Robust portfolio optimization with second order stochastic dominance constraints

Sehgal, Ruchika; Mehra, Aparna

doi:10.1016/j.cie.2020.106396

Cited by 18 publications

(4 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the analysis of Table 7, it can be concluded that the R-Vine Copula model has the maximum likelihood value, indicating that it has better fitting performance and can better demonstrate the risk contagion relationship between financial assets [30]. Meanwhile, the likelihood value under the D-Vine structure (parallel structure) is closer to the likelihood value under the R-Vine structure compared to the C-Vine structure (star structure), indicating that the risk contagion relationship between financial assets is also closer to the parallel structure rather than the star structure [31]. Furthermore, based on the Vuong test results, it can be concluded that the R-Vine Copula model has better fitting ability.…”

Section: Research On Risk Contagion Measurement Based On R-vine Copul...mentioning

confidence: 99%

Measurement and Control of Risk Contagion in Portfolio Optimization Processes

Qiao,

Qiao

2024

Symmetry

View full text Add to dashboard Cite

The success of an investment portfolio is not only related to its yield, but is also influenced by market risks, especially the contagion risks that may exist between assets. Therefore, effective portfolio optimization requires first studying the risk contagion relationship between financial assets. We selected a total of six financial assets from different stock and exchange rate markets as the research objects, and explored the risk contagion relationships of various assets in the investment portfolio through Vine Copula. Research has shown that there is often a structural mutation in one or some assets of an investment portfolio, leading to significant fluctuations in investment returns. The Vine Copula model can effectively measure the risk contagion between assets caused by asset structural mutations.

show abstract

Section: Research On Risk Contagion Measurement Based On R-vine Copul...mentioning

confidence: 99%

Measurement and Control of Risk Contagion in Portfolio Optimization Processes

Qiao,

Qiao

2024

Symmetry

View full text Add to dashboard Cite

show abstract

“…The numerical results used data from the FTSE100 index stocks during 2018. Sehgal and Mehra (2020) proposed a model for robust portfolio optimization with second order stochastic dominance constraints, in which the input returns of each asset at every scenario is varied in a bounded and symmetric interval. The cutting plane algorithm is employed to deal with the optimization problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

View full text Add to dashboard Cite

Simulation, and now digital twins, excel in estimating parameters associated with complex and timedependent stochastic processes, e.g., large airport, highway, and warehouse operations. Within such contexts, we consider statistical inference, whereby one seeks to quantify the error in the obtained simulation estimate. Historically, statistical inference in simulation has been considered challenging because parameters needing estimation are often complicated, and simulation output often autocorrelated and non-normal. However, we argue that the remarkably simple idea of batching can be used as an "omnibus" inference device in simulation settings. Batching for inference works in three simple steps: (i) divide simulation output data into overlapping batches; (ii) construct parameter estimates from each batch; and (iii) use the batch estimates, after accounting for dependence, to perform statistical inference. As we describe in this paper, the resulting procedures are usually trivial to implement in software, and are provably correct and efficient. Batching ideas originated in the 1950s, and have enjoyed steady development in the simulation community since the 1970s mostly within the problem of variance estimation. Our thesis is that batching ideas have much wider utility, and that batching should be considered alongside other resampling ideas such as the bootstrap or the jackknife in modern statistics.

show abstract

“…The traditional mean-risk formulations are unable to extract much information from the return distribution, and thus some researchers recommended the incorporation of either more than one risk measures or moments ( Konno et al 1993;Konno and Suzuki 1995;Sharma and Mehra 2013;Sharma and Mehra 2015;Zhao et al 2015) or SD (Roman and Mitra 2009) in portfolio optimization framework. Apart from being theoretically sound, portfolio optimization models incorporating SSD criteria from the benchmark portfolio are computational efficient (Fabian et al 2011;Bruni et al 2012;Roman et al 2013;Goel and Sharma 2019;Sehgal and Mehra 2020). Dentcheva and Ruszczyński (2003; used the lower partial moment (LPM) characterization of SSD in the constraints from the benchmark portfolio.…”

Section: Second Order Stochastic Dominancementioning

confidence: 99%

“…Goel and Sharma (2019) introduced deviation measure in SSD and explored its application to enhanced indexing. Sehgal and Mehra (2020) proposed a robust portfolio model with SSD constraints. Zhai et al (2020) designed a multi-constraint SSD model and solved it using Whale optimization algorithm (WOA).…”

Section: Second Order Stochastic Dominancementioning

confidence: 99%

Sectoral portfolio optimization by judicious selection of financial ratios via PCA

Dhingra¹,

Sharma²,

Gupta³

2021

Preprint

View full text Add to dashboard Cite

Embedding value investment in portfolio optimization models has always been a challenge. In this paper, we attempt to incorporate it by first employing principal component analysis sector wise to filter out dominant financial ratios from each sector and thereafter, use the portfolio optimization model incorporating second order stochastic dominance criteria to derive the final optimal investment. We consider a total of 11 well known financial ratios corresponding to each sector representing four categories of ratios, namely Liquidity, Solvency, Profitability, and Valuation. PCA is then applied sector wise over a period of 10 years from April 2004 to March 2014 to extract dominant ratios from each sector in two ways, one from the component solution and other from each category on the basis of their communalities. The two step Sectoral Portfolio Optimization (SPO) model integrating the SSD criteria in constraints is then utilized to build an optimal portfolio. The strategy formed using the former extracted ratios is termed as PCA-SPO(A) and the latter one as PCA-SPO(B).The results obtained from the proposed strategies are compared with the SPO model and one step nominal SSD model, with and without financial ratios for computational study. Empirical performance of proposed strategies is assessed over the period of 6 years from April 2014 to March 2020 using a rolling window scheme with varying outof-sample time line of 3, 6, 9, 12 and 24 months for S&P BSE 500 market. We observe that the proposed strategy PCA-SPO(B) outperforms all other models in terms of downside deviation, CVaR, VaR, Sortino ratio, Rachev ratio, and STARR ratios over almost all out-of-sample periods. This highlights the importance of value investment where ratios are carefully selected and embedded quantitatively in portfolio selection process.

show abstract

Robust portfolio optimization with second order stochastic dominance constraints

Cited by 18 publications

References 50 publications

Measurement and Control of Risk Contagion in Portfolio Optimization Processes

Measurement and Control of Risk Contagion in Portfolio Optimization Processes

Proceedings of SW21 The OR Society Simulation Workshop

Sectoral portfolio optimization by judicious selection of financial ratios via PCA

Contact Info

Product

Resources

About