1,721,063 research outputs found
The optimal corridor for implied volatility: from calm to turmoil periods
No full textCorridor implied volatility is obtained from model-free implied volatility by truncating the integration domain between two barriers. Empirical evidence on volatility forecasting, in various markets, points to the utility of trimming the risk-neutral distribution of the underlying stock price, in order to obtain unbiased measures of future realised volatility (see e.g. [9], [3]). The aim of the paper is to investigate, both in a statistical and in an economic setting, the optimal corridor of strike prices to use for volatility forecasting in the Italian market, by analysing a data set which covers the years 2005-2010 and span both a relatively tranquil and a turmoil period
The relation between implied and realised volatility: are call options more informative than put options? Evidence from the DAX index options market
No full textThe aim of this paper is to investigate the relation between implied volatility, historical volatility and realised volatility in the Dax index options market. Since implied volatility varies across option type (call versus put) we run a horse race of different implied volatility estimates: implied call, implied put and average implied that is a weighted average of call and put implied volatility with weights proportional to traded volume. Two hypotheses are tested in the Dax index options market: unbiasedness and efficiency of the different volatility forecasts. Our results suggest that all the three implied volatility forecasts are unbiased (after a constant adjustment) and efficient forecasts of future realised volatility in that they subsume all the information contained in historical volatility
Option based forecasts of volatility: An empirical study in the DAX index options market
Full text linkOption based volatility forecasts can be divided into “model dependent” forecast, such as implied volatility, that is obtained by inverting the Black and Scholes formula, and “model free” forecasts, such as model free volatility, proposed by Britten-Jones and Neuberger (2000), that do not rely on a particular option pricing model. The aim of this paper is to investigate the unbiasedness and efficiency in predicting future realized volatility of the two option based volatility forecasts: implied volatility and model free volatility. The comparison is pursued by using intradaily data on the Dax-index options market. Our results suggest that Black-Scholes volatility subsumes all the information contained in historical volatility and is a better predictor than model free volatility
News Sentiment indicators and the Cross‐Section of Stock Returns in the European Stock Market
Full text linkThis paper investigates whether the Bloomberg investor sentiment index can provide valuable information for investors and fund managers for the purposes of stock picking and portfolio selection. The dataset consists of all the listed companies in the Euro area for the period from 2010 to 2021. By exploiting portfolio sorting strategies, the paper evaluates to what extent and how long investor sentiment can affect stock returns. Moreover, it considers whether additional factors can affect the relationship between sentiment and returns, casting light on the asymmetric effect related to positive and negative news.
The findings are as follows. First, high (low) sentiment stocks exhibit high (low) returns on average. The average return of the portfolio that takes a long position in the stocks with very high sentiment and a short position in stocks with very low sentiment is statistically and economically significant and is robust to the inclusion of commonly used risk factors. Second, the predictability of stock returns using the sentiment indicator declines fast after one month. Third, evidence is found of the profitability of a long-short strategy that invests in stocks with low capitalization: profitability declines with the duration of the investment period. Finally, it is found that positive news is factored into the stock price more slowly than negative news, especially for stocks with low market capitalization
ESG criteria for the evaluation of European countries: a multicriteria analysis (MCDA)
Full text linkIn recent decades, climate change and sustainability have emerged as critical issues within global economic and political contexts. This paper investigates the application of ESG (Environmental, Social, and Governance) criteria for
evaluating European countries using Multi-Criteria Decision Analysis (MCDA) methods. Following an overview of the evolution of climate policies and key sustainability frameworks, four MCDA methods (TOPSIS, VIKOR, PROMETHEE
and SIR) are analyzed in detail. Each method is explored from both a theoretical perspective and through practical implementations in Python. Additionally, various normalisation techniques are applied to the selected methods to examine their influence on the results and to study the differences arising from their application (in detail: Vector Normalisation, Linear Normalisation, Row/Column Maximum Normalisation). A case study is presented to evaluate European countries based on ESG data, comparing the effectiveness and differences among the selected methods.
The results highlight the potential of MCDA approaches, combined with normalisation techniques to support complex decision-making processes, providing an integrated and systematic perspective to address sustainability challenge
The green premium in the European stock market
Full text linkThis paper investigates the concept of greenium, the premium investors are willing to pay for sustainable assets relative to conventional investments, with a focus on the European stock market. As sustainable finance gains prominence, understanding greenium's implications is crucial for both investors and policymakers. The study addresses the challenges of defining and estimating greenium, including varying perceptions of climate risk and the lack of standardized criteria for green assets. Through comprehensive empirical analysis, we evaluate the presence of greenium and its effects on investment strategies, highlighting how sustainability considerations shape asset valuation and investor behavior. The findings contribute to a deeper understanding of the interplay between sustainability and financial performance in today's investment landscape
Aggregating sentiment in Europe: the relationship with volatility and returns
Full text linkThis paper presents several proposals for creating an aggregate sentiment index for the European stock market. We achieve this objective by using the OWA and WOWA operators, which have been successful in finance and have a strong financial interpretation. We compute ten different aggregate sentiment indices for the 2007-2021 period and evaluate their ability to provide information about current and future market volatility and returns. We find several results of interest for both investors and policymakers. Sentiment indices have a strong negative relationship with market volatility. Extreme values of sentiment can predict future market returns, with low values indicating positive returns and high values suggesting negative returns. Finally, using stock market capitalisation as an input of the WOWA operator enhances explanatory power of the indices on future market returns compared to the OWA operator
Climate risk measures and main data providers
Full text linkESG (Environmental, Social, and Governance) ratings are becoming
increasingly significant in guiding financial investment decisions.
However, numerous studies have highlighted discrepancies in ESG ratings
across different data providers, primarily due to variations in the
methodologies they employ. To address this issue, we provide a summary
of the key ESG data providers, focusing on two distinct aspects: ESG
ratings for firms and ESG ratings for countries. These two topics are
essential in empirical analysis to attempt to integrate both dimensions—
firm and country—into the evaluation process for assessing whether they
play a significant role in defining climate risk. Moreover, this integration
aids in understanding the implications for climate risk premiums in the
stock market. By considering both the firm and country dimensions, we
can better capture the multifaceted nature of climate risk and its impact
on investment outcomes. The purpose of this report is twofold: first, we
retrieve information from websites concerning the ESG criteria and
methodologies of major climate indicator data providers, evaluating their
transparency and accountability, and highlighting the differences
between public and private sources. Second, we focus on alternative
measures of climate risk that should be used in empirical analysis. Indeed,
ESG ratings at both the company and country levels are not the only
measures of climate risk exposures. In particular, while ESG ratings or
solely the Environmental dimension are often used to assess transition
climate risk, they are less frequently applied to assess physical risk
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