Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation
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https://hdl.handle.net/10037/31609Date
2023-08-17Type
Journal articleTidsskriftartikkel
Peer reviewed
Abstract
Value at risk is a statistic used to anticipate the largest possible losses over a specific
time frame and within some level of confidence, usually 95% or 99%. For risk management and
regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become
the most widely used and accepted indicator of downside risk. Today, commercial banks and financial
institutions utilize it as a tool to estimate the size and probability of upcoming losses in portfolios
and, as a result, to estimate and manage the degree of risk exposure. The goal is to obtain the average
number of VaR “failures” or “breaches” (losses that are more than the VaR) as near to the target rate
as possible. It is also desired that the losses be evenly distributed as possible. VaR can be modeled in
a variety of ways. The simplest method is to estimate volatility based on prior returns according to
the assumption that volatility is constant. Otherwise, the volatility process can be modeled using the
GARCH model. Machine learning techniques have been used in recent years to carry out stock market
forecasts based on historical time series. A machine learning system is often trained on an in-sample
dataset, where it can adjust and improve specific hyperparameters in accordance with the underlying
metric. The trained model is tested on an out-of-sample dataset. We compared the baselines for the
VaR estimation of a day (d) according to different metrics (i) to their respective variants that included
stock return forecast information of d and stock return data of the days before d and (ii) to a GARCH
model that included return prediction information of d and stock return data of the days before
d. Various strategies such as ARIMA and a proposed ensemble of regressors have been employed
to predict stock returns. We observed that the versions of the univariate techniques and GARCH
integrated with return predictions outperformed the baselines in four different marketplaces.
Publisher
MDPICitation
Bagheri, Reforgiato Recupero, Sirnes. Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation. Data. 2023;8(8)Metadata
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