Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1-N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1-N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many "miles to go" before the gains promised by optimal portfolio choice can actually be realized out of sample. The Author 2007. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

/pdf/optimal-versus-naive-diversification-how-inefficient-is-the-42sjq28l5w.pdf

Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?

http://perso.ens-lyon.fr/patrick.flandrin/LedoitWolf_JMA2004.pdf

A well-conditioned estimator for large-dimensional covariance matrices

http://finance.martinsewell.com/stylized-facts/volatility/Andersen-etal2001a.pdf

The distribution of realized stock return volatility

The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.

https://www.pnas.org/content/pnas/103/10/3863.full.pdf

Information-based functional brain mapping

Improved Estimation of the Covariance Matrix of Stock Returns With an Application to Portfolio Selection

The central message of this article is that no one should use the sample covariance matrix for portfolio optimization. It is subject to estimation error of the kind most likely to perturb a mean-variance optimizer. Instead, a matrix can be obtained from the sample covariance matrix through a transformation called shrinkage. This tends to pull the most extreme coefficients toward more central values, systematically reducing estimation error when it matters most. Statistically, the challenge is to know the optimal shrinkage intensity. Shrinkage reduces portfolio tracking error relative to a benchmark index, and substantially raises the manager9s realized information ratio.

/pdf/honey-i-shrunk-the-sample-covariance-matrix-3o838a4s7t.pdf

Honey, I shrunk the sample covariance matrix

Robust Performance Hypothesis Testing with the Sharpe Ratio

We develop an approach to asset pricing in incomplete markets that bridges the gap between the two fundamental approaches in finance: model‐based pricing and pricing by no arbitrage. We strengthen the absence of arbtrage assumption by precluding investment opportunities whose attractiveness to a benchmark investor exceeds a specified threshold. In our framework, the attractiveness of an investment opportunity is measured by the gain‐loss ratio. We show that a restriction on the maximum gain‐loss ratio is equivalent to a restriction on the ratio of the maximum to minimum values of the pricing kernel. By limiting the maximum gainloss ratio, we can restrict the admissible set of pricing kernels, which in turn allows us to restrict the set of prices that can be assigned to assets. We illustrate our methodology by computing price bounds for call options in a Black‐Scholes economy without intermediate trading. When we vary the maximum permitted gainloss ratio, these bounds can range from the exact prices implie...

Olivier Ledoit

Papers

A well-conditioned estimator for large-dimensional covariance matrices

Improved Estimation of the Covariance Matrix of Stock Returns With an Application to Portfolio Selection

Honey, I shrunk the sample covariance matrix

Robust Performance Hypothesis Testing with the Sharpe Ratio

Gain, Loss, and Asset Pricing