The Limitations of Standard Deviation as a Measure of Bond Portfolio Risk

2003 ◽  
Vol 6 (3) ◽  
pp. 35-38 ◽  
Author(s):  
Brett H Wander ◽  
Ron D'Vari
Keyword(s):  
Standard Deviation ◽  
Portfolio Risk ◽  
Bond Portfolio

Contagion effect on bond portfolio risk measures in a hybrid credit risk model

2014 ◽  
Vol 11 (2) ◽  
pp. 131-139 ◽  
Author(s):  
Mathieu Boudreault ◽  
Geneviève Gauthier ◽  
Tommy Thomassin
Keyword(s):  
Credit Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Contagion Effect ◽  
Portfolio Risk ◽  
Bond Portfolio ◽  
Credit Risk Model

scholarly journals The GARCH (1, 1) Model As A Risk Predictor For International Portfolios

2011 ◽  
Vol 7 (11) ◽  
Author(s):  
Jean-François Laplante ◽  
Jean Desrochers ◽  
Jacques Préfontaine,
Keyword(s):  
Standard Deviation ◽  
Stock Returns ◽  
Covariance Matrix ◽  
Minimum Variance ◽  
Stock Index ◽  
Forecasting Model ◽  
Portfolio Risk ◽  
Volatility Forecasting ◽  
Forecasting Models ◽  
Minimum Variance Portfolio

This study pertains to forecasting portfolio risk using a GARCH (Generalized Autoregressive Conditional Heteroscedasticity) approach. Three models are compared to the GARCH model (1,1) i.e., random walk (RW), historical mean (HMM) and J.P. Morgans exponentially weighted moving average (EWMA). In recent years, many volatility forecasting models have been presented in the financial literature. Using the historical average of stock returns to determine the optimal portfolio is current practice in academic circles. However, we doubt the ability of this method to provide the best estimated portfolio variance. Moreover, an error in the estimated covariance matrix could result in a completely different portfolio mix. Consequently, we believe it would be relevant to examine the volatility forecasting model proposed in different studies to estimate the standard deviation of an efficient portfolio. With a view to building an efficient portfolio in an international context, we will analyze the forecasting models mentioned above. The purpose of this research is to determine whether a GARCH approach to forecasting the covariance matrix makes it possible to obtain a risk that most resembles the actual observed risk for a given return than the model traditionally used by practitioners and academic researchers. To this end, we selected six international stock indices. The study was conducted in a Canadian context and consequently, each stock index is converted into Canadian dollars. Initially, we estimate the covariance matrix for each forecasting model mentioned above. Then, we determine the proportions to invest in the portfolio and calculate the standard deviation of a minimum variance portfolio. Finally, the best model is selected based on the variances between estimated and actual risk by minimizing the root mean squared error (RMSE) for each forecasting model. Our results show that the GARCH (1,1) model is good for estimating risk in a minimum variance portfolio. As well, we find that it is statistically impossible to make a distinction between the accuracy of this model and the RW model. Lastly, our results show that based on the four statistical error measures used, the HMM is the least accurate for estimating portfolio risk. We therefore decided not to use this model and to rely instead on the GARCH approach or the RW, the simplest of all the models.

scholarly journals PERHITUNGAN VaR PORTOFOLIO SAHAM MENGGUNAKAN DATA HISTORIS DAN DATA SIMULASI MONTE CARLO

2012 ◽  
Author(s):  
WAYAN ARTHINI ◽  
KOMANG DHARMAWAN ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Standard Deviation ◽  
Stock Returns ◽  
Value At Risk ◽  
Time Horizon ◽  
Historical Data ◽  
Portfolio Risk ◽  
Simulation Data ◽  
Risk And Return

Value at Risk (VaR) is the maximum potential loss on a portfolio based on the probability at a certain time.  In this research, portfolio VaR values calculated from historical data and Monte Carlo simulation data. Historical data is processed so as to obtain stock returns, variance, correlation coefficient, and variance-covariance matrix, then the method of Markowitz sought proportion of each stock fund, and portfolio risk and return portfolio. The data was then simulated by Monte Carlo simulation, Exact Monte Carlo Simulation and Expected Monte Carlo Simulation. Exact Monte Carlo simulation have same returns and standard deviation  with historical data, while the Expected Monte Carlo Simulation satistic calculation similar to historical data. The results of this research is the portfolio VaR  with time horizon T=1, T=10, T=22 and the confidence level of 95 %, values obtained VaR between historical data and Monte Carlo simulation data with the method exact and expected. Value of VaR from both Monte Carlo simulation is greater than VaR historical data.

Lowering Portfolio Risk with Corporate Social Responsibility

2019 ◽  
Keyword(s):  
Corporate Social Responsibility ◽  
Standard Deviation ◽  
Social Responsibility ◽  
Lower Risk ◽  
Negative Relationship ◽  
Portfolio Risk ◽  
Portfolio Strategy ◽  
Predicted Probability ◽  
Corporate Social ◽  
Extreme Returns

This article examines the link between corporate social responsibility (CSR), as measured by the Kinder, Lydenberg, and Domini Research and Analytics Inc. (KLD) data, and the likelihood that a firm experiences an extreme return in a given year. An extreme-return firm is defined as one that has a return either in the top or bottom 3% of all firms with CSR data. The authors find CSR is negatively related to the likelihood of a firm experiencing an extreme return. Accounting for this negative relationship significantly improves a model used to predict future extreme returns. Finally, they form two portfolios: one with all firms with CSR data and one with all firms with CSR data except those firms with the highest predicted probability of extreme returns in the following year. Our results indicate that the returns for the two portfolios are nearly identical. However, the standard deviation of the portfolio excluding likely extreme movers is 3% lower than the portfolio with all firms. Thus, this simple portfolio strategy incorporating CSR has the potential to lower risk without impacting return.

