awliner.blogg.se

Real time commodity risk engine machine learning
Real time commodity risk engine machine learning













real time commodity risk engine machine learning

A set of economic and financial market variables that exhibit asset return predictability, serve as potential predictors.

real time commodity risk engine machine learning

(VIX, VXO, VXN, VXD) and three European (VDAX, VCAC, and VSTOXX) implied volatility indices are studied. index implied volatility can be forecast. Konstantinidi et al. ( 2008) explore whether European and U.S. In addition, Ahoniemi ( 2006) investigates the economic significance of the underlying forecasts and finds that implementing a trading strategy based on S&P 500 options does not achieve abnormal profits. Early studies include Aboura ( 2003), Ahoniemi ( 2006) and they conclude that the evolution of implied volatility indices is statistically predictable. The empirical evidence on the predictability of implied volatility indices is less extensive. Moreover, Goyal and Saretto ( 2006) use information stemming from the cross-section of various stock option implied volatilities to find predictable patterns in implied volatility dynamics and they conclude that there is both significant statistical and economic predictability. In a similar study, Gemmill and Kamiyama ( 2000) find that changes in index option implied volatilities in a certain market are impacted by lagged changes in other markets implied volatilities, i.e. A set of macroeconomic predictors is used to conclude that changes in implied volatility are partially predictable, but, once again, the results are not economically significant. Part of the literature has concentrated on the predictability of short-term at-the-money volatility more specifically, Harvey and Whaley ( 1992) study at the money option volatility for the S&P 100 index, Guo ( 2000) for the Philadelphia Stock Exchange currency options, and Brooks and Oozeer ( 2002) for LIFFE long Gilt futures and options markets. By contrast, Goncalves and Guidolin ( 2006) detect a statistically predictable pattern that cannot be meaningfully exploited due to the high transaction costs involved. They conclude that there is too much instability in the underlying forecasts for them to be useful for pricing and hedging purposes.

real time commodity risk engine machine learning

Dumas et al. ( 1998) explore whether S&P 500 implied volatility dynamics are predictable across different strike prices and expiry dates over alternative periods. It is noteworthy that a large part of the empirical evidence on implied volatility predictability is mixed. The VIX is one of the most recognized volatility measures globally.Ī substantial amount of research by both academics and practitioners has focused on the investigation of volatility forecasting and, consequently, on identifying variables that have predictive ability for time-varying volatility dynamics. More specifically, the VIX is designed to produce a measure of constant, 30-day expected volatility, derived from real-time, mid-quote prices of S&P 500 index call and put options. This study focuses on the Chicago Board Options Exchange's (CBOE) VIX index (VIX) that can be considered as a model-free estimator of the equity market's implied volatility. Realized volatility gauges the fluctuations of underlying securities or indices by measuring price changes over predetermined time periods, while implied volatility is a forward-looking metric that represents future expectations of the market's uncertainty. In practice, a number of different metrics have been introduced for the estimation of market volatility the most well-known and followed metrics, however, are realized and implied volatility. As such, identifying, but, more importantly, predicting aggregate market volatility is of critical importance for the implementation of effective asset allocation programs, investment and/or trading strategies and for hedging purposes in particular.Īs periods of transition from a low to a high market volatility regime can be very abrupt and relatively short-lived, the development of an effective modeling framework is of critical importance for the design and implementation of active portfolio immunization strategies in order to avoid sizeable drawdowns during periods of turmoil in particular. History shows that asset performance and its associated volatility is distinct and asymmetric during different market volatility regimes, as the pricing of market risk and, as a result, investor sentiment is time-varying. Over time, a great deal of attention has centered on the predictability of equity market volatility, be it realized and/or implied, as changes in market volatility have significant repercussions on investor preferences.















Real time commodity risk engine machine learning