Carlo Sala

Assistant Professor of Finance at ESADE Business School - Barcelona

Here is my: CV

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I'm an assistant professor of Finance at ESADE Business School in Barcelona

I obtained my Ph.D. from the Swiss Finance Institute at the University of Lugano and supervised by Professor Giovanni Barone-Adesi.


Main research interests: theoretical and empirical asset pricing.

Specifically, my interests focus on:

Most of my papers analyze different theoretical and empirical aspects related to the use of the market information in asset pricing.


Option-implied State Prices and the Stochastic Discount Factor

with Giovanni Barone-Adesi and Mira Antonietta

Estimating the market’s subjective distribution of future returns by means of backward-looking historical data leads to an uninformative and, at best, partially-conditional measure. What is missing are the investors’ forward-looking beliefs. This long-lasting problem affects a huge amount of literature and leads to puzzles and suboptimal results. This paper proposes a new market-based, flexible and highly informative non-parametric method to estimate a conditional and time-varying physical measure. Starting from the classical approach, that relies on historical data only, the proposed measure is completed through the informational content of the implied moments of the option prices. The revised density, a mixture of different sources of information, combines the options forward-looking knowledge with the historical background provided by stock returns thus encoding past, present and future information. As a natural test, the new measure is used to investigate extensively the pricing kernel monotonicity.

WTI Crude oil option implied VaR and CVaR: an empirical application

with Giovanni Barone-Adesi and Legnazzi Chiara

In a recent theoretical paper Barone Adesi(2015) shows how to extract the option implied VaR and CVaR. This is the first empirical application of that paper. We extract the 2014-2015 daily option implied VaR and CVaR from the WTI crude oil future prices and the options written on it. Without relying on any distributional assumption we are able to backtest the CVaR values, thus proposing a coherent and elicitable risk measure. From a forecasting viewpoint a ratio of the two risk measures allows us to predict the probability density of jumps in the underlying price, which would have been unpredictable with standard inference methods.

Sentiment Lost: The Effect of Projecting the Empirical Pricing Kernel Onto a Smaller Filtration Set

with Giovanni Barone-Adesi

Supported by empirical examples this paper provides a theoretical analysis on the impacts of using a suboptimal information set for the estimation of the empirical pricing kernel and, more in general, for the validity of the fundamental theorems of asset pricing. While inferring the risk-neutral measure from options data provides a naturally forward-looking estimate, extracting the real world one from a stream of historical returns is only partially informative, thus suboptimal with respect to investors' future beliefs. As a consequence of this disalignment, the two measures no longer share the same nullsets thus distorting the investor's risk premium. It follows that the relative empirical pricing kernel is no longer a true martingale, as required by the theory, but a strict local martingale with consequences on the validity of the risk-neutral pricing. From a probabilistic viewpoints, the missing beliefs are totally unaccessible stopping times on the coarser filtration set, so that an absolutely continuous strict local martingale, once projected on it, becomes continuous with jumps.

Conditioning the Information in Portfolio Optimization

with Giovanni Barone-Adesi
Journal of Mathematical Finance, 2016, Vol. 6, pp. 598-625

This paper proposes a theoretical analysis on the impacts of using a suboptimal information set on the three main components used in asset pricing, namely the risk physical and neutral measures and the relative pricing kernel. The analysis is carried out by means of a portfolio optimization problem for a small and rational investor. Solving for the maximal expected utility of terminal wealth, we prove the existence of an information premium between what is required by the theory, a complete information set thus a fully conditional measure, and what is instead achievable by en econometrician. Searching for the best bounds, we then study the impact of the premium on the pricing kernel. Finally, exploiting the strong interconnection between the pricing kernel and its densities, the extension to the risk-neutral measure follows naturally.

S&P 500 Index, an Option Implied Risk Analysis

with Giovanni Barone-Adesi and Chiara Legnazzi

The forward-looking nature of option market data allows one to derive economically-based and model-free conditional risk measures. The option-implied methodology is a tool for regulators and companies to perform external or internal risk analysis without posing assumptions on the distribution of returns. The article proposes the first comprehensive and extensive analysis of the performances of these measures compared with classical risk measures for the S&P500. Delivering good results both at short and long time horizons, the option-implied estimates emerge as a convenient alternative to the existing risk measures.

Greed and Fear: The Nature of Sentiment

with Giovanni Barone-Adesi and Matteo Pisati

Empirical indicators of sentiment are commonly employed in the economic literature while a precise understanding of what is sentiment is still missing. Exploring the links among the most popular proxies of sentiment, fear and uncertainty this paper aims to fill this gap. We show how fear and sentiment are specular in their predictive power in relation to the aggregate market and to cross-sectional returns. Finally, we document how sentiment and fear time cross-sectional returns: conditionally on a today's high (low) level of fear we observe a next month high (low) return per unit of risk. The opposite holds for sentiment.

The Pricing Kernel Density: The Case of the Information that Did Not Bark

with Giovanni Barone-Adesi

Estimating daily conditional events using partially-conditional real-world probabilities leads to misleading results. Recently Sala (2015) proposed a new estimation technique to overcome this issue and obtain a fully conditional real-world measure from different sources of information. This paper is an extended version of Sala (2015)[47] and contributes to his findings in two ways. First a valid pricing kernel can only be obtained through well behaved measures. We put a focus on the statistical properties of the estimated measures and its consequences on a daily and yearly basis. Secondly, since the proposed model makes use of an intensive simulation technique, we show how possible numerical errors may lead to puzzling results.



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