Portfolio optimization and marginal contribution to risk on multivariate normal tempered stable model

Kim, Young S.

doi:10.1007/s10479-022-04613-7

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…In this case, Ξ is referred to as the N -dimensional standard NTS random variable with parameters (α, θ, β, Σ) and we denote it by Ξ ∼ stdNTS N (α, θ, β, Σ) (See more details in Kim and Volkmann (2013) and Kim (2022). ).…”

Section: Wherementioning

confidence: 99%

“…We suppose that the return R follows Proposition 3.1 (Kim (2022)). Consider a portfolio with N assets for a positive integer N .…”

Section: Nts Market Modelmentioning

confidence: 99%

“…The parameter estimation method in this section is similar as the method in Kim (2022) fit method is used between the cdf of the stdNTS distribution obtained by (3) and the empirical cdf obtained by the kernel density estimation. The same as Kim (2022), we use the index-based method with DJIA index data in order to find α and θ and then estimate the β vector and Σ matrix. That is, we find the 30-dimensional stdNTS parameters as the following two-step method:…”

Section: Empirical Illustrationmentioning

confidence: 99%

“…The NTS distribution has been popularly applied in finance by capturing the fat-tails of the asset return distribution and describing asymmetric dependence (See Kim and Volkmann (2013)). For example, it is applied to financial risk management in Anand et al (2017) and Kurosaki and Kim (2018), and portfolio management in Eberlein and Madan (2010), Anand et al (2016), and Kim (2022). In addition, Kim et al (2015) applies a Lévy process generated by the two-dimensional NTS distribution to Quanto option pricing, and its extension to capture stochastic dependence is further discussed in Kim et al (2023).…”

Section: Introductionmentioning

confidence: 99%

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Portfolio Optimization with Relative Tail Risk

Kim¹

2023

Preprint

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This paper proposes analytic forms of portfolio CoVaR and CoCVaR on the normal tempered stable market model. Since CoCVaR captures the relative risk of the portfolio with respect to a benchmark return, we apply it to the relative portfolio optimization. Moreover, we derive analytic forms for the marginal contribution to CoVaR and the marginal contribution to CoCVaR. We discuss the Monte-Carlo simulation method to calculate CoCVaR and the marginal contributions of CoVaR and CoCVaR. As the empirical illustration, we show relative portfolio optimization with thirty stocks under the distress condition of the Dow Jones Industrial Average. Finally, we perform the risk budgeting method to reduce the CoVaR and CoCVaR of the portfolio based on the marginal contributions to CoVaR and CoCVaR.

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Section: Wherementioning

confidence: 99%

“…We suppose that the return R follows Proposition 3.1 (Kim (2022)). Consider a portfolio with N assets for a positive integer N .…”

Section: Nts Market Modelmentioning

confidence: 99%

Section: Empirical Illustrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Portfolio Optimization with Relative Tail Risk

Kim¹

2023

Preprint

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“…To solve the MOSNP of Equation (16) at each time

t

, a key step is forecasting the joint distribution

f_{{bold-italicr}_{t}}

conditional on

ℬ false(\dots, {bold-italicr}_{- 1}, {bold-italicr}_{0}, \dots, {bold-italicr}_{t - h - 1} false)

(Kim, 2022). Numerical methods are used here to handle a large number of integral calculations from Equations (7) to (13).…”

Section: The Multiobjective Portfolio Optimizationmentioning

confidence: 99%

Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

Dai

Zhang

Lau

et al. 2023

Journal of Forecasting

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This study designs a methodological framework to forecasting allocation weights and evaluating multiobjective portfolios considering the investment horizon heterogeneity. The investment horizon heterogeneity may induce the terrible data loss problem under traditional asset return definition, while in the real world, there are many portfolio makers whose investment horizons are not consistent. The methodological framework has three parts. First, this paper gives a novel multitimescale analysis (MTA) tool as a computation procedure to decompose the raw return series, and the decomposed subreturn series could represent the information for a portfolio maker with specific investment horizon and has the same data length as the raw return series. Second, proposed methodological framework uses a time‐varying parameter GAS‐D‐Vine‐Copula model to construct the joint distribution of subreturn series of multiassets in a portfolio. Third, due to the stochastic dominance consistency issue, this paper applies three different utility functions as the outputs of a portfolio strategy and two cost functions as the inputs of a portfolio strategy in an efficiency evaluation model. The empirical example of US aviation stock market data from 2013 to 2021 reveals that the Mean‐Skewness‐Volatility‐HMCR‐LPM multiobjective has the greatest numbers of optimal strategy timings for portfolio makers with 2‐, 3–5‐, and 10–50‐day‐length investment horizons. The investment horizon of 1‐day length is the least efficient, and the investment horizon between 10‐ and 50‐day length is the most efficient. The proposed methodology indicates that more multiobjectives in a portfolio strategy are not necessarily better, and there is an optimal range of investment horizons for portfolio makers.

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Portfolio Optimization in Different Industries in the Hong Kong Financial Market

Zhang

2023

Applied Economics and Policy Studies

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Portfolio optimization and marginal contribution to risk on multivariate normal tempered stable model

Cited by 9 publications

References 44 publications

Portfolio Optimization with Relative Tail Risk

Portfolio Optimization with Relative Tail Risk

Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

Portfolio Optimization in Different Industries in the Hong Kong Financial Market

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