Quantitative Estimation Approaches to the Eurobonds Market Risks

2017 ◽  
Vol 2017 (3) ◽  
pp. 109-131
Author(s):  
Irina Bessarabova
Keyword(s):  
At Risk ◽  
Risk Premium ◽  
Value At Risk ◽  
Oil And Gas ◽  
Quantitative Estimation ◽  
Market Risks ◽  
Risks Assessment ◽  
Oil And Gas Companies ◽  
Structural Solutions ◽  
Return Value

The paper provides the simulation of quantitative market risks assessment metrics Value-at-Risk and Expected Shortfall for a portfolio of eurobonds of Russian oil and gas companies, and for eurobonds of each particular company. As a result of the modeling, we noted an overall significant market risks` impact on the value of the analyzed securities and made a conclusion that it is impossible to completely neutralize the influence of market risks. In this regard, the author proposes and justifies the administrative and structural solutions and recommendations, the introduction of which will enable the eurobonds issuing companies to enhance investors` loyalty to their issues and thereby reduce their cost, i.e. mitigate the required investors` return (Value-at-Risk in this case acts as a risk-premium).

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 ESTIMASI VALUE AT RISK PORTOFOLIO SAHAM MENGGUNAKAN METODE GARCH-COPULA (Studi Kasus : Harga Penutupan Saham Harian Unilever Indonesia dan Kimia Farma Periode 1 Januari 2013- 31 Desember 2016)

2018 ◽  
Vol 7 (4) ◽  
pp. 397-407
Author(s):  
Lingga Bayu Prasetya ◽  
Dwi Ispriyanti ◽  
Alan Prahutama
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Tail Dependence ◽  
Conditional Heteroscedasticity ◽  
Mutual Correlation ◽  
Copula Method ◽  
Stock Portfolio ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Return Value ◽  
Return Data

Any investment in the stock market will earn returns accompanied by risks. Return and risk has a mutual correlation that equilibrium. The formation of a portfolio is intended to provide a lower risk or with the same risk but provide a higher return. Value at Risk (VaR) is a instrument to analyze risk management. Time series model used in stock return data that it has not normal distribution and heteroscedastisicity is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH-Copula is a combined method of GARCH and Copula. The Copula method is used in joint distribution modeling because it does not require the assumption of normality of the data and can capture tail dependence between each variable. This research uses return data from stock closing prices of Unilever Indonesia and Kimia Farma period January 1, 2013 until December 31, 2016. Copula model is selected based on the highest likelihood log value is Copula Clayton. Value at Risk estimates of Unilever Indonesia and Kimia Farma's stock portfolio on the same weight were performed using Monte Carlo simulation with backtesting of 30 days period data at 95% confidence level. Keywords : Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Copula, Value at Risk

Cost recovery: when reducing costs for an asset can increase costs for an operator

2018 ◽  
Vol 58 (2) ◽  
pp. 571
Author(s):  
Robin Ludwig
Keyword(s):  
At Risk ◽  
Joint Venture ◽  
Cost Allocation ◽  
Oil And Gas ◽  
Operating Model ◽  
Allocation Process ◽  
Reducing Costs ◽  
Unpredictable Environment ◽  
Contractual Obligations ◽  
Oil And Gas Companies

In an unpredictable environment, oil and gas companies within Australia need to reduce their cost of production in order to protect the company’s margins. Many large companies are doing this by adapting a traditional asset-based operating model to be a central functionally aligned model, and creating synergies through offshoring, outsourcing, combining corporate capabilities across global assets, and/or creating centres of excellence. A functionally aligned centralised operating model, however, has the potential to increase the complexity for companies when recovering costs from their joint venture partners. In order to be recoverable, an operator must prove that the costs incurred are still directly related to the asset and by moving functions away from the front line, attribution to assets can be complex. If not allocated, documented and communicated in line with the joint venture agreements, the recoverability of these costs is put at risk. It could, therefore, eventuate that the reduction of costs for an asset results in an increase of costs for the operator. When assessing the recoverability of costs, operators should consider: (i) documenting the end to end cost allocation process and communicate this with joint venture partners; (ii) embedding robust governance and tools to aid cost transparency; and (iii) reviewing their contractual obligations as operations evolve. If the appropriate steps are taken, an operator can reduce costs without increasing their own costs by putting recoverability at risk.

scholarly journals THE PERSISTENCY AND THE SUSTAINABILITY OF THE INDONESIA'S CURRENT ACCOUNT DEFICIT

2015 ◽  
Vol 17 (3) ◽  
pp. 315-338
Author(s):  
Tuti Eka Asmarani
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Value At Risk ◽  
Current Account Deficit ◽  
Balance Sheet ◽  
Expected Shortfall ◽  
Jel Classification ◽  
Small Enterprise ◽  
Basel Committee ◽  
Market Risks

Asian and European crises were witnesses of banks’ vulnerable due to market risks. The Basel Committee requires an internal risk assessment applying Value at Risk (VaR). However, a replacement of VaR with Expected Shortfall (ES) has been suggested recently due to an excessive losses produced by banks which are beyond VaR estimations. This paper studied the risk of Indonesian banks applying a historical expected shortfall. We used JIBOR (overnight) from 2009 – 2012 as a proxy of market risk. The assessment of a historical expected shortfall of the net position of 27 banks accounts for October 2012 showed that state owned banks placed among the five highest value of each component (net position) in the balance sheet, namely placement to Bank Indonesia, interbank placement, spot and derivatives claims, securities, and loans. It means that the state owned banks had the highest risk and were the most aggressive among Indonesian banks. It might be due to carrying some of the government’s program, such as small enterprise loans.  Keywords: expected shortfall, value at risk, banks, risk. JEL Classification: D81, G210

scholarly journals Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach

2019 ◽  
Vol 15 (1) ◽  
pp. 29
Author(s):  
F Sukono ◽  
Eman Lesmana ◽  
Dwi Susanti ◽  
Herlina Napitupulu ◽  
Yuyun Hidayat
Keyword(s):  
At Risk ◽  
Risk Premium ◽  
Value At Risk ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Investment Risk ◽  
Capital Asset ◽  
Asset Pricing Model ◽  
Back Testing ◽  
Market Index

Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARFIMA models.It is also assumed that the stock risk premium correlates with market risk premiums, and stock risk premiums some time before. The correlation will be analyzed using the ARMAX-GARCH model approach. The Modified Value-at-Risk was then formulated based on the Capital asset Pricing Model with the ARMAX-GARCH model approach.To measure the performance of Modified Value-at-Risk that has been formulated is done with back testing. Back testing is carried out based on the Lopez II method. As a case study, analyzed some data on 10 stocks traded on the capital market in Indonesia.The results of the analysis show that the market index return risk premium significantly follows the ARFIMA model, and the 10 share risk premium significantly follows the ARMAX-GARCH model. Based on the results of back testing calculations indicate that the Value-at-Risk Adjustedor Modified Value-at-Risk is very suitable to be used to measure investment risk in the 10 stocks analyzed.

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