scholarly journals Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2110
Author(s):  
Inés Jiménez ◽  
Andrés Mora-Valencia ◽  
Trino-Manuel Ñíguez ◽  
Javier Perote
Keyword(s):  
Risk Assessment ◽  
Value At Risk ◽  
Risk Measures ◽  
Joint Estimation ◽  
Portfolio Risk ◽  
Conditional Correlation ◽  
Confidence Levels ◽  
Stepwise Procedure ◽  
Parsimonious Model ◽  
Two Step Estimation

The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of dynamic equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio of cryptocurrencies by implementing backtesting techniques and for different risk measures: value-at-risk, expected shortfall and median shortfall. The results support our proposal showing that the SNP-DCC model has better performance for lower confidence levels than the SNP-DECO model and is more appropriate for portfolio diversification purposes.

scholarly journals Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

2020 ◽  
Author(s):  
Inés Jiménez ◽  
Andrés Mora-Valencia ◽  
Trino-Manuel Ñíguez ◽  
Javier Perote
Keyword(s):  
Risk Assessment ◽  
Value At Risk ◽  
Risk Measures ◽  
Joint Estimation ◽  
Portfolio Risk ◽  
Conditional Correlation ◽  
Confidence Levels ◽  
Stepwise Procedure ◽  
Parsimonious Model ◽  
Two Step Estimation

The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of Dynamic Equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio on cryptocurrencies by implementing backtesting techniques and for different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. The results support our proposal showing that the SNP-DCC model has better performance for a smaller confidence level than the SNP-DECO model, although both models perform similarly for higher confidence levels.

scholarly journals Estimation of the Portfolio Risk from Conditional Value at Risk Using Monte Carlo Simulation

2021 ◽  
Vol 17 (3) ◽  
pp. 370-380
Author(s):  
Ervin Indarwati ◽  
Rosita Kusumawati
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Estimation ◽  
Conditional Value At Risk ◽  
Portfolio Risk ◽  
Portfolio Returns

Portfolio risk shows the large deviations in portfolio returns from expected portfolio returns. Value at Risk (VaR) is one method for determining the maximum risk of loss of a portfolio or an asset based on a certain probability and time. There are three methods to estimate VaR, namely variance-covariance, historical, and Monte Carlo simulations. One disadvantage of VaR is that it is incoherent because it does not have sub-additive properties. Conditional Value at Risk (CVaR) is a coherent or related risk measure and has a sub-additive nature which indicates that the loss on the portfolio is smaller or equal to the amount of loss of each asset. CVaR can provide loss information above the maximum loss. Estimating portfolio risk from the CVaR value using Monte Carlo simulation and its application to PT. Bank Negara Indonesia (Persero) Tbk (BBNI.JK) and PT. Bank Tabungan Negara (Persero) Tbk (BBTN.JK) will be discussed in this study.  The  daily  closing  price  of  each  BBNI  and BBTN share from 6 January 2019 to 30 December 2019 is used to measure the CVaR of the two banks' stock portfolios with this Monte Carlo simulation. The steps taken are determining the return value of assets, testing the normality of return of assets, looking for risk measures of returning assets that form a normally distributed portfolio, simulate the return of assets with monte carlo, calculate portfolio weights, looking for returns portfolio, calculate the quartile of portfolio return as a VaR value, and calculate the average loss above the VaR value as a CVaR value. The results of portfolio risk estimation of the value of CVaR using Monte Carlo simulation on PT. Bank Negara Indonesia (Persero) Tbk and PT. Bank Tabungan Negara (Persero) Tbk at a confidence level of 90%, 95%, and 99% is 5.82%, 6.39%, and 7.1% with a standard error of 0.58%, 0.59%, and 0.59%. If the initial funds that will be invested in this portfolio are illustrated at Rp 100,000,000, it can be interpreted that the maximum possible risk that investors will receive in the future will not exceed Rp 5,820,000, Rp 6,390,000 and Rp 7,100,000 at the significant level 90%, 95%, and 99%

scholarly journals Value at Risk and Market Risk: Case of the Regional Securities Exchange

2020 ◽  
Vol 7 (6) ◽  
pp. 19
Author(s):  
Mouhamadou Saliou Diallo
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Financial Crisis ◽  
Value At Risk ◽  
Composite Index ◽  
Market Risk ◽  
Management Approach ◽  
Portfolio Risk ◽  
Confidence Levels ◽  
Determining Factor

The study of the BRVM market risk using the VaR method is a determining factor in assessing the performance of our equity portfolio composed of the BRVM composite index and the BRVM10 index. It has enabled us, with the help of Basel regulations, to use backtesting to determine the minimum amount of capital that an investor must hold per day to protect against risk. The kupiec test enables us to determine the reliability of VaR calculated at different confidence levels. The result of our study confirms, using the extreme VaR method, the robustness of our threshold-based portfolio risk management approach. It also confirms the problem of market attractiveness during times of financial crisis.

scholarly journals How to combine precious metals with corn in a risk-minimizing two-asset portfolio?

