Taras Bodnar
Biographic Data
| ID | 8921023 |
|---|---|
| NAME | Taras Bodnar |
| GIVEN NAMES | Taras |
| FAMILY NAME | Bodnar |
| SIGNATURE | BODNAR T |
| AFFILIATIONS | Stockholm University |
| ORCID | 0000-0001-7855-8221 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Incorporating Different Sources of Information for Bayesian Optimal Portfolio Selection
This article introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolio weights. This approach not only enables direct inference about the weights but also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and a market condition metric (MCM), exemplified by the CBOE Volatility Inde…
Optimal Shrinkage-Based Portfolio Selection in High Dimensions
In this article, we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices. We construct a linear shrinkage estimator which is distribution-free and is optimal in the sense of maximizing with probability 1 the asymptotic out-of-sample expected utility, that is, mean-variance objective function for different values of risk aversion coefficient which in particular leads to the m…
Singular Conditional Autoregressive Wishart Model for Realized Covariance Matrices
Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in nonsingular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices m…
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Optimal Shrinkage-Based Portfolio Selection in High Dimensions
In this article, we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices. We construct a linear shrinkage estimator which is distribution-free and is optimal in the sense of maximizing with probability 1 the asymptotic out-of-sample expected utility, that is, mean-variance objective function for different values of risk aversion coefficient which in particular leads to the m…
Singular Conditional Autoregressive Wishart Model for Realized Covariance Matrices
Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in nonsingular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices m…
Incorporating Different Sources of Information for Bayesian Optimal Portfolio Selection
This article introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolio weights. This approach not only enables direct inference about the weights but also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and a market condition metric (MCM), exemplified by the CBOE Volatility Inde…
Econometrics (3 works) · Mathematics (3 works) · Computer Science (2 works) · Economics (2 works) · Financial Markets and Investment Strategies (2 works) · Financial Risk and Volatility Modeling (2 works) · Portfolio (2 works) · Statistics (2 works) · Algorithm (1 work) · Applied Mathematics (1 work)