23,643 research outputs found
Functional dynamic factor models with application to yield curve forecasting
Accurate forecasting of zero coupon bond yields for a continuum of maturities
is paramount to bond portfolio management and derivative security pricing. Yet
a universal model for yield curve forecasting has been elusive, and prior
attempts often resulted in a trade-off between goodness of fit and consistency
with economic theory. To address this, herein we propose a novel formulation
which connects the dynamic factor model (DFM) framework with concepts from
functional data analysis: a DFM with functional factor loading curves. This
results in a model capable of forecasting functional time series. Further, in
the yield curve context we show that the model retains economic interpretation.
Model estimation is achieved through an expectation-maximization algorithm,
where the time series parameters and factor loading curves are simultaneously
estimated in a single step. Efficient computing is implemented and a
data-driven smoothing parameter is nicely incorporated. We show that our model
performs very well on forecasting actual yield data compared with existing
approaches, especially in regard to profit-based assessment for an innovative
trading exercise. We further illustrate the viability of our model to
applications outside of yield forecasting.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS551 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Forecasting the term structure of government bond yields
Despite powerful advances in yield curve modeling in the last twenty years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach, which focuses on accurately fitting the cross section of interest rates at any given time but neglects time-series dynamics, nor the equilibrium approach, which focuses on time-series dynamics (primarily those of the instantaneous rate) but pays comparatively little attention to fitting the entire cross section at any given time and has been shown to forecast poorly. Instead, we use variations on the Nelson-Siegel exponential components framework to model the entire yield curve, period-by-period, as a three-dimensional parameter evolving dynamically. We show that the three time-varying parameters may be interpreted as factors corresponding to level, slope and curvature, and that they may be estimated with high efficiency. We propose and estimate autoregressive models for the factors, and we show that our models are consistent with a variety of stylized facts regarding the yield curve. We use our models to produce term-structure forecasts at both short and long horizons, with encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts. JEL Code: G1, E4, C
Modeling and forecasting electricity spot prices: A functional data perspective
Classical time series models have serious difficulties in modeling and
forecasting the enormous fluctuations of electricity spot prices. Markov regime
switch models belong to the most often used models in the electricity
literature. These models try to capture the fluctuations of electricity spot
prices by using different regimes, each with its own mean and covariance
structure. Usually one regime is dedicated to moderate prices and another is
dedicated to high prices. However, these models show poor performance and there
is no theoretical justification for this kind of classification. The merit
order model, the most important micro-economic pricing model for electricity
spot prices, however, suggests a continuum of mean levels with a functional
dependence on electricity demand. We propose a new statistical perspective on
modeling and forecasting electricity spot prices that accounts for the merit
order model. In a first step, the functional relation between electricity spot
prices and electricity demand is modeled by daily price-demand functions. In a
second step, we parameterize the series of daily price-demand functions using a
functional factor model. The power of this new perspective is demonstrated by a
forecast study that compares our functional factor model with two established
classical time series models as well as two alternative functional data models.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS652 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Short and long-term wind turbine power output prediction
In the wind energy industry, it is of great importance to develop models that
accurately forecast the power output of a wind turbine, as such predictions are
used for wind farm location assessment or power pricing and bidding,
monitoring, and preventive maintenance. As a first step, and following the
guidelines of the existing literature, we use the supervisory control and data
acquisition (SCADA) data to model the wind turbine power curve (WTPC). We
explore various parametric and non-parametric approaches for the modeling of
the WTPC, such as parametric logistic functions, and non-parametric piecewise
linear, polynomial, or cubic spline interpolation functions. We demonstrate
that all aforementioned classes of models are rich enough (with respect to
their relative complexity) to accurately model the WTPC, as their mean squared
error (MSE) is close to the MSE lower bound calculated from the historical
data. We further enhance the accuracy of our proposed model, by incorporating
additional environmental factors that affect the power output, such as the
ambient temperature, and the wind direction. However, all aforementioned
models, when it comes to forecasting, seem to have an intrinsic limitation, due
to their inability to capture the inherent auto-correlation of the data. To
avoid this conundrum, we show that adding a properly scaled ARMA modeling layer
increases short-term prediction performance, while keeping the long-term
prediction capability of the model
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