Investors’ behaviour in the Chinese Stock Exchanges: Empirical Evidence in a Systemic Approach

This paper investigates the Chinese mainland Stock Exchanges and their following interconnecting features: savers’ attitude towards stock investments, investors’ trading behaviour and stock returns explanations. We evaluate the effectiveness of the recent efforts made by the Chinese authorities to improve the level of legal protections for shareholders and the opening-up of the Chinese Stock Markets to foreign investors. The whole analysis is carried out through a system of simultaneous equations. The main results are that Chinese shareholders and stock markets are mostly driven by emotional behaviour. Stock market returns are barely influenced by the overall Chinese economic booming, but reveal the presence of speculative influences. Investors’ behaviour, as well as general trading activities, hardly seems to be affected by the legal framework introduced by the national Authorities.Chinese Stock Exchanges, shareholders’ rights, corporate governance, investors’ behaviour, system of simultaneous equations

Nonlinear Adjustment in US Bond Yields: an Empirical Analysis with Conditional Heteroskedasticity

Starting from the work by Campbell and Shiller (1987), empirical analysis of interest rates has been conducted in the framework of cointegration. However, parts of this approach have been questioned recently, as the adjustment mechanism may not follow a simple linear rule; another line of criticism points out that stationarity of the spreads is difficult to maintain empirically. In this paper, we analyse data on US bond yields by means of an augmented VAR specification which approximates a generic nonlinear adjustment model. We argue that nonlinearity captures macro information via the shape of the yield curve and thus provides an alternative explanation for some findings recently appeared in the literature. Moreover, we show how conditional heteroskedasticity can be taken into account via GARCH specifications for the conditional variance, either univariate and multivariate.interest rates, cointegration, nonlinear adjustment, conditional heteroskedasticity

Asset management with TEV and VaR constraints: the constrained efficient frontiers

It is well known that investors usually assign part of their funds to asset managers who are given the task of beating a benchmark portfolio. On the other hand, the risk management office could impose some restrictions to the asset managers' activity in order to mantain the overall portfolio risk under control. This situation could lead managers to select non efficient portfolios in the total return and absolute risk perspective. In this paper we focus on portfolio efficiency when a tracking error volatility (TEV) constraint holds. First, we define the TEV Constrained-Efficient Frontier (ECTF), a set of TEV constrained portfolios that are mean-variance efficient. Second, we discuss the effects on such boundary when a VaR and/or a variance restriction is also added

starvars: An R Package for Analysing Nonlinearities in Multivariate Time Series

Although linear autoregressive models are useful to practitioners in different fields, often a nonlinear specification would be more appropriate in time series analysis. In general, there are many alternative approaches to nonlinearity modelling, one consists in assuming multiple regimes. Among the possible specifications that account for regime changes in the multivariate framework, smooth transition models are the most general, since they nest both linear and threshold autoregressive models. This paper introduces the starvars package which estimates and predicts the Vector Logistic Smooth Transition model in a very general setting which also includes predetermined variables. In comparison to the existing R packages, starvars offers the estimation of the Vector Smooth Transition model both by maximum likelihood and nonlinear least squares. The package allows also to test for nonlinearity in a multivariate setting and detect the presence of common breaks. Furthermore, the package computes multi-step-ahead forecasts. Finally, an illustration with financial time series is provided to show its usage

The impact of attractiveness on job opportunities in Italy: a gender field experiment

none3This paper assesses the impact of being attractive and not being native on the gender gap in the opportunity of obtaining a job in Italy. To do so, we propose a field experiment that consists in sending 9680 fictitious curricula vitae to real firms looking for employees. We estimate an Heckit model in order to consider different response from firms and then to calculate the probability to receive a callback. We show that gender gap in opportunity of receiving a callback is a very important issue and this gap is affected by interaction with both attractiveness and not being italian natives, especially for the most qualified jobs.Pubblicazione online open access agosto 2020. Pubblicazione cartacea 2021openBusetta, Giovanni; Fiorillo, Fabio; Palomba, GiulioBusetta, Giovanni; Fiorillo, Fabio; Palomba, Giuli

Sentinel-1 Flood Delineation with Supervised Machine Learning

Floods are one of the major natural hazards in terms of affected people and economic damages. The increasing and often uncontrolled urban sprawl together with climate change effects will make future floods more frequent and impacting. An accurate flood mapping is of paramount importance in order to update hazard and risk maps and to plan prevention measures. In this paper, we propose the use of a supervised machine learning approach for flood delineation from satellite data. We train and evaluate the proposed algorithm using Sentinel-1 acquisition and certified flood delineation maps produced by the Copernicus Emergency Management Service across different geographical regions in Europe, achieving increased performances against previously proposed supervised machine learning approaches for flood mapping

Nonlinear Adjustment in US Bond Yields: an Empirical Analysis with Conditional Heteroskedasticity

Starting from the work by Campbell and Shiller (1987), empirical analysis of interest rates has been conducted in the framework of cointegration. However, parts of this approach have been questioned recently, as the adjustment mechanism may not follow a simple linear rule; another line of criticism points out that stationarity of the spreads is difficult to maintain empirically. In this paper, we analyse data on US bond yields by means of an augmented VAR specification which approximates a generic nonlinear adjustment model. We argue that nonlinearity captures macro information via the shape of the yield curve and thus provides an alternative explanation for some findings recently appeared in the literature. Moreover, we show how conditional heteroskedasticity can be taken into account via GARCH specifications for the conditional variance, either univariate and multivariate

Investors’ behaviour in the Chinese Stock Exchanges: Empirical Evidence in a Systemic Approach

This paper investigates the Chinese mainland Stock Exchanges and their following interconnecting features: savers’ attitude towards stock investments, investors’ trading behaviour and stock returns explanations. We evaluate the effectiveness of the recent efforts made by the Chinese authorities to improve the level of legal protections for shareholders and the opening-up of the Chinese Stock Markets to foreign investors. The whole analysis is carried out through a system of simultaneous equations. The main results are that Chinese shareholders and stock markets are mostly driven by emotional behaviour. Stock market returns are barely influenced by the overall Chinese economic booming, but reveal the presence of speculative influences. Investors’ behaviour, as well as general trading activities, hardly seems to be affected by the legal framework introduced by the national Authorities