Forecasting daily conditional volatility and h-step-ahead short and long Value-at-Risk accuracy: Evidence from financial data

A portfolio rebalancing model with self-finance strategy and consideration of V-shaped transaction cost is presented in this paper. Our main contribution is that a new constraint is introduced to confirm that the rebalance necessity of the existing portfolio needs to be adjusted. The constraint is constructed by considering both the transaction amount and transaction cost without any additional supply to the investment amount. The V-shaped transaction cost function is used to calculate the transaction cost of the portfolio, and conditional value at risk (CVaR) is used to measure the risk of the portfolios. Computational tests on practical financial data show that the proposed model is effective and the rebalanced portfolio increases the expected return of the portfolio and reduces the CVaR risk of the portfolio.

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Testing for Strategic Manipulation of Economic and Financial Data

Benford's Law ◽

10.23943/princeton/9780691147611.003.0013 ◽

2015 ◽

Author(s):

Charles C. Moul ◽

John V. C. Nye

Keyword(s):

At Risk ◽

Value At Risk ◽

Strategic Behavior ◽

Financial Data ◽

Benford’S Law ◽

Data Series ◽

The Past ◽

Strategic Manipulation ◽

Benford's Law ◽

The U.S

This chapter surveys applications of Benford's law within economics, such as its use in investigating the validity of macroeconomic data series. It argues that, given the strong interest in strategic behavior in economics, it makes sense to use Benford's law to investigate possible anomalies that suggest manipulation or other interference, especially when incentives increase for such tampering. The chapter then considers how a first-digit analysis informs Value at Risk (VAR) data from the U.S. financial sector over the past ten years. It finds that Benford's law fits precrisis data very well but is rejected for postcrisis data. Opportunities and incentives for such misreporting are then discussed.

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Evaluating value-at-risk measures in the presence of long memory conditional volatility

The Journal of Risk ◽

10.21314/jor.2008.172 ◽

2008 ◽

Vol 10 (3) ◽

pp. 79-110 ◽

Cited By ~ 24

Author(s):

Massimiliano Caporin

Keyword(s):

At Risk ◽

Long Memory ◽

Value At Risk ◽

Risk Measures ◽

Conditional Volatility

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Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

International Review of Financial Analysis ◽

10.1016/j.irfa.2012.06.001 ◽

2013 ◽

Vol 27 ◽

pp. 21-33 ◽

Cited By ~ 20

Author(s):

Stavros Degiannakis ◽

Christos Floros ◽

Pamela Dent

Keyword(s):

At Risk ◽

Value At Risk ◽

Expected Shortfall ◽

Integrated Models ◽

Conditional Volatility ◽

International Evidence

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A New Class of Heavy-Tailed Distributions: Modeling and Simulating Actuarial Measures

Complexity ◽

10.1155/2021/5580228 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Jin Zhao ◽

Zubair Ahmad ◽

Eisa Mahmoudi ◽

E. H. Hafez ◽

Marwa M. Mohie El-Din

Keyword(s):

At Risk ◽

Value At Risk ◽

Weibull Model ◽

Financial Data ◽

Statistical Distributions ◽

New Family ◽

Heavy Tailed Distributions ◽

Beta Power ◽

Heavy Tailed ◽

Modeling Data

Statistical distributions play a prominent role for modeling data in applied fields, particularly in actuarial, financial sciences, and risk management fields. Among the statistical distributions, the heavy-tailed distributions have proven the best choice to use for modeling heavy-tailed financial data. The actuaries are often in search of such types of distributions to provide the best description of the actuarial and financial data. This study presents a new power transformation to introduce a new family of heavy-tailed distributions useful for modeling heavy-tailed financial data. A submodel, namely, heavy-tailed beta-power transformed Weibull model is considered to demonstrate the adequacy of the proposed method. Some actuarial measures such as value at risk, tail value at risk, tail variance, and tail variance premium are calculated. A brief simulation study based on these measures is provided. Finally, an application to the insurance loss dataset is analyzed, which revealed that the proposed distribution is a superior model among the competitors and could potentially be very adequate in describing and modeling actuarial and financial data.

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The Efficiency of GARCH Models in Realizing Value at Risk Estimates

ACTA VŠFS Ekonomicke studie a analyzy ◽

10.37355/acta-2020/1-03 ◽

2020 ◽

Vol 14 (1) ◽

pp. 32-50

Author(s):

Tomáš Jeřábek

Keyword(s):

At Risk ◽

Value At Risk ◽

Financial Risk ◽

Market Risk ◽

Conditional Volatility ◽

Maximum Loss ◽

Specific Distribution ◽

Volatility Models ◽

Volatility Model ◽

Traditional Approaches

Market risk is an important type of financial risk that is usually caused by price fluctuations in financial markets. One determinant of market risk comprises Value at Risk (VaR), which is defined as the maximum loss that can be achieved within a certain time horizon and at a given reliability level. The aim of the article is to determine the importance of selecting conditional volatility model within the parametric and semi-parametric approach for VaR estimation. The results ascertained show that the application of these models tends to provide more accurate predictions of actual losses as compared to traditional approaches to VaR estimates. Overall, the application of conditional volatility models ensures that VaR estimates are more flexible to adapt to changing market conditions – especially in the periods associated with higher return volatility. Furthermore, the results show that the differences between individual models of contingent volatility are primarily determined by selecting the specific distribution of the standardized residue series

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Quantile-based smooth transition value at risk estimation

Econometrics Journal ◽

10.1093/ectj/utz009 ◽

2019 ◽

Vol 22 (3) ◽

pp. 241-261

Author(s):

Stefan Hubner ◽

Pavel Čížek

Keyword(s):

At Risk ◽

Value At Risk ◽

Risk Estimation ◽

Estimation Procedure ◽

Autoregressive Process ◽

Transition Function ◽

Smooth Transition ◽

Conditional Volatility ◽

Conditional Quantiles ◽

Two Step Estimation

Summary Value at risk models are concerned with the estimation of conditional quantiles of a time series. Formally, these quantities are a function of conditional volatility and the respective quantile of the innovation distribution. The former is often subject to asymmetric dynamic behaviour, e.g., with respect to past shocks. In this paper, we propose a model in which conditional quantiles follow a generalised autoregressive process governed by two parameter regimes with their weights determined by a smooth transition function. We develop a two-step estimation procedure based on a sieve estimator, approximating conditional volatility by using composite quantile regression, which is then used in the generalised autoregressive conditional quantile estimation. We show that the estimator is consistent and asymptotically normal, and we complement the results with a simulation study. In our empirical application, we consider daily returns of the German equity index (DAX) and the USD/GBP exchange rate. Although only the latter follows a two-regime model, we find that our model performs well in terms of out-of-sample prediction in both cases.

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