3,197 research outputs found
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
The Applications of Mixtures of Normal Distributions in Empirical Finance: A Selected Survey
This paper provides a selected review of the recent developments and applications of mixtures of normal (MN) distribution models in empirical finance. Once attractive property of the MN model is that it is flexible enough to accommodate various shapes of continuous distributions, and able to capture leptokurtic, skewed and multimodal characteristics of financial time series data. In addition, the MN-based analysis fits well with the related regime-switching literature. The survey is conducted under two broad themes: (1) minimum-distance estimation methods, and (2) financial modeling and its applications.Mixtures of Normal, Maximum Likelihood, Moment Generating Function, Characteristic Function, Switching Regression Model, (G) ARCH Model, Stochastic Volatility Model, Autoregressive Conditional Duration Model, Stochastic Duration Model, Value at Risk.
Bayesian Nonparametric Calibration and Combination of Predictive Distributions
We introduce a Bayesian approach to predictive density calibration and
combination that accounts for parameter uncertainty and model set
incompleteness through the use of random calibration functionals and random
combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010)
and Gneiting, T. and Ranjan, R. (2013), we use infinite beta mixtures for the
calibration. The proposed Bayesian nonparametric approach takes advantage of
the flexibility of Dirichlet process mixtures to achieve any continuous
deformation of linearly combined predictive distributions. The inference
procedure is based on Gibbs sampling and allows accounting for uncertainty in
the number of mixture components, mixture weights, and calibration parameters.
The weak posterior consistency of the Bayesian nonparametric calibration is
provided under suitable conditions for unknown true density. We study the
methodology in simulation examples with fat tails and multimodal densities and
apply it to density forecasts of daily S&P returns and daily maximum wind speed
at the Frankfurt airport.Comment: arXiv admin note: text overlap with arXiv:1305.2026 by other author
Mixed normal conditional heteroskedasticity
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQ-index data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect. Klassifikation: C22, C51, G1
Portfolio optimization with mixture vector autoregressive models
Obtaining reliable estimates of conditional covariance matrices is an
important task of heteroskedastic multivariate time series. In portfolio
optimization and financial risk management, it is crucial to provide measures
of uncertainty and risk as accurately as possible. We propose using mixture
vector autoregressive (MVAR) models for portfolio optimization. Combining a
mixture of distributions that depend on the recent history of the process, MVAR
models can accommodate asymmetry, multimodality, heteroskedasticity and
cross-correlation in multivariate time series data. For mixtures of Normal
components, we exploit a property of the multivariate Normal distribution to
obtain explicit formulas of conditional predictive distributions of returns on
a portfolio of assets. After showing how the method works, we perform a
comparison with other relevant multivariate time series models on real stock
return data.Comment: 19 pages, 9 figures, 2 table
A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation
Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian semiparametric approach. In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures of Gaussian distributions with a Dirichlet process prior on the mixing distribution, leading to a Dirichlet process mixture model. The proposed specification allows for a greater exibility in capturing both the skewness and kurtosis frequently observed in financial returns. The Bayesian model provides statistical inference with finite sample validity. Furthermore, it is also possible to obtain predictive distributions for the Value at Risk (VaR), which has become the most widely used measure of market risk for practitioners. Through a simulation study, we demonstrate the performance of the proposed semiparametric method and compare results with the ones from a normal distribution assumption. We also demonstrate the superiority of our proposed semiparametric method using real data from the Bombay Stock Exchange Index (BSE-30) and the Hang Seng Index (HSI).Bayesian estimation, Deviance information criterion, Dirichlet process mixture, Financial time series, Location-scale Gaussian mixture, Markov chain Monte Carlo
Dynamic dependence networks: Financial time series forecasting and portfolio decisions (with discussion)
We discuss Bayesian forecasting of increasingly high-dimensional time series,
a key area of application of stochastic dynamic models in the financial
industry and allied areas of business. Novel state-space models characterizing
sparse patterns of dependence among multiple time series extend existing
multivariate volatility models to enable scaling to higher numbers of
individual time series. The theory of these "dynamic dependence network" models
shows how the individual series can be "decoupled" for sequential analysis, and
then "recoupled" for applied forecasting and decision analysis. Decoupling
allows fast, efficient analysis of each of the series in individual univariate
models that are linked-- for later recoupling-- through a theoretical
multivariate volatility structure defined by a sparse underlying graphical
model. Computational advances are especially significant in connection with
model uncertainty about the sparsity patterns among series that define this
graphical model; Bayesian model averaging using discounting of historical
information builds substantially on this computational advance. An extensive,
detailed case study showcases the use of these models, and the improvements in
forecasting and financial portfolio investment decisions that are achievable.
Using a long series of daily international currency, stock indices and
commodity prices, the case study includes evaluations of multi-day forecasts
and Bayesian portfolio analysis with a variety of practical utility functions,
as well as comparisons against commodity trading advisor benchmarks.Comment: 31 pages, 9 figures, 3 table
Mixed exponential power asymmetric conditional heteroskedasticity
To match the stylized facts of high frequency financial time series precisely andparsimoniously, this paper presents a finite mixture of conditional exponential powerdistributions where each component exhibits asymmetric conditional heteroskedasticity. Weprovide stationarity conditions and unconditional moments to the fourth order. We apply thisnew class to Dow Jones index returns. We find that a two-component mixed exponentialpower distribution dominates mixed normal distributions with more components, and moreparameters, both in-sample and out-of-sample. In contrast to mixed normal distributions, allthe conditional variance processes become stationary. This happens because the mixedexponential power distribution allows for component-specific shape parameters so that it canbetter capture the tail behaviour. Therefore, the more general new class has attractive featuresover mixed normal distributions in our application: Less components are necessary and theconditional variances in the components are stationary processes. Results on NASDAQ indexreturns are similar.finite mixtures, exponential power distributions, conditional heteroskedasticity, asymmetry, heavy tails, value at risk
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