| 1. |
- Kiss, Tamás, 1988-, et al.
(författare)
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Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we analyze how skewness and heavy tails affect the estimated relationship between the real economy and the corporate bond-yield spread, a popular predictor of rea lactivity. We use quarterly US data to estimate Bayesian VAR models with stochastic volatility and various distributional assumptions regarding the disturbances. In-sample, we find that – after controlling for stochastic volatility – innovations in GDP growth can be well-described by a Gaussian distribution. In contrast, both the unemployment rate and the yield spread appear to benefit from being modelled using non-Gaussian innovations. When it comes to real-time forecasting performance, we find that the yield spread is an important predictor of GDP growth, and that accounting for stochastic volatility matters, mainly for density forecasts. Incremental improvements from non-Gaussian innovations are limited to forecasts of the unemployment rate. Our results suggest that stochastic volatility is of first order importance when modelling the relationship between yield spread and real variables; allowing for non-Gaussian innovations is less important.
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| 2. |
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| 3. |
- Kiss, Tamás, 1988-, et al.
(författare)
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Predicting returns and dividend growth - The role of non-Gaussian innovations
- 2022
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Ingår i: Finance Research Letters. - : Elsevier. - 1544-6123 .- 1544-6131. ; 46:Part A
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we assess whether flexible modelling of innovations impact the predictive performance of the dividend price ratio for returns and dividend growth. Using Bayesian vector autoregressions we allow for stochastic volatility, heavy tails and skewness in the innovations. Our results suggest that point forecasts are barely affected by these features, suggesting that workhorse models on predictability are sufficient. For density forecasts, however, we find that stochastic volatility substantially improves the forecasting performance.
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| 4. |
- Alfelt, Gustav, 1985-, et al.
(författare)
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On the mean and variance of the estimated tangency portfolio weights for small samples
- 2022
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Ingår i: Modern Stochastics: Theory and Applications. - Vilnius : VTeX. - 2351-6054 .- 2351-6046. ; 9:4, s. 453-482
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a sample estimator of the tangency portfolio (TP) weights is con-sidered. The focus is on the situation where the number of observations is smaller than the number of assets in the portfolio and the returns are i.i.d. normally distributed. Under these as-sumptions, the sample covariance matrix follows a singular Wishart distribution and, therefore, the regular inverse cannot be taken. In the paper, bounds and approximations for the first two moments of the estimated TP weights are derived, as well as exact results are obtained when the population covariance matrix is equal to the identity matrix, employing the Moore-Penrose inverse. Moreover, exact moments based on the reflexive generalized inverse are provided. The properties of the bounds are investigated in a simulation study, where they are compared to the sample moments. The difference between the moments based on the reflexive generalized inverse and the sample moments based on the Moore-Penrose inverse is also studied.
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| 5. |
- Bodnar, Taras, et al.
(författare)
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Discriminant analysis in small and large dimensions
- 2020
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Ingår i: Theory of Probability and Mathematical Statistics. - Providence, Rhode Island : American Mathematical Society (AMS). - 1547-7363 .- 0094-9000. ; 100, s. 21-41
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Tidskriftsartikel (refereegranskat)abstract
- We study the distributional properties of the linear discriminant function under the assumption of normality by comparing two groups with the same covariance matrix but different mean vectors. A stochastic representation for the discriminant function coefficients is derived, which is then used to obtain their asymptotic distribution under the high-dimensional asymptotic regime. We investigate the performance of the classification analysis based on the discriminant function in both small and large dimensions. A stochastic representation is established, which allows to compute the error rate in an efficient way. We further compare the calculated error rate with the optimal one obtained under the assumption that the covariance matrix and the two mean vectors are known. Finally, we present an analytical expression of the error rate calculated in the high-dimensional asymptotic regime. The finite-sample properties of the derived theoretical results are assessed via an extensive Monte Carlo study.
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| 6. |
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| 7. |
- Drin, Svitlana, 1977-, et al.
(författare)
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A test on the location of tangency portfolio for small sample size and singular covariance matrix
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we propose the test for the location of the tangency portfolio on the set of feasible portfolios when both the population and the sample covariance matrices of asset returns are singular. We derive the exact distribution of the test statistic under both the null and alternative hypotheses. Furthermore, we establish the high-dimensional asymptotic distribution of that test statistic when both the portfolio dimension and the sample size increase to infinity. We complement our theoretical findings by comparing the high-dimensional asymptotic test with an exact finite sample test in the numerical study. A good performance of the obtained results is documented.
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| 8. |
- Gulliksson, Mårten, 1963-, et al.
(författare)
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An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection
- 2020
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Ingår i: Computational Economics. - : Springer. - 0927-7099 .- 1572-9974. ; 56, s. 773-794
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Tidskriftsartikel (refereegranskat)abstract
- Covariance matrix of the asset returns plays an important role in the portfolioselection. A number of papers is focused on the case when the covariance matrixis positive definite. In this paper, we consider portfolio selection with a singu-lar covariance matrix. We describe an iterative method based on a second orderdamped dynamical systems that solves the linear rank-deficient problem approxi-mately. Since the solution is not unique, we suggest one numerical solution that canbe chosen from the iterates that balances the size of portfolio and the risk. The nu-merical study confirms that the method has good convergence properties and givesa solution as good as or better than the constrained least norm Moore-Penrose solu-tion. Finally, we complement our result with an empirical study where we analyzea portfolio with actual returns listed in S&P 500 index.
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| 9. |
- Gulliksson, Mårten, 1963-, et al.
(författare)
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Portfolio Selection with a Rank-deficient Covariance Matrix
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we consider optimal portfolio selection when the covariance matrix of the asset returns is rank-deficient. For this case, the original Markowitz’ problem does not have a unique solution. The possible solutions belong to either two subspaces namely the range- or nullspace of the covariance matrix. The former case has been treated elsewhere but not the latter. We derive an analytical unique solution, assuming the solution is in the null space, that is risk-free and has minimum norm. Furthermore, we analyse the iterative method which is called the discrete functional particle method in the rank-deficient case. It is shown that the method is convergent giving a risk-free solution and we derive the initial condition that gives the smallest possible weights in the norm. Finally, simulation results on artificial problems as well as real-world applications verify that the method is both efficient and stable.
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| 10. |
- Gulliksson, Mårten, 1963-, et al.
(författare)
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Portfolio Selection with a Rank-Deficient Covariance Matrix
- 2024
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Ingår i: Computational Economics. - : Springer. - 0927-7099 .- 1572-9974. ; 63, s. 2247-2269
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we consider optimal portfolio selection when the covariance matrix of the asset returns is rank-deficient. For this case, the original Markowitz' problem does not have a unique solution. The possible solutions belong to either two subspaces namely the range- or nullspace of the covariance matrix. The former case has been treated elsewhere but not the latter. We derive an analytical unique solution, assuming the solution is in the null space, that is risk-free and has minimum norm. Furthermore, we analyse the iterative method which is called the discrete functional particle method in the rank-deficient case. It is shown that the method is convergent giving a risk-free solution and we derive the initial condition that gives the smallest possible weights in the norm. Finally, simulation results on artificial problems as well as real-world applications verify that the method is both efficient and stable.
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