Search

The contact lens (CL) industry has made great strides in improving CL-wearing experiences. However, a large amount of CL wearers continue to experience ocular dryness, known as contact lens-induced dry eye (CLIDE), stemming from the reduction in tear volume, tear film instability, increased tear osmolarity followed by inflammation and resulting in ocular discomfort and visual disturbances. In this article, to address tear film thinning between the CL and the ocular surface, the concept of using a CL with microchannels to deliver the tears from the pre-lens tear film (PrLTF) to the post-lens ocular surface using in vitro eye-blink motion is investigated. This study reports an eye-blink mimicking system with microfluidic poly(2-hydroxyethyl methacrylate) (poly(HEMA)) hydrogel with integrated microchannels to demonstrate eye-blink assisted flow through microchannels. This in vitro experimental study provides a proof-of-concept result that tear transport from PrLTF to post-lens tear film can be enhanced by an artificial eyelid motion in a pressure range of 0.1-5 kPa (similar to human eyelid pressure) through poly(HEMA) microchannels. Simulation is conducted to support the hypothesis. This work demonstrates the feasibility of developing microfluidic CLs with the potential to help prevent or minimize CLIDE and discomfort by the enhanced transport of pre-lens tears to the post-lens ocular surface.

A large literature finds evidence that pricing kernels estimated nonparametrically from option prices and historical returns are not monotonically decreasing in market index returns. We propose a new nonparametric estimator of the pricing kernel that reflects the information available to investors who set option prices. In simulations, the estimator outperforms current techniques. Our empirical estimates using S&P 500 index option data from 1996-2012 and FTSE 100 index option data from 2002-2013 suggest that the "pricing kernel puzzle'' is a byproduct of econometric technique rather than a behavioral or economic phenomenon.

We show that a common component governs volatility dynamics across a wide range of traded equity factors. This `common factor volatility' (CFV) exists even among orthogonal factors. CFV occurs in both cash-flow and discount-rate components of factor returns and derives from market responses to fundamental news rather than underlying commonality in news volatility. Incorporating CFV improves factor volatility forecasts relative to models that include only own-factor volatility. The pervasiveness of CFV helps characterize the dynamics of stochastic discount factor (SDF) volatility. CFV results in conditional SDFs that exhibit procyclical volatility, highlighting an inconsistency between traded factors and prominent macro models.

Since the early 2000s liquidity in option markets has become less resilient, and our evidence suggests that it is so because of an increased vulnerability to liquidity shocks in the underlying. To demonstrate the causal impact, we consider an incident in which a large broker dealer erroneously executed millions of orders in the stock market. The computer glitch increased uninformed order flow resulting in liquidity-related uncertainty in the equity market. Option spreads of impacted stocks widened by as much as one-third while abnormal stock order flow was ongoing and remained wide until uncertainty about the glitch was resolved. The evidence is consistent with a mechanism whereby option market makers face risk of being picked off with every revised quote in the underlying. Option market participants bear higher adverse selection costs than equity market participants, making the derivative markets fragile.

We examine whether investors efficiently incorporate the effect of financial sector shocks into the equity prices of non-financial firms. Shocks to the financial sector are complex macroeconomic events affecting many firms to varying degrees. Prices may adjust slowly in response to financial sector shocks if information processing capabilities are constrained. We test this hypothesis by examining the ability of systemic risk shocks to predict future returns of financially constrained firms. We find that in the month following the month of large systemic events, large debt constrained firms exhibit significantly lower returns than unconstrained firms with an average relative risk-adjusted return of -3.5% (-42% annualized). More generally, innovations in systemic risk exhibit significant predictability for returns of constrained firms in excess of unconstrained firms. To identify financially constrained firms we introduce a new classification method using random decision forests, a machine learning technique. This method allows us to extend the measures based on textual analysis of firm filings both in the cross-section and time series using only financial information. Overall, the results provide evidence that the market is slow to incorporate the effects of financial sector shocks into equity prices.

We propose a new, minimally parameterized way of modeling stock-level covariances as a function of firm characteristics. Our model uses a large number of indicator functions to approximate the surface mapping two firms' characteristics to the correlation of their returns. We show that the method performs better than existing methods both in and out of our sample period. In truly out of sample tests, we show that using the information in firm characteristics, we can predict future covariances better than when using common asset pricing models. Correspondingly, we discuss how one can use our model as an avenue for understanding how characteristics relate to stock returns in the context of linear asset pricing models.

This paper studies the asset pricing implications of financial contagion risk in the cross section of expected stock returns. Contagion among systemically important financial firms played a major role in the financial crisis of 2008 and 2009. The purpose of this paper is to determine whether investors require a premium for holding assets that expose them to losses during events similar to those that unfolded during the recent financial crisis. We propose three measures of contagion in the financial sector. We show that factors based upon comovement and concentration of equity returns among systemically important financial firms are very robustly priced in the cross section of stock returns. Our results hold even when we remove the recent financial crisis from our sample. We further establish that the risks associated with these contagion factors are distinct from other measures of risk typically associated with market downturns.

We extensively study the term structure of volatility in individual equity options. We begin by studying the behavior of implied volatility in the cross section. We examine the joint dynamics of short maturity and long maturity implied volatility in order to gain a thorough understanding of how volatility term structure evolves. We uncover a number of stylized facts which, to the best of our knowledge, we are the first to empirically document. We then propose a simple framework of term structure dynamics that captures the features documented in our empirical study. This simple framework is illustrative and gives an intuitive way to understand the dynamics of the volatility term structure seen in the cross section. Using the intuition gleaned from our analysis, we examine strategies for trading volatility. Consistent with the term structure dynamics, we uncover a number of profitable volatility trading strategies across maturities. We further examine the extent to which profitability of these trading strategies is due to an interaction between volatility term structure and realized volatility.

In (10) a "direct" stochastic transfer principle was introduced, which represented multiple integrals with respect to fractional Brownian mo- tion in terms of multiple integrals with respect to standard Brownian motion. The method employed in (10) involved an operator i (n) H , mapping a class of functions L 2 to L 2 . However, the operator does not map L 2 onto L 2 . Hence i (n) H is not invertible. The non-invertibility arises from the fact that i (n) H is defined in terms of n-fold Riemann-Liouville fractional integrals and it is well known that functions f 2 L p (a,b) cannot always be represented as fractional integrals of functions ' 2 L p (a,b). This paper establishes the inverse sto- chastic transfer principle. Our purpose is to explicitly define an operator, which, acting on a certain class of functions, gives the transfer principle in- verting that in (10). As a result, multiple stochastic integrals with respect to standard Brownian motion are represented in terms of multiple stochastic integrals with respect to persistent fractional Brownian motion. In order to do this, we first prove a characterization of a class of functions for which there exists ', defined on a compact set in R n , such that f is equal to the n-fold Riemann-Liouville fractional integral of '. We establish a method for computing ' and conclude the paper with an example of using the inverse transfer principle in filtering.

The problem of nonlinear filtering of multiparameter random fields, observed in the presence of a long-range dependent spatial noise, is considered. When the observation noise is modelled by a persistent fractional Wiener sheet, several pathwise representations of the optimal filter are derived. The representations involve series of multiple stochastic integrals of different types and are particularly important since the evolution equations, satisfied by the best mean-square estimate of the signal random field, have a complicated analytical structure and fail to be proper (measure-valued) stochastic partial differential equations. Several of the above optimal filter representations involve a new family of strong martingale transforms associated to the multiparameter fractional Brownian sheet; the latter martingale family is of independent interest in fractional stochastic calculus of multiparameter random fields.