A comparison of several time-series models for assessing the value at risk of shares

2001 ◽  
Vol 17 (1) ◽  
pp. 135-148 ◽  
Author(s):  
Walter Zucchini ◽  
Kristin Neumann
Keyword(s):  
At Risk ◽  
Time Series ◽  
Value At Risk ◽  
Time Series Models

scholarly journals Smoothing methods for histogram-valued time series: an application to value-at-risk

2011 ◽  
Vol 4 (2) ◽  
pp. 216-228 ◽  
Author(s):  
Javier Arroyo ◽  
Gloria González-Rivera ◽  
Carlos Maté ◽  
Antonio Muñoz San Roque
Keyword(s):  
At Risk ◽  
Time Series ◽  
Value At Risk ◽  
Smoothing Methods

Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

2017 ◽  
Vol 67 ◽  
pp. 355-367 ◽  
Author(s):  
Heng-Guo Zhang ◽  
Chi-Wei Su ◽  
Yan Song ◽  
Shuqi Qiu ◽  
Ran Xiao ◽  
...  
Keyword(s):  
At Risk ◽  
Time Series ◽  
Value At Risk ◽  
High Dimensional ◽  
Mapping Model ◽  
Random Mapping

VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS

2006 ◽  
Vol 16 (05) ◽  
pp. 371-382 ◽  
Author(s):  
EDMOND H. C. WU ◽  
PHILIP L. H. YU ◽  
W. K. LI
Keyword(s):  
At Risk ◽  
Time Series ◽  
Independent Component Analysis ◽  
Value At Risk ◽  
Risk Estimation ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Garch Models ◽  
Computationally Efficient

We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.

Time series modeling for risk of stock price with value at risk computation

2015 ◽  
Vol 9 ◽  
pp. 2779-2787 ◽  
Author(s):  
Dodi Devianto ◽  
Maiyastri ◽  
Dian Rezki Fadhilla
Keyword(s):  
At Risk ◽  
Time Series ◽  
Value At Risk ◽  
Stock Price ◽  
Time Series Modeling

scholarly journals Using High-Frequency Entropy to Forecast Bitcoin’s Daily Value at Risk

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 102 ◽  
Author(s):  
Daniel Pele ◽  
Miruna Mazurencu-Marinescu-Pele
Keyword(s):  
At Risk ◽  
Time Series ◽  
High Frequency ◽  
Value At Risk ◽  
Explanatory Power ◽  
High Resolution Data ◽  
Symbolic Time Series Analysis ◽  
Daily Returns ◽  
Resolution Data ◽  
Daily Time

In this paper we investigate the ability of several econometrical models to forecast value at risk for a sample of daily time series of cryptocurrency returns. Using high frequency data for Bitcoin, we estimate the entropy of intraday distribution of logreturns through the symbolic time series analysis (STSA), producing low-resolution data from high-resolution data. Our results show that entropy has a strong explanatory power for the quantiles of the distribution of the daily returns. Based on Christoffersen’s tests for Value at Risk (VaR) backtesting, we can conclude that the VaR forecast build upon the entropy of intraday returns is the best, compared to the forecasts provided by the classical GARCH models.

A spectral analysis based heteroscedastic model for the estimation of value at risk

2018 ◽  
Vol 19 (3) ◽  
pp. 295-314
Author(s):  
Yang Zhao
Keyword(s):  
At Risk ◽  
Time Series ◽  
Spectral Analysis ◽  
Value At Risk ◽  
Financial Time Series ◽  
Estimation Method ◽  
Reliable Estimate ◽  
Content Type ◽  
Financial Time ◽  
Proposed Model

Purpose This paper aims to focus on a better model to capture the trait of varying volatility in various financial time series, as well as to obtain reliable estimate of value at risk (VaR). Design/methodology/approach The typical methods in spectral analysis are used to obtain the sample of conditional mean, conditional variance and residual term. The generalized regression neural network is used to establish a time-varying non-linear model, and the non-parametric kernel density estimation method is applied for the estimation of VaR. Findings The proposed model is able to follow the heteroscedastic characteristic which is common in financial time series, and the estimated VaR is satisfactory. Practical implications The analysis method in this study allows the hedgers, bankers, financial analysts as well as economists to draw a better inference from financial time series. Also, relatively more precise estimate of the VaR value for a certain kind of financial asset is available. The model with its derived estimates would definitely help in developing other models. Originality/value Up-to-date, the study in literature which models financial time serial from the viewpoint of spectral analysis is rare to see. Thus, the proposed model, along with a comprehensive empirical study which reveals desirable result for the estimation of VaR would enrich the related researches at present.

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