EMPIRICAL ANALYSIS OF THE RELATIONSHIP BETWEEN OIL AND PRECIOUS METALS MARKETS

2018 ◽  
Vol 13 (01) ◽  
pp. 1850003 ◽  
Author(s):  
KHALED MOKNI
Keyword(s):  
Crude Oil ◽  
Value At Risk ◽  
Precious Metals ◽  
Market Risk ◽  
Dependence Structure ◽  
Empirical Results ◽  
Dependence Modeling ◽  
Market Risks ◽  
Risk Managers ◽  
The Relationship

The relationship between crude oil and precious metals has been a major issue in economic and financial literature. In this paper, the FIEGARCH-copula framework was used to investigate the co-movements not only between returns, but also between volatilities and market risks among crude oil and precious metals markets. Based on daily crude oil and the major precious metals prices from January 2, 2000 to December 31, 2016, our empirical results are as follows: First, a significant positive and asymmetric relationship between oil and precious metals returns, volatilities and market risk was detected. Second, the dependence structure between oil-silver and oil-gold for returns and volatilities are time varying, while the other pairs are characterized by constant dependence. Third, based on the dependence modeling between daily Value-at-Risk (VaR) for the long and short trading position, empirical results show that the market risk relationship between crude oil and precious metals change over time and increase with VaR’s confidence level. Our findings are of interest for investors and risk managers in portfolio’s design and allow for a reliable framework for returns and risk prediction.

scholarly journals Modelling Crude Oil Returns Using the NRIG Distribution

2020 ◽  
Vol 9 (1) ◽  
pp. 204-222
Author(s):  
Knowledge Chinhamu ◽  
Nompilo Mabaso ◽  
Retius Chifurira
Keyword(s):  
Crude Oil ◽  
Value At Risk ◽  
Heavy Tails ◽  
Market Risk ◽  
Management Tool ◽  
Ratio Test ◽  
Volatility Clustering ◽  
Crude Oil Prices ◽  
Quantification Model ◽  
Risk Managers

Over the past decade, crude oil prices have risen dramatically, making the oil market very volatile and risky; hence, implementing an efficient risk management tool against market risk is crucial. Value-at-risk (VaR) has become the most common tool in this context to quantify market risk. Financial data typically have certain features such as volatility clustering, asymmetry, and heavy and semi-heavy tails, making it hard, if not impossible, to model them by using a normal distribution. In this paper, we propose the subclasses of the generalised hyperbolic distributions (GHDs), as appropriate models for capturing these characteristics for the crude oil and gasoline returns. We also introduce the new subclass of GHDs, namely normal reciprocal inverse Gaussian distribution (NRIG), in evaluating the VaR for the crude oil and gasoline market. Furthermore, VaR estimation and backtesting procedures using the Kupiec likelihood ratio test are conducted to test the extreme tails of these models. The main findings from the Kupiec likelihood test statistics suggest that the best GHD model should be chosen at various VaR levels. Thus, the final results of this research allow risk managers, financial analysts, and energy market academics to be flexible in choosing a robust risk quantification model for crude oil and gasoline returns at their specific VaR levels of interest. Particularly for NRIG, the results suggest that a better VaR estimation is provided at the long positions.

Dependency between Risks and the Insurer’s Economic Capital: A Copula-based GARCH Model

2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeungbo Shim ◽  
Seung-Hwan Lee
Keyword(s):  
Value At Risk ◽  
Heavy Tails ◽  
Garch Model ◽  
Joint Modeling ◽  
Market Risk ◽  
Capital Requirements ◽  
Economic Capital ◽  
Series Data ◽  
Dependence Structure ◽  
T Distribution

AbstractCopulas can be a useful tool to capture heavy-tailed dependence between risks in estimating economic capital. This paper provides a procedure of combining copula with GARCH model to construct a multivariate distribution. The copula-based GARCH model using a skewed student’s t-distribution controls for the issues of skewness, heavy tails, volatility clustering and conditional dependencies contained in the financial time series data. Using the sample of U.S. property liability insurance industry, we perform Monte Carlo simulation to estimate the insurer’s economic capital measured by Value-at-Risk (VaR) and Expected Shortfall (ES). The result indicates that the choice of dependence structure and business mix between asset classes and liability lines has a significant impact on the resulting capital requirements and diversification benefits. We find the incremental diversification benefit in terms of a reduction in the total capital requirement from the joint modeling of underwriting risk and market risk compared to the modeling of market risk only.

