-and-hold benchmarks we consider, whereas out-of-sample results evince similar performances.Cool-off effect, Futures markets, Magnet effect, Price limits, Transactions data

A survey of announcement effects on foreign exchange volatility and jumps

This article reviews, evaluates, and links research that studies foreign exchange volatility reaction to macro announcements. Scheduled and unscheduled news typically raises volatility for about an hour and often causes price discontinuities or jumps. News contributes substantially to volatility but other factors contribute even more to periodic volatility. The same types of news that affect returns—payrolls, trade balance, and interest rate shocks—are also the most likely to affect volatility, and U.S. news tends to produce more volatility than foreign news. Recent research has linked news to volatility through the former’s effect on order flow. Empirical research has confirmed the predictions of microstructure theory on how volatility might depend on a number of factors: the precision of the information in the news, the state of the business cycle, and the heterogeneity of traders’ beliefs.Foreign exchange

High-Frequency and Model-Free Volatility Estimators

This paper focuses on volatility of financial markets, which is one of the most important issues in finance, especially with regard to modeling high-frequency data. Risk management, asset pricing and option valuation techniques are the areas where the concept of volatility estimators (consistent, unbiased and the most efficient) is of crucial concern. Our intention was to find the best estimator of true volatility taking into account the latest investigations in finance literature. Basing on the methodology presented in Parkinson (1980), Garman and Klass (1980), Rogers and Satchell (1991), Yang and Zhang (2000), Andersen et al. (1997, 1998, 1999a, 199b), Hansen and Lunde (2005, 2006b) and Martens (2007), we computed the various model-free volatility estimators and compared them with classical volatility estimator, most often used in financial models. In order to reveal the information set hidden in high-frequency data, we utilized the concept of realized volatility and realized range. Calculating our estimator, we carefully focused on Δ (the interval used in calculation), n (the memory of the process) and q (scaling factor for scaled estimators). Our results revealed that the appropriate selection of Δ and n plays a crucial role when we try to answer the question concerning the estimator efficiency, as well as its accuracy. Having nine estimators of volatility, we found that for optimal n (measured in days) and Δ (in minutes) we obtain the most efficient estimator. Our findings confirmed that the best estimator should include information contained not only in closing prices but in the price range as well (range estimators). What is more important, we focused on the properties of the formula itself, independently of the interval used, comparing the estimator with the same Δ, n and q parameter. We observed that the formula of volatility estimator is not as important as the process of selection of the optimal parameter n or Δ. Finally, we focused on the asymmetry between market turmoil and adjustments of volatility. Next, we put stress on the implications of our results for well-known financial models which utilize classical volatility estimator as the main input variable.financial market volatility, high-frequency financial data, realized volatility and correlation, volatility forecasting, microstructure bias, the opening jump effect, the bid-ask bounce, autocovariance bias, daily patterns of volatility, emerging markets

Modeling and explaining the dynamics of European Union allowance prices at high-frequency

In this paper we model the adjustment process of European Union Allowance (EUA) prices to the releases of announcements at high-frequency controlling for intraday periodicity, volatility clustering and volatility persistence. We find that the high-frequency EUA price dynamics are very well captured by a fractionally integrated asymmetric power GARCH process. The decisions of the European Commission on second National Allocation Plans have a strong and immediate impact on EUA prices. On the other hand, our results suggest that EUA prices are only weakly connected to indicators about the future economic development as well as the current economic activity. --EU ETS,EUA,Announcement Effects,Price Formation,Long Memory

Which news moves the euro area bond market?

This paper explores a long dataset (1999-2005) of intraday prices on German long-term bond futures and examines market responses to major macroeconomic announcements and ECB monetary policy releases. In general, adjustments in prices are quick and new information is usually incorporated into prices within five minutes of announcements. The volatility adjustment is more long-lasting than that in the conditional mean, and excess volatility can be observed up to 30 minutes after the releases. Overall, German bond markets tend to react more strongly to the surprise component in US macro releases compared to euro area and domestic releases, and the strength of those reactions to US releases has increased over the period considered. The paper also provides evidence that the outcome of German unemployment figures has been known to investors ahead of the prescheduled release. JEL Classification: E43, E44, E58intraday data, macroeconomic announcements, monetary policy

Realized volatility

Realized volatility is a nonparametric ex-post estimate of the return variation. The most obvious realized volatility measure is the sum of finely-sampled squared return realizations over a fixed time interval. In a frictionless market the estimate achieves consistency for the underlying quadratic return variation when returns are sampled at increasingly higher frequency. We begin with an account of how and why the procedure works in a simplified setting and then extend the discussion to a more general framework. Along the way we clarify how the realized volatility and quadratic return variation relate to the more commonly applied concept of conditional return variance. We then review a set of related and useful notions of return variation along with practical measurement issues (e.g., discretization error and microstructure noise) before briefly touching on the existing empirical applications.Stochastic analysis