Functional Time Series with Applications in Finance and Insurance
A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 468
Special Issue Editor
2. School of Accounting and Finance, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Interests: functional time series; portfolio optimization; climate change modeling and climate change risks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Functional data analysis (FDA) came into prominence in the 1990s, with its defining feature that data are viewed conceptually as elements of a function space, for example, as continuous curves or surfaces, or more generally, as infinite dimensional objects. This contrasts with classical statistical theory, in which data are viewed as scalar or vector-valued. Early developments in FDA focused on simple random samples of curves or surfaces. Often, though, functional data are obtained sequentially as time series. Such data arise from a number of sources, including (i) breaking dense time records of a continuous phenomenon into natural segments, (ii) sequentially observed summary functions, etc. The analysis of such time series data falls within the domain of functional time series (FTS).
Topics of interest include applications of univariate/multivariate FTS in finance and/or insurance which, ideally, deal with one or more of the following issues in applications:
- Stationarity testing and change point analysis;
- Goodness-of-fit and white noise testing;
- Methods of bandwidth selection in spectral density operator estimation;
- Forecasting/prediction;
- etc.
Prof. Dr. Tony Wirjanto
Guest Editor
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Keywords
- Stationarity testing
- Change point analysis
- Goodness-of-fit
- White noise testing
- Bandwidth selection in spectral density operator estimation
- Forecasting/Prediction
- Finance
- Insurance
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