Oil Prices and Economic Growth in Major Emerging Economies

2020 ◽  
pp. 1-23
Author(s):  
Emrah I Cevik ◽  
Sel Dibooglu ◽  
Tugba Kantarci ◽  
Hande Caliskan
Keyword(s):  
Crude Oil ◽  
Emerging Economies ◽  
Economic Activity ◽  
Oil Prices ◽  
Energy Prices ◽  
Empirical Results ◽  
Crude Oil Prices ◽  
Causality Tests ◽  
The Difference ◽  
The Relationship

There is a strong correlation between energy prices and economic activity. The relationship particularly holds true for crude oil as changes in oil prices are associated with changes in production costs, and economic activity also generates significant demand for energy and crude oil. This chapter examines the relationship between economic activity and crude oil prices using causality tests in the frequency domain and taking into account the difference between positive and negative changes in both oil prices and economic activity as the relationship can be asymmetric. The authors present empirical results for major emerging economies including Brazil, Russia, India, China, South Africa, and Turkey. Empirical results indicate that for most countries there is bidirectional causality between crude oil prices and economic activity whereas only negative oil price shocks seem to negatively affect economic activity.

Impact of non-normal return and market capitalization on estimation of VaR

2015 ◽  
Vol 7 (3) ◽  
pp. 222-242
Author(s):  
Pankaj Sinha ◽  
Shalini Agnihotri
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Empirical Distribution ◽  
Parametric Method ◽  
Market Risk ◽  
Content Type ◽  
Market Capitalization ◽  
Stock Indices ◽  
Relationship Of ◽  
The Relationship

Purpose – This paper aims to investigate the effect of non-normality in returns and market capitalization of stock portfolios and stock indices on value at risk and conditional VaR estimation. It is a well-documented fact that returns of stocks and stock indices are not normally distributed, as Indian financial markets are more prone to shocks caused by regulatory changes, exchange rate fluctuations, financial instability, political uncertainty and inadequate economic reforms. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. Design/methodology/approach – In this paper, VaR is estimated by fitting empirical distribution of returns, parametric method and by using GARCH(1,1) with Student’s t innovation method. Findings – It is observed that both the stocks, stock indices and their residuals exhibit non-normality; therefore, conventional methods of VaR calculation are not accurate in real word situation. It is observed that parametric method of VaR calculation is underestimating VaR and CVaR but, VaR estimated by fitting empirical distribution of return and finding out 1-a percentile is giving better results as non-normality in returns is considered. The distributions fitted by the return series are following Logistic, Weibull and Laplace. It is also observed that VaR violations are increasing with decreasing market capitalization. Therefore, we can say that market capitalization also affects accurate VaR calculation. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. It is observed that the decrease in liquidity increases the value at risk of the firms. Research limitations/implications – This methodology can further be extended to other assets’ VaR calculation like foreign exchange rates, commodities and bank loan portfolios, etc. Practical implications – This finding can help risk managers and mutual fund managers (as they have portfolios of different assets size) in estimating VaR of portfolios with non-normal returns and different market capitalization with precision. VaR is used as tool in setting trading limits at trading desks. Therefore, if VaR is calculated which takes into account non-normality of underlying distribution of return then trading limits can be set with precision. Hence, both risk management and risk measurement through VaR can be enhanced if VaR is calculated with accuracy. Originality/value – This paper is considering the joint issue of non-normality in returns and effect of market capitalization in VaR estimation.

scholarly journals Market-Risk Optimization among the Developed and Emerging Markets with CVaR Measure and Copula Simulation

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 78
Author(s):  
Trabelsi ◽  
Tiwari
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Value At Risk ◽  
Risk Measure ◽  
Generalized Pareto Distribution ◽  
Market Risk ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
Stock Market Returns ◽  
Pareto Tails

In this paper, the generalized Pareto distribution (GPD) copula approach is utilized to solve the conditional value-at-risk (CVaR) portfolio problem. Particularly, this approach used (i) copula to model the complete linear and non-linear correlation dependence structure, (ii) Pareto tails to capture the estimates of the parametric Pareto lower tail, the non-parametric kernel-smoothed interior and the parametric Pareto upper tail and (iii) Value-at-Risk (VaR) to quantify risk measure. The simulated sample covers the G7, BRICS (association of Brazil, Russia, India, China and South Africa) and 14 popular emerging stock-market returns for the period between 1997 and 2018. Our results suggest that the efficient frontier with the minimizing CVaR measure and simulated copula returns combined outperforms the risk/return of domestic portfolios, such as the US stock market. This result improves international diversification at the global level. We also show that the Gaussian and t-copula simulated returns give very similar but not identical results. Furthermore, the copula simulation provides more accurate market-risk estimates than historical simulation. Finally, the results support the notion that G7 countries can provide an important opportunity for diversification. These results are important to investors and policymakers.

