scholarly journals Out of sample value-at-risk and backtesting with the standardized pearson type-IV skewed distribution

2013 ◽  
Vol 60 (2) ◽  
pp. 231-247 ◽  
Author(s):  
Stavros Stavroyiannis ◽  
Leonidas Zarangas
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Skewed Distribution ◽  
Financial Analyst ◽  
Type Iv ◽  
Pearson Type ◽  
Out Of Sample ◽  
Sample Testing ◽  
T Distribution ◽  
Pearson Type Iv

This paper studies the efficiency of an econometric model where the volatility is modeled by a GARCH (1,1) process, and the innovations follow a standardized form of the Pearson type-IV distribution. The performance of the model is examined by in sample and out of sample testing, and the accuracy is explored by a variety of Value-at-Risk methods, the success/failure ratio, the Kupiec-LR test, the independence and conditional coverage tests of Christoffersen, the expected shortfall measures, and the dynamic quantile test of Engle and Manganelli. Overall, the proposed model is a valid and accurate model performing better than the skewed Student-t distribution, providing the financial analyst with a good candidate as an alternative distributional scheme.

Econometric modeling and value-at-risk using the Pearson type-IV distribution

2012 ◽  
Vol 22 ◽  
pp. 10-17 ◽  
Author(s):  
S. Stavroyiannis ◽  
I. Makris ◽  
V. Nikolaidis ◽  
L. Zarangas
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Econometric Modeling ◽  
Type Iv ◽  
Pearson Type ◽  
Pearson Type Iv

Value-at-risk for the long and short trading position with the Pearson type-IV distribution

2013 ◽  
Vol 15 (1) ◽  
pp. 14 ◽  
Author(s):  
Stavros Stavroyiannis ◽  
Ilias Makris ◽  
Vasilis Nikolaidis ◽  
Leonidas Zarangas
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Type Iv ◽  
Pearson Type ◽  
Pearson Type Iv

Value-at-risk and related measures for the Bitcoin

2018 ◽  
Vol 19 (2) ◽  
pp. 127-136 ◽  
Author(s):  
Stavros Stavroyiannis
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Capital Allocation ◽  
Capital Requirements ◽  
Spot Price ◽  
Content Type ◽  
Type Iv ◽  
Pearson Type ◽  
Standard And Poor's

Purpose The purpose of this paper is to examine the value-at-risk and related measures for the Bitcoin and to compare the findings with Standard and Poor’s SP500 Index, and the gold spot price time series. Design/methodology/approach A GJR-GARCH model has been implemented, in which the residuals follow the standardized Pearson type-IV distribution. A large variety of value-at-risk measures and backtesting criteria are implemented. Findings Bitcoin is a highly volatile currency violating the value-at-risk measures more than the other assets. With respect to the Basel Committee on Banking Supervision Accords, a Bitcoin investor is subjected to higher capital requirements and capital allocation ratio. Practical implications The risk of an investor holding Bitcoins is measured and quantified via the regulatory framework practices. Originality/value This paper is the first comprehensive approach to the risk properties of Bitcoin.

scholarly journals Forecasting Value at Risk: Evidence from Emerging Economies in Asia

2018 ◽  
Vol 2 (1) ◽  
pp. 77-90
Author(s):  
Le Trung Thanh ◽  
Nguyen Thi Ngan ◽  
Hoang Trung Nghia
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Stock Returns ◽  
Emerging Economies ◽  
Value At Risk ◽  
Stock Index ◽  
Historical Simulation ◽  
Out Of Sample ◽  
Student’S T ◽  
T Distribution

In this paper, various Value-at-Risk techniques are applied to stock indices of 9 Asian emerging financial markets. The results from our selected models are then backtested by Unconditional Coverage, Independence, Joint Tests of Unconditional Coverage and Independence and Basel tests to ensure the quality of Value-at-Risk (VaR) estimates. The main conclusions are: (1) Timevarying volatility is the most important characteristic of stock returns when modelling VaR; (2) Financial data is not normally distributed, indicating that the normality assumption of VaR is not relevant; (3) Among VAR forecasting approaches, the backtesting based on in- and out-of-sample evaluations confirms its superiority in the class of GARCH models; Historical Simulation (HS), Filtered Historical Simulation (FHS), RiskMetrics and Monte Carlo were rejected because of its underestimation (for HS and RiskMetrics) or overestimation (for the FHS and Monte Carlo); (4) Models under student’s t and skew student’s t distribution are better in taking into account financial data’s characters; and (5) Forecasting VaR for futures index is harder than for stock index. Moreover, results show that there is no evidence to recommend the use of GARCH (1,1) to estimate VaR for all markets. In practice, the HS and RiskMetrics are popularly used by banks for large portfolios, despite of its serious underestimations of actual losses. These findings would be helpful for financial managers, investors and regulators dealing with stock markets in Asian emerging economies.  

scholarly journals Risk-based constraints with correlated uncertainties for the optimal operation of an energy community

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Measurement Data ◽  
Real Life ◽  
Optimal Operation ◽  
Conditional Value At Risk ◽  
Forecast Errors ◽  
Worst Case ◽  
Life Measurement ◽  
Out Of Sample

<div>This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. </div><div>First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent.</div><div>To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics.</div>

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