Financial Risk Modelling and Portfolio Optimization with R

Pfaff, Bernhard

doi:10.1002/9781118477144

Cited by 32 publications

(48 citation statements)

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“…In this section, we articulate how the data has been analysed using R Studio. We start by analysing some multi-asset data for the following major indexes across the world (Pfaff, 2016) The resulting returns are in the following increasing order; N225, FTSE, GDAXI, GSPC, RUA, GREXP, BGO5.L, DJCBTI, GLD, and EEM.…”

Section: Discussionmentioning

confidence: 99%

“…Risk-optimal portfolios can be differentiated from Markowitz's portfolios based on the fact that a certain VaR or ES (expected shortfall) level is not the result of an efficient portfolio allocation, but an objective (Pfaff, 2016). The market risk measure CVaR is the expected loss exceeding the VaR for a given confidence level (Pfaff, 2016).…”

Section: Var Modelmentioning

confidence: 99%

“…The market risk measure CVaR is the expected loss exceeding the VaR for a given confidence level (Pfaff, 2016). For CVaR model we can use the default settings as shown below (Würtz et al, 2015).…”

Section: Var Modelmentioning

confidence: 99%

“…The R packages used for data analysis include: Financial Risk Modelling and Portfolio Optimization with R (FRAPO) (Pfaff, 2016); Rmetrics-Portfolio Selection and Optimization (FPortfolio) (Pfaff, 2016); Econometric tools for performance and risk analysis (Performance Analytics) (Würtz et al, 2015).…”

Section: Journal Of Financial Risk Managementmentioning

confidence: 99%

See 3 more Smart Citations

Portfolio Optimization Modelling with R for Enhancing Decision Making and Prediction in Case of Uganda Securities Exchange

Baganzi¹,

Kim²,

Shin³

2017

JFRM

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Portfolio Optimization involves choosing proportions of assets to be held in a portfolio, so as to make the portfolio better than any other. In this research, we use a software for statistical computing R to analyse the performance of portfolio optimization models which include; Markowitz's Mean-Variance (MV) model, the VaR model, and Konno and Yamazaki's Mean-Absolute Deviation (MAD) model. We start by analysing multi-asset data for the major indexes in the world followed by historical data of 16 constituent shares listed on the Uganda Securities Exchange (USE) covering 6.5 years. The paper then tests the stock performance of the models using R. We found that GREXP bonds dominated the world market as they accounted for more than 60% of the Maximum Diversified Portfolio (MDP). For the USE, we generated more risk measures like volatility, Sharpe Ratio (SR), Risk Parity (RP), Expected Shortfall (ES) or CVaR which we used to assess stock performance. UMEME, NVL, BATU, JHL, DFCU, EBL, EABL, KCB, SBU and CENT were the bestperforming stocks. By understanding the performance of portfolio optimization models in R, Ugandan investors will develop a better view of the latest performance of the stocks listed on the USE. This will help them to decide on which stocks to include in their investment portfolios, thus prevent wrong investment decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Var Modelmentioning

confidence: 99%

Section: Var Modelmentioning

confidence: 99%

Section: Journal Of Financial Risk Managementmentioning

confidence: 99%

See 2 more Smart Citations

Portfolio Optimization Modelling with R for Enhancing Decision Making and Prediction in Case of Uganda Securities Exchange

Baganzi¹,

Kim²,

Shin³

2017

JFRM

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“…A closed-form solution can only be derived under the assumption that all asset pairs share the same correlation coefficient. Under this assumption, the optimal weights are determined by the ratio of the inverse volatility of the ith asset and the average of the inverse asset volatilities (see Pfaff, 2013).…”

Section: Equal Risk Contributed Portfoliomentioning

confidence: 99%

Portfolios in the Ibex 35 Index: Alternative Methods to the Traditional Framework, a Comparative with the Naive Diversification in a Pre- and Post- Crisis Context

2015

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In this paper, we present an analysis of the effectiveness of various portfolio optimization strategies Simón Sosvilla-RiveroComplutense University of Madrid sosvilla@ccee.ucm.es Abstract:In this paper, we present an analysis of the effectiveness of various portfolio optimization strategies applied to the stocks included in the Spanish Ibex 35 index, for a period of 14 years, from 2001 until 2014. The period under study includes episodes of volatility and instability in financial markets, incorporating the Global Financial Crisis and the European Sovereign Debt Crisis. This implies a challenge in portfolio optimization strategies since the methodologies are restricted to the assignment of positive weights. We have taken for asset allocation the daily returns with an estimation window equal to 1 year and we hold portfolio assets for another year. This paper attempts to influence the discussion over whether the naive diversification proves to be an effective strategy as opposed to portfolio optimization models. For that, we evaluate the out-of-sample performance of 15 strategies for asset allocation in the Ibex 35 index, before and after of the Global Financial Crisis. Our results suggest that a large number of strategies outperform to the 1/N rule and to the Ibex 35 index in terms of return, Sharpe ratio and lower VaR and CVaR. The mean-variance portfolio of Markowitz with shortsale constraints, it is the only strategy that renders a Sharpe ratio statistically different to Ibex 35 index in the

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2017

Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R

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Financial Risk Modelling and Portfolio Optimization with R

Cited by 32 publications

References 0 publications

Portfolio Optimization Modelling with R for Enhancing Decision Making and Prediction in Case of Uganda Securities Exchange

Portfolio Optimization Modelling with R for Enhancing Decision Making and Prediction in Case of Uganda Securities Exchange

Portfolios in the Ibex 35 Index: Alternative Methods to the Traditional Framework, a Comparative with the Naive Diversification in a Pre- and Post- Crisis Context

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