1,846 research outputs found
Uncovering predictability in the evolution of the WTI oil futures curve
Accurately forecasting the price of oil, the world's most actively traded
commodity, is of great importance to both academics and practitioners. We
contribute by proposing a functional time series based method to model and
forecast oil futures. Our approach boasts a number of theoretical and practical
advantages including effectively exploiting underlying process dynamics missed
by classical discrete approaches. We evaluate the finite-sample performance
against established benchmarks using a model confidence set test. A realistic
out-of-sample exercise provides strong support for the adoption of our approach
with it residing in the superior set of models in all considered instances.Comment: 28 pages, 4 figures, to appear in European Financial Managemen
Quantile Forecast Combinations in Realised Volatility Prediction
This paper tests whether it is possible to improve point, quantile and density forecasts of realised volatility by conditioning on a set of predictive variables. We employ quantile autoregressive models augmented with macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarise the information content in the candidate predictors. Our findings suggest that no single variable is able to provide more information for the evolution of the volatility distribution beyond that contained in its own past. The best performing variable is the return on the stock market followed by the inflation rate. Our complete subset approach achieves superior point, quantile and density predictive performance relative to the univariate models and the autoregressive benchmark
On a generalised model for time-dependent variance with long-term memory
The ARCH process (R. F. Engle, 1982) constitutes a paradigmatic generator of
stochastic time series with time-dependent variance like it appears on a wide
broad of systems besides economics in which ARCH was born. Although the ARCH
process captures the so-called "volatility clustering" and the asymptotic
power-law probability density distribution of the random variable, it is not
capable to reproduce further statistical properties of many of these time
series such as: the strong persistence of the instantaneous variance
characterised by large values of the Hurst exponent (H > 0.8), and asymptotic
power-law decay of the absolute values self-correlation function. By means of
considering an effective return obtained from a correlation of past returns
that has a q-exponential form we are able to fix the limitations of the
original model. Moreover, this improvement can be obtained through the correct
choice of a sole additional parameter, . The assessment of its validity
and usefulness is made by mimicking daily fluctuations of SP500 financial
index.Comment: 6 pages, 4 figure
Cross-Sectional Distribution of GARCH Coefficients across S&P 500 Constituents: Time-Variation over the Period 2000-2012
Constructing seasonally adjusted data with time-varying confidence intervals
Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide confidence intervals around the seasonally adjusted data. One particularly useful time series model for seasonal adjustment is the basic structural time series [BSM] model. The usual premise of the BSM is that the variance of each of the components is constant. In this paper we address the possibility that the variance of the trend component in a macro-economic time series in some way depends on the business cycle. One reason for doing so is that one can expect that there is more uncertainty in recession periods. We extend the BSM by allowing for a business-cycle dependent variance in the level equation. Next we show how this affects the confidence intervals of seasonally adjusted data. We apply our extended BSM to monthly US unemployment and we show that the estimated confidence intervals for seasonally adjusted unemployment change with past changes in the oil price
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