scholarly journals PENGUKURAN RISIKO KREDIT DAN PENGUKURAN KINERJA DARI PORTOFOLIO OBLIGASI

2018 ◽  
Vol 7 (1) ◽  
pp. 43-53
Author(s):  
Bimbi Ardhana Rizky ◽  
Sudarno Sudarno ◽  
Diah Safitri
Keyword(s):  
Credit Risk ◽  
Loss Probability ◽  
Portfolio Performance ◽  
Portfolio Risk ◽  
Credit Risks ◽  
Bond Portfolio ◽  
Mean Variance ◽  
Return Value ◽  
Transition Rating

Except getting coupon as a profit, there is loss probability in bond investment that is credit risks investment. One way to measure the credit risk of a bond is to use the credit metrics method. It uses the ratings of the bond issuer company and the transition rating issued by the rating company for its calculations. Mean Variance Efficient Portfolio (MVEP) can be used to make an optimal portfolio so that risk can be obtained to a minimum. An assessment of portfolio performance is needed  to increase confidence to invest. Sharpe index can measure portfolio performance based on return value of bond. In this case, study has been conduct in two bonds which are Obligasi Berkelanjutan I Bank BTN Tahap II Tahun 2013 and Obligasi Berkelanjutan I PLN Tahap I Tahun 2013 Seri B. The optimum portfolio formed results 67,96% proportion for the first bond and 32,04% for the second bond. For the result, and there is Rp239,4235(billion) of portfolio risk formed. And there is 0,212496for Sharpe index performance assessment portfolio. Keywords: Bond, portfolio, credit risk, credit metrics, Mean Variance Efficient Portfolio, Sharpe index

scholarly journals A General Framework for Portfolio Theory. Part II: Drawdown Risk Measures

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 76 ◽  
Author(s):  
Stanislaus Maier-Paape ◽  
Qiji Zhu
Keyword(s):  
Standard Deviation ◽  
General Framework ◽  
Risk Measures ◽  
Risk Measure ◽  
Portfolio Theory ◽  
Portfolio Risk ◽  
Convex Risk Measures ◽  
Efficient Portfolios ◽  
The Absolute

The aim of this paper is to provide several examples of convex risk measures necessary for the application of the general framework for portfolio theory of Maier-Paape and Zhu (2018), presented in Part I of this series. As an alternative to classical portfolio risk measures such as the standard deviation, we, in particular, construct risk measures related to the “current” drawdown of the portfolio equity. In contrast to references Chekhlov, Uryasev, and Zabarankin (2003, 2005), Goldberg and Mahmoud (2017), and Zabarankin, Pavlikov, and Uryasev (2014), who used the absolute drawdown, our risk measure is based on the relative drawdown process. Combined with the results of Part I, Maier-Paape and Zhu (2018), this allows us to calculate efficient portfolios based on a drawdown risk measure constraint.

Index correlation: implications for asset allocation

2015 ◽  
Vol 41 (11) ◽  
pp. 1236-1256
Author(s):  
Allen Michel ◽  
Jacob Oded ◽  
Israel Shaked
Keyword(s):  
Standard Deviation ◽  
Asset Allocation ◽  
Market Performance ◽  
Portfolio Risk ◽  
Market Returns ◽  
Content Type ◽  
Shape Distribution ◽  
Asset Class ◽  
Asset Classes ◽  
Equity Indices

Purpose – The cornerstone of Modern Portfolio Theory with implications for many aspects of corporate finance is that reduced correlation among assets and reduced standard deviation are key elements in portfolio risk reduction. The purpose of this paper is to analyze the conditional correlation and standard deviation of a broad set of indices with the S & P 500 conditioned on market performance. Design/methodology/approach – The authors examined volatility and correlation for a set of indices for a 19-year period based on weekly data from July 2, 1993 to June 30, 2012. These included the NASDAQ, MSCI EAFE, Russell 1000, Russell 2000, Russell 3000, Russell 1000 Growth, Russell 1000 Value, Gold, MSCI EM and Dow Jones UBS Commodity. The data for the Wilshire US REIT, Barclays Multiverse, Multiverse 1-3, Multiverse 3-5 and Multiverse 10+ became available starting July 2, 2002. For these indices the authors used weekly data from July 1, 2002 through June 30, 2012. For the iBarclays TIPS, the authors used weekly data from the time of availability, namely, for the period December 12, 2003 through June 29, 2012. Findings – The findings demonstrate that both the conditional correlations and standard deviations vary as a function of market performance. Moreover, the authors obtain a U-shape distribution of correlations conditioned on market performance for equity indices, such as NASDAQ, as well as for the Wilshire REIT. Namely, correlations tend to be high when market returns are at low or high extremes. For more typical market performance, correlations tend to be low. A modified U-shape is found for bond indices and the Dow Jones UBS Commodity Index. Interestingly, the correlation between gold and the S & P 500 is unrelated to the return on the S & P. Originality/value – While it has been observed that asset classes move together, this paper is the first to systematically analyze the nature of these asset class correlations.

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