scholarly journals Measuring Contagion of Subprime Crisis Based on MVMQ-CAViaR Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Wuyi Ye ◽  
Kebing Luo ◽  
Shaofu Du
Keyword(s):  
Portfolio Management ◽  
Value At Risk ◽  
Global Financial Crisis ◽  
Recovery Period ◽  
Sampling Period ◽  
Market Risk ◽  
Dependence Structure ◽  
Financial Contagion ◽  
Subprime Crisis ◽  
The Global Financial Crisis

The analysis of financial contagion is a topical issue in international finance and portfolio management. In this paper, we investigate whether the global financial crisis originating from American subprime crisis spreads to China, Japan, UK, France, and Germany. Firstly, multivariate conditional autoregressive value at risk (MV-CAViaR) models are applied to the whole sample to analyze the variation of market risk among these countries. By dividing the sampling period into three important subperiods (precrisis period, crisis period, and recovery period), we examine the changes of the dependence structure of risk during each period. Comparing with the situations in precrisis period, if the estimated coefficients become significant or market risk increases during the crisis, it implies the existence of contagion from the angle of coefficient significance or risk. The findings show that the concerned coefficients are significant or the market risks of the tested countries increase during the crisis except for China. The results imply that there is contagion from the US to all other countries, except for China. Furthermore, the changes of the market risk are found to be consistent with market events and media reports during that period.

scholarly journals Can Stock Analysts Predict Market Risk? New Evidence from Copula Theory

2019 ◽  
Vol 23 (1) ◽  
pp. 38-48
Author(s):  
I. S. Medovikov
Keyword(s):  
Asset Management ◽  
Value At Risk ◽  
Realized Volatility ◽  
Market Risk ◽  
Stock Selection ◽  
Copula Theory ◽  
Trading Simulation ◽  
New Evidence ◽  
The Relationship ◽  
Stock Analysts

We assess investment value of stock recommendations from the standpoint of market risk. We match I/B/E/S (Institutional Brokers’ Estimates System) consensus recommendations issued in January 2015 for a cross-section of u.S. public equities with realized volatility of these papers, showing that these recommendations signifcantly correlate with subsequent changes in market risk. Thus, the results indicate that to some extent the analysts can predict an increase or decrease in risk, which can beneft asset management. However, the relationship between the recommendations and the risk is not linear and depends on the specifc recommendation. using a semi-parametric copula model, we fnd recommendation levels to be associated with future changes in volatility. We further fnd this relationship to be asymmetric and most pronounced among the best-rated stocks which experience largest volatility declines. We conduct a trading simulation showing how stock selection based on such ratings can lead to a reduction in portfolio-level value-at-risk.

scholarly journals RISK OF INDONESIAN BANKS: AN APPLICATION OF HISTORICAL EXPECTED SHORTFALL METHOD

2015 ◽  
Vol 17 (3) ◽  
pp. 299-314
Author(s):  
Nevi Danila ◽  
Bunyamin Bunyamin ◽  
Siti Munfaqiroh
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Value At Risk ◽  
Balance Sheet ◽  
Market Risk ◽  
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 Modeling risk dependence and portfolio VaR forecast through vine copula for cryptocurrencies

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242102
Author(s):  
Khreshna Syuhada ◽  
Arief Hakim
Keyword(s):  
Value At Risk ◽  
Risk Model ◽  
Forecast Accuracy ◽  
Dependence Structure ◽  
Financial Risks ◽  
Portfolio Risk ◽  
Dependence Modeling ◽  
Vine Copula ◽  
Dependence Assumption ◽  
Marginal Risk

Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk in insurance. It is often that we encounter several risks, in practice, instead of single risk. In this paper, we construct a dependence modeling for financial risks and form a portfolio risk of cryptocurrencies. The marginal risk model is assumed to follow a heteroscedastic process of GARCH(1,1) model. The dependence structure is presented through vine copula. We carry out numerical analysis of cryptocurrencies returns and compute Value-at-Risk (VaR) forecast along with its accuracy assessed through different backtesting methods. It is found that the VaR forecast of returns, by considering vine copula-based dependence among different returns, has higher forecast accuracy than that of returns under prefect dependence assumption as benchmark. In addition, through vine copula, the aggregate VaR forecast has not only lower value but also higher accuracy than the simple sum of individual VaR forecasts. This shows that vine copula-based forecasting procedure not only performs better but also provides a well-diversified portfolio.

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