Raffaele Mattera

Sapienza University of Rome | la sapienza · Department of Economic and