Functional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal...

We introduce a functional volatility process for modeling volatility trajectories for high frequency observations in financial markets and describe functional representations and data-based recovery of the process from repeated observations. A study of its asymptotic properties, as the frequency...

We use the copula approach to study the structure of dependence between sell-side analysts’ consensus recommendations and subsequent security returns, with a focus on asymmetric tail dependence. We match monthly vintages of I/B/E/S recommendations for the period January–December 2011 with...

Functional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal...

Tanulmányunk egyrészt arra a kérdésre keresi a választ, vajon helytálló-e a tőkepiaci árazási modell (CAPM) azon feltevése, hogy a piaci kockázat mérőszáma, a béta és a várható hozam között lineáris kapcsolat áll fenn. Másrészt nem tudjuk, hogy megalapozott-e a...

A nonparametric model that includes non-Gaussian characteristics of skewness and kurtosis is proposed based on the cubic market capital asset pricing model. It is an equilibrium pricing model but risk-neutral valuation can be introduced through return data transformation. The model complies with...

This paper proposes computational framework for empirical estimation of Financial Agent-Based Models (FABMs) that does not rely upon restrictive theoretical assumptions. We customise a recent methodology of the Non-Parametric Simulated Maximum Likelihood Estimator (NPSMLE) based on kernel...

In this paper, we discuss estimation procedure and various inferential methods for varying coefficient panel data models that include spatially correlated error components. Our estimation procedure is an extension of the quasi-maximum likelihood method for spatial panel data regression to the...

We propose a new semi-parametric model for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree-boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility. To overcome the poor predicting power of...

A copula approach is adopted to examine the extreme return-volume relationship in six emerging East-Asian equity markets. The empirical results indicate that the return-volume dependence is significant and asymmetric at extremes for all six East-Asian markets. In particular extremely high...