scholarly journals VALUE AT RISK AS A TOOL FOR ECONOMIC-MANAGERIAL DECISION-MAKING IN THE PROCESS OF TRADING IN THE FINANCIAL MARKET

2021 ◽  
Vol 15 (2) ◽  
pp. 13-26
Author(s):  
Mariana Dimitrova ◽  
Laurenţiu-Mihai Treapăt ◽  
Irina Tulyakova
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Regression Models ◽  
Explanatory Power ◽  
Value Added ◽  
Managerial Decision ◽  
Significance Levels ◽  
Mathematical Formulas ◽  
Derivatives Of

Research background: Risk is an integral part of the world of financial markets today. One of the best known and widespread methods of quantifying the risk of a securities portfolio is the concept of value at risk (VaR). The method quantifies the maximum possible loss of a securities portfolio for specific variables. We used the work of Carol Alexander as a basis for our contribution, whence we borrowed mathematical formulas and derivatives of normal linear VaR and VaR scaling. Purpose of the article: The aim of this study is to design our own method of using the VaR calculation in the trading process and to practically verify the explanatory power of such calculation. To meet this goal, we used our own designed and adjusted formulas to calculate normal linear VaR and scaling VaR. Methods: The purpose of these adjusted formulas is to calculate specific levels of significance of specific scenarios of the course of trading positions, which represent the probability of their occurrence. Subsequently, we used regression analysis and constructed two regression models to verify that the significance levels themselves were significant variables, and that they could explain the variability of the explanatory variable to such an extent that they could be considered as strong predictors in the trading process. Findings & Value added: Based on such research, we find that the resulting levels of significance of our proposed VaR calculation formulas are significant. Based on the compiled regression models, we also find that the dependence we identified is a strong one and can therefore be considered as systematic. Nevertheless, the materiality levels could explain only a small proportion of the variability of the variable being explained, and therefore could not be considered as strong predictors and thus involved in the trading process itself.

Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach

2018 ◽  
Vol 21 (02) ◽  
pp. 1850010 ◽  
Author(s):  
Yam Wing Siu
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Risk Premium ◽  
Value At Risk ◽  
Realized Volatility ◽  
Management Tool ◽  
Spot Price ◽  
Pricing Options ◽  
Significance Levels ◽  
Risk Approach

This paper examines the predicting power of the volatility indexes of VIX and VHSI on the future volatilities (or called realized volatility, [Formula: see text] of their respective underlying indexes of S&P500 Index, SPX and Hang Seng Index, HSI. It is found that volatilities indexes of VIX and VHSI, on average, are numerically greater than the realized volatilities of SPX and HSI, respectively. Further analysis indicates that realized volatility, if used for pricing options, would, on some occasions, result in greatest losses of 2.21% and 1.91% of the spot price of SPX and HSI, respectively while the greatest profits are 2.56% and 2.93% of the spot price of SPX and HSI, respectively, making it not an ideal benchmark for validating volatility forecasting techniques in relation to option pricing. Hence, a new benchmark (fair volatility, [Formula: see text] that considers the premium of option and the cost of dynamic hedging the position is proposed accordingly. It reveals that, on average, options priced by volatility indexes contain a risk premium demanded by the option sellers. However, the options could, on some occasions, result in greatest losses of 4.85% and 3.60% of the spot price of SPX and HSI, respectively while the greatest profits are 4.60% and 5.49% of the spot price of SPX and HSI, respectively. Nevertheless, it can still be a valuable tool for risk management. [Formula: see text]-values of various significance levels for value-at-risk and conditional value-at-value have been statistically determined for US, Hong Kong, Australia, India, Japan and Korea markets.

scholarly journals How Risky Are the Options? A Comparison with the Underlying Stock Using MaxVaR as a Risk Measure

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 76
Author(s):  
Saswat Patra ◽  
Malay Bhattacharyya
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Value At Risk ◽  
Risk Measure ◽  
Stock Index ◽  
Risk Exposure ◽  
Index Options ◽  
Significance Level ◽  
Significance Levels ◽  
Better Than