2021 ◽  
Vol 67 (No. 2) ◽  
pp. 60-69
Author(s):  
Dejan Živkov ◽  
Petra Balaban ◽  
Boris Kuzman
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Precious Metals ◽  
Conditional Value At Risk ◽  
Average Risk ◽  
Conditional Correlation ◽  
Dynamic Correlation ◽  
Asset Portfolio ◽  
Optimal Dynamic

This paper tries to find out which precious metal futures are the best hedging tools for corn spot commodity, taking into account three different risk measures – variance (Var), value at risk (VaR), and conditional value at risk (CVaR). For computation purposes, we use an optimal dynamic conditional correlation (DCC) specification for every considered pair. Our findings indicate that portfolio with gold outperforms the other three precious metals (silver, platinum, and palladium) with respect to all three risk metrics. The reason for such findings is two-fold. First, gold has the lowest average dynamic correlation with corn (below 11%), and gold also has the lowest average risk of all precious metals. The second-best combination is corn-platinum, whereas the corn-silver pair gives the worst hedging results. This happens because silver has the highest average dynamic correlation with corn (14.5%), but more importantly, silver is the riskiest commodity, which makes this asset unsuitable for combining with corn. According to the results, the ratio between corn and gold in a two-asset portfolio should be about 27 : 73.

Impact of factor models on portfolio risk measures: a structural approach

2012 ◽  
Vol 8 (2) ◽  
pp. 47-79 ◽  
Author(s):  
Marcos Escobar ◽  
Tobias Frielingsdorf ◽  
Rudi Zagst
Keyword(s):  
Risk Measures ◽  
Factor Models ◽  
Structural Approach ◽  
Portfolio Risk

scholarly journals Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

2021 ◽  
Author(s):  
Sheri Markose ◽  
Simone Giansante ◽  
Nicolas A. Eterovic ◽  
Mateusz Gatkowski
Keyword(s):  
Early Warning ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Market Price ◽  
System Stability ◽  
Conditional Value At Risk ◽  
Centrality Measures ◽  
Global Banking ◽  
Pair Method

AbstractWe analyse systemic risk in the core global banking system using a new network-based spectral eigen-pair method, which treats network failure as a dynamical system stability problem. This is compared with market price-based Systemic Risk Indexes, viz. Marginal Expected Shortfall, Delta Conditional Value-at-Risk, and Conditional Capital Shortfall Measure of Systemic Risk in a cross-border setting. Unlike paradoxical market price based risk measures, which underestimate risk during periods of asset price booms, the eigen-pair method based on bilateral balance sheet data gives early-warning of instability in terms of the tipping point that is analogous to the R number in epidemic models. For this regulatory capital thresholds are used. Furthermore, network centrality measures identify systemically important and vulnerable banking systems. Market price-based SRIs are contemporaneous with the crisis and they are found to covary with risk measures like VaR and betas.

scholarly journals Portfolio Optimization Constrained by Performance Attribution

2021 ◽  
Vol 14 (5) ◽  
pp. 201
Author(s):  
Yuan Hu ◽  
W. Brent Lindquist ◽  
Svetlozar T. Rachev
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Effect ◽  
Conditional Value At Risk ◽  
Positive Role ◽  
Performance Attribution ◽  
Dow Jones Industrial Average

This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE.

scholarly journals Sharing Risk – An Economic Perspective

2009 ◽  
Vol 39 (2) ◽  
pp. 591-613 ◽  
Author(s):  
Andreas Kull
Keyword(s):  
Value At Risk ◽  
Risk Sharing ◽  
Risk Measures ◽  
Insurance Industry ◽  
Risk Measure ◽  
Capital Allocation ◽  
Risk Capital ◽  
Risk Class ◽  
Capital Costs ◽  
Risk Transfer

AbstractWe revisit the relative retention problem originally introduced by de Finetti using concepts recently developed in risk theory and quantitative risk management. Instead of using the Variance as a risk measure we consider the Expected Shortfall (Tail-Value-at-Risk) and include capital costs and take constraints on risk capital into account. Starting from a risk-based capital allocation, the paper presents an optimization scheme for sharing risk in a multi-risk class environment. Risk sharing takes place between two portfolios and the pricing of risktransfer reflects both portfolio structures. This allows us to shed more light on the question of how optimal risk sharing is characterized in a situation where risk transfer takes place between parties employing similar risk and performance measures. Recent developments in the regulatory domain (‘risk-based supervision’) pushing for common, insurance industry-wide risk measures underline the importance of this question. The paper includes a simple non-life insurance example illustrating optimal risk transfer in terms of retentions of common reinsurance structures.

On the treatment of uncertainty in seismic vulnerability and portfolio risk assessment

2017 ◽  
Vol 47 (1) ◽  
pp. 87-104 ◽  
Author(s):  
Luis Sousa ◽  
Vitor Silva ◽  
Mário Marques ◽  
Helen Crowley
Keyword(s):  
Risk Assessment ◽  
Seismic Vulnerability ◽  
Portfolio Risk
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