37,340 research outputs found
Volatility forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1
Intraday forecasts of a volatility index: Functional time series methods with dynamic updating
As a forward-looking measure of future equity market volatility, the VIX
index has gained immense popularity in recent years to become a key measure of
risk for market analysts and academics. We consider discrete reported intraday
VIX tick values as realisations of a collection of curves observed sequentially
on equally spaced and dense grids over time and utilise functional data
analysis techniques to produce one-day-ahead forecasts of these curves. The
proposed method facilitates the investigation of dynamic changes in the index
over very short time intervals as showcased using the 15-second high-frequency
VIX index values. With the help of dynamic updating techniques, our point and
interval forecasts are shown to enjoy improved accuracy over conventional time
series models.Comment: 29 pages, 5 figures, To appear at the Annals of Operations Researc
Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization
The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle
Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage
trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a
Neural Network fitness function for financial forecasting purposes. This is done by
benchmarking the ARBF-PSO results with those of three different Neural Networks
architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model
(ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy.
More specifically, the trading and statistical performance of all models is investigated in a
forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time
series over the period January 1999 to March 2011 using the last two years for out-of-sample
testing
Multifractal model of asset returns with leverage effect
Multifractal processes are a relatively new tool of stock market analysis.
Their power lies in the ability to take multiple orders of autocorrelations
into account explicitly. In the first part of the paper we discuss the
framework of the Lux model and refine the underlying phenomenological picture.
We also give a procedure of fitting all parameters to empirical data. We
present a new approach to account for the effective length of power-law memory
in volatility. The second part of the paper deals with the consequences of
asymmetry in returns. We incorporate two related stylized facts, skewness and
leverage autocorrelations into the model. Then from Monte Carlo measurements we
show, that this asymmetry significantly increases the mean squared error of
volatility forecasts. Based on a filtering method we give evidence on similar
behavior in empirical data.Comment: 23 pages, 8 figures, updated some figures and references, fixed two
typos, accepted to Physica
Density Forecasting: A Survey
A density forecast of the realization of a random variable at some future time is an estimate of the probability distribution of the possible future values of that variable. This article presents a selective survey of applications of density forecasting in macroeconomics and finance, and discusses some issues concerning the production, presentation and evaluation of density forecasts.
Volatility Forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3,4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
Modelling and trading the Greek stock market with gene expression and genetic programing algorithms
This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter
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