Mauro Bernardi, Daniele Bianchi, Nicolas Bianco
arXiv 14 Dec 2022 · Econometrics · 4 citations (OpenAlex)
arXiv:2212.07288 · PDF · DOI · OpenAlex · Extracted main text
We propose an alternative approach towards cost mitigation in volatility-managed portfolios based on smoothing the predictive density of an otherwise standard stochastic volatility model. Specifically, we develop a novel variational Bayes estimation method that flexibly encompasses different smoothness assumptions irrespective of the persistence of the underlying latent state. Using a large set of equity trading strategies, we show that smoothing volatility targeting helps to regularise the extreme leverage/turnover that results from commonly used realised variance estimates. This has important implications for both the risk-adjusted returns and the mean-variance efficiency of volatility-managed portfolios, once transaction costs are factored in. An extensive simulation study shows that our variational inference scheme compares favourably against existing state-of-the-art Bayesian estimation methods for stochastic volatility models.
appendix boundary found by appendix_command · 79% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | A. Moreira and T. Muir (2017) Volatility-managed portfolios | 1.000 | 16 | 3 | 100% |
| 2 | F. Corsi (2009) A simple approximate long-memory model of realized volatility | 1.000 | 10 | 3 | 100% |
| 3 | J. C. Chan and X. Yu (2022) Fast and accurate variational inference for large bayesian vars with stochastic volatility | 1.000 | 8 | 4 | 100% |
| 4 | D. Hosszejni and G. Kastner (2021) Modeling univariate and multivariate stochastic volatility in R with stochvol and factorstochvol | 1.000 | 5 | 4 | 100% |
| 5 | P. Barroso and A. Detzel (2021) Do limits to arbitrage explain the benefits of volatility-managed portfolios? | 0.874 | 13 | 2 | 100% |
| 6 | S. Cederburg, M. S. O’Doherty, F. Wang, and X. S. Yan (2020) On the performance of volatility-managed portfolios | 0.874 | 13 | 2 | 100% |
| 7 | F. Wang and X. S. Yan (2021) Downside risk and the performance of volatility-managed portfolios | 0.874 | 5 | 2 | 100% |
| 8 | N. Jegadeesh and S. Titman (1993) Returns to buying winners and selling losers: Implications for stock market efficiency | 0.843 | 3 | 3 | 100% |
| 9 | S. J. Taylor (1994) Modeling stochastic volatility: A review and comparative study | 0.843 | 3 | 3 | 100% |
| 10 | P. Barroso and P. Santa-Clara (2015) Momentum has its moments | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 59 scored citations.