QUANTILE REGRESSION: CONCEPT AND RISK MEASURE PREDICTION
Time series often exhibit distinct changes in data pattern. Data with changed pattern can be modeled with markov switching regression. Markov switching model is dynamic regression whose switching between regimes follows the markov process. The expansion of markov switching model is markov switchi...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/47708 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Time series often exhibit distinct changes in data pattern. Data with changed
pattern can be modeled with markov switching regression. Markov switching
model is dynamic regression whose switching between regimes follows the markov
process. The expansion of markov switching model is markov switching
quantile regression model which is a dynamic quantile model. A special form
of markov switching quantile regression is quantile regression. Quantile regression
can provide a model for each quantile, thereby the picture of relationship
between random variables studied is more complete. Quantile regression has
properties that are an advantage of quantile regression namely robustness and
the absence of distribution assumptions. Quantile regression is the right choice
to predict quantile risk measures, particularly in Covid-19 risk prediction. The
most commonly used risk measure is Value-at-Risk (VaR). VaR prediction can
be done with a quantile regression models including QAR,QARCH, dan CAViaR.
VaR prediction with quantile regression model was applied to nancial
and health data. In the insurance sector, quantile regression can be used to
determine premiums and reserves. |
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