9,554 research outputs found
Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns
In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving average (MA), a recurrent NN and a parametric GACH in terms of their ability to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange rates from July 2, 2003 to June 30, 2005 and New York Stock Exchange (NYSE) daily composite index from July 3, 2003 to June 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms the MA, the recurrent NN and the parametric GARCH based on the criteria of mean absolute error (MAE) and directional accuracy (DA). No structured way being available to choose the free parameters of SVR, the sensitivity of performance is also examined to the free parameters.recurrent support vector regression, GARCH model, volatility forecasting
Testing for the best alternative with an application to performance measurement
Suppose that we are searching for the maximum of many unknown and analytically untractable quantities or, say, the 'best alternative' among several candidates. If our decision is based on historical or simulated data there is some sort of selection bias and it is not evident if our choice is significantly better than any other. In the present work a large sample test for the best alternative is derived in a rather general setting. The test is demonstrated by an application to financial data and compared with the Jobson-Korkie test for the Sharpe ratios of two asset portfolios. We find that ignoring conditional heteroscedasticity and non-normality of asset returns can lead to misleading decisions. In contrast, the presented test for the best alternative accounts for these kinds of phenomena. --Ergodicity,Gordin's condition,heteroscedasticity,Jobson-Korkie test,Monte Carlo simulation,performance measurement,Sharpe ratio
A New Algorithm for Exploratory Projection Pursuit
In this paper, we propose a new algorithm for exploratory projection pursuit.
The basis of the algorithm is the insight that previous approaches used fairly
narrow definitions of interestingness / non interestingness. We argue that
allowing these definitions to depend on the problem / data at hand is a more
natural approach in an exploratory technique. This also allows our technique
much greater applicability than the approaches extant in the literature.
Complementing this insight, we propose a class of projection indices based on
the spatial distribution function that can make use of such information.
Finally, with the help of real datasets, we demonstrate how a range of
multivariate exploratory tasks can be addressed with our algorithm. The
examples further demonstrate that the proposed indices are quite capable of
focussing on the interesting structure in the data, even when this structure is
otherwise hard to detect or arises from very subtle patterns.Comment: 29 pages, 8 figure
Robust Mean-Variance Portfolio Selection
This paper investigates model risk issues in the context of mean-variance portfolio selection. We analytically and numerically show that, under model misspecification, the use of statistically robust estimates instead of the widely used classical sample mean and covariance is highly beneficial for the stability properties of the mean-variance optimal portfolios. Moreover, we perform simulations leading to the conclusion that, under classical estimation, model risk bias dominates estimation risk bias. Finally, we suggest a diagnostic tool to warn the analyst of the presence of extreme returns that have an abnormally large influence on the optimization results.Mean-variance e .cient frontier; Outliers; Model risk; Robust es-timation
ROC-Based Model Estimation for Forecasting Large Changes in Demand
Forecasting for large changes in demand should benefit from different estimation than that used for estimating mean behavior. We develop a multivariate forecasting model designed for detecting the largest changes across many time series. The model is fit based upon a penalty function that maximizes true positive rates along a relevant false positive rate range and can be used by managers wishing to take action on a small percentage of products likely to change the most in the next time period. We apply the model to a crime dataset and compare results to OLS as the basis for comparisons as well as models that are promising for exceptional demand forecasting such as quantile regression, synthetic data from a Bayesian model, and a power loss model. Using the Partial Area Under the Curve (PAUC) metric, our results show statistical significance, a 35 percent improvement over OLS, and at least a 20 percent improvement over competing methods. We suggest management with an increasing number of products to use our method for forecasting large changes in conjunction with typical magnitude-based methods for forecasting expected demand
Statistical identiïŹcation of geometric parameters for high speed train catenary
Pantograph/catenary interaction is known to be strongly dependent on the static geometry of the catenary, this research thus seeks to build a statistical model of this geometry. Sensitivity analyses provide a selection of relevant parameters affecting the geometry. After correction for the dynamic nature of the measurement, provide a database of measurements. One then seeks to solve the statistical inverse problem using the maximum entropy principle and the maximum likelihood method. Two methods of multivariate density estimations are presented, the Gaussian kernel density estimation method and the Gaussian parametric method. The results provide statistical information on the signiïŹcant parameters and show that the messenger wire tension of the catenary hides sources of variability that are not yet taken into account in the model
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