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Dynamic Factor Variance-Covariance Model (DFVCM) makes multi-step forecasts of large variance-covariance matrix with dynamic factor model.
Overview
The Dynamic Factor Variance-Covariance Model (DFVCM) makes multi-step forecasts of multivariate volatilities of a large number time-series (e.g. those of numerous investable assets in many markets) by applying dynamic factor model (DFM).
The multi-step forecasts of multivariate volatilities are composed of contributions (1) from common factors of the time-series (e.g., volatility components caused by common economic and market conditions), (2) from estimated and forecasted multivariate auto-covariance matrix of the common factors (e.g., volatility jumps in panic, and drops in euphoria, markets), and (3) from dynamics unique to individual time-series (e.g., volatilities due to specific trajectories of individual equity shares).
The factor-based dimension-reduction capacity of DFVCM can work with big data sets of large number of time-series, which may cause difficulties for traditional multivariate GARCH model. Multi-step forecast by DFVCM is an advantage over static risk factor model.
Highlights
- Most of forecasts on sets of large number of time-series are either (1) to predict future values of time-series by, for example, DFM (dynamic factor model) or VAR / VARMA (vector autoregressive / moving-average) models, OR (2) to predict future variance-covariance matrixes by, for example, multivariate GARCH or risk-factor (static factors, either statistical or fundamental) models. Here, i4cast lists (DFM-based) DFVCM algorithm to make muti-step forecasts of large variance-covariance matrix of large set of time-series.
- Advantages of making multivariate volatility forecasts by DFM vs. by multivariate GARCH are presented by Alessi, Barigozzi and Capasso (2007) in “Dynamic factor GARCH: Multivariate volatility forecast for a large number of series”, LEM Working Paper Series, No. 2006/25, Pisa. Equations for making multi-step forecasts of multivariate volatilities by (DFM-based) DFVCM are detailed by i4cast LLC (2024) in “Introduction to Multi-step Forecast of Multivariate Volatility with Dynamic Factor Model”, https://github.com/i4cast/aws/blob/main/dynamic_factor_variance-covariance_model/publication/.
- In addition to DFVCM (to make volatility forecast), i4cast lists LMVAR model to make multi-step forecasts of values of the same set of time-series. Both DFVCM and LMVAR models are based on the SAME combination of LMDFM and YWpcAR algorithms by i4cast. The DFVCM is tuned by metrics evaluating volatility forecasts, while the LMVAR is tuned by metrics evaluating time-series forecasts. Different evaluation metrics can render same algorithm into different models.
Details
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Training Recommended | Algorithm training on the ml.m5.xlarge instance type | $0.10 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.10 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $0.10 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.10 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.10 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.10 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.10 |
ml.g4dn.2xlarge Inference (Batch) | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $0.10 |
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Delivery details
Amazon SageMaker algorithm
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Version release notes
The Dynamic Factor Variance-Covariance Model (DFVCM) makes multi-step forecasts of multivariate volatilities of a large number time-series by applying dynamic factor model (DFM).
The multi-step forecasts of multivariate volatilities are composed of contributions (1) from common factors of the time-series, (2) from estimated and forecasted multivariate auto-covariance matrix of the common factors, and (3) from dynamics unique to individual time-series.
The factor-based dimension-reduction capacity of DFVCM can work with big data sets of large number of time-series.
Additional details
Inputs
- Summary
The DFVCM algorithm takes, as input data, multiple time-series data contained in a CSV (comma separated value) data table, in a format of a CSV text-string or a CSV text-file.
Each row of the data table is for values of an individual time-series (TS). Row header is the label or symbol of the time-series. Each column is for values of all time-series at a specific moment in time. Column header is the time-index or time-stamp of the moment.
- Input MIME type
- text/csv
https://github.com/i4cast/aws/blob/main/dynamic_factor_variance-covariance_model/input/Weekly_VTS_6Yr.csv
https://github.com/i4cast/aws/blob/main/dynamic_factor_variance-covariance_model/input/Weekly_VTS_6Yr.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Values of time stamp | Each row of the data table is for values of an individual time-series (TS). Row header is the label or symbol of the time-series. Each column is for values of all time-series at a specific moment in time. Column header is the time-index or time-stamp of the moment. The first data column is for the earliest time and the last column for the most recent time. The current version of DFVCM requires equally spaced time-stamps. | Type: FreeText | Yes |
Support
Vendor support
For questions or call-back number, please send email to i4cast LLC at prod.i4cast@gmail.com .
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