This paper investigates the risk exposure for options and proposes MaxVaR as an alternative risk measure which captures the risk better than Value-at-Risk especially. While VaR is a measure of end-of-horizon risk, MaxVaR captures the interim risk exposure of a position or a portfolio. MaxVaR is a more stringent risk measure as it assesses the risk during the risk horizon. For a 30-day maturity option, we find that MaxVaR can be 40% higher than VaR at a 5% significance level. It highlights the importance of MaxVaR as a risk measure and shows that the risk is vastly underestimated when VaR is used as the measure for risk. The sensitivity of MaxVaR with respect to option characteristics like moneyness, time to maturity and risk horizons at different significance levels are observed. Further, interestingly enough we find that the MaxVar to VaR ratio is higher for stocks than the options and we can surmise that stock returns are more volatile than options. For robustness, the study is carried out under different distributional assumptions on residuals and for different stock index options.

scholarly journals Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process

2021 ◽  
Author(s):  
Massimiliano Frezza ◽  
Sergio Bianchi ◽  
Augusto Pianese
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Average Method ◽  
Moving Average ◽  
Computational Approach ◽  
Local Regularity ◽  
Price Process ◽  
Weighted Moving Average ◽  
Pointwise Regularity

AbstractA new computational approach based on the pointwise regularity exponent of the price time series is proposed to estimate Value at Risk. The forecasts obtained are compared with those of two largely used methodologies: the variance-covariance method and the exponentially weighted moving average method. Our findings show that in two very turbulent periods of financial markets the forecasts obtained using our algorithm decidedly outperform the two benchmarks, providing more accurate estimates in terms of both unconditional coverage and independence and magnitude of losses.

scholarly journals Non-extensive value-at-risk estimation during times of crisis

2021 ◽  
pp. 2150099
Author(s):  
Ahmad Hajihasani ◽  
Ali Namaki ◽  
Nazanin Asadi ◽  
Reza Tehrani
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Risk Estimation ◽  
Specific Pattern ◽  
Gaussian Probability Density Function ◽  
Confidence Levels ◽  
Control Instrument ◽  
The Difference ◽  
Non Gaussian

Value-at-risk (VaR) is a crucial subject that researchers and practitioners extensively use to measure and manage uncertainty in financial markets. Although VaR is a standard risk control instrument, there are criticisms about its performance. One of these cases, which has been studied in this research, is the VaR underestimation during times of crisis. In these periods, the non-Gaussian behavior of markets intensifies, and the estimated VaRs by typical models are lower than the real values. A potential approach that can be used to describe the non-Gaussian behavior of return series is the Tsallis entropy framework and nonextensive statistical methods. This paper has used the nonextensive models for analyzing financial markets’ behavior during crisis times. By applying the q-Gaussian probability density function for emerging and mature markets over 20 years, we can see a better VaR estimation than the regular models, especially during crisis times. We have shown that the q-Gaussian models composed of VaR and Expected Shortfall (ES) estimate risk better than the standard models. By comparing the ES, VaR, [Formula: see text]-VaR and [Formula: see text]-ES for emerging and mature markets, we see in confidence levels more than 0.98, the outputs of q models are more real, and the [Formula: see text]-ES model has lower errors than the other ones. Also, it is evident that in the mature markets, the difference of VaR between normal condition and nonextensive approach increases more than one standard deviation during times of crisis. Still, in the emerging markets, we cannot see a specific pattern. The findings of this paper are useful for analyzing the risk of financial crises in different markets.

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.

scholarly journals Commodity Indices – The End of an Era?

2014 ◽  
Vol 5 (2) ◽  
pp. 45-65
Author(s):  
Alex Büscher ◽  
Eric Frère ◽  
Gerrit Hellwig ◽  
Svend Reuse
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
20Th Century ◽  
Value At Risk ◽  
Risk Measurement ◽  
Classical Approach ◽  
Safe Haven ◽  
Us Dollar

Commodities are very important for the welfare of whole nations and so an increased demand, even on the financial markets, can be seen in the 20th century. For this reason commodities were no longer only product factors. They became more and more a speculative character for investors, especially in times of crisis as a possible safe haven (Mildner / Rudloff / Westphal, 2012, p. 57). Because of their development over two decades, during which time the invested volume grew up to an amount of 320 Billion US-Dollar at the beginning of 2011 (Knoepfel, 2011, p. 2) and the return of investing in commodities had beaten traditional investments, it might be very interesting to invest in commodity indices, if they can diversify an investor´s portfolio while improving the return. For the valuation and comparison of traditional and commodity indices, this article uses the classical approach of the volatility and the Value at Risk (VaR) for risk measurement and logarithmic returns for the performances. The analysis is indexed on July 1998 to get comparable results and aims to test if commodities can diversify a portfolio any longer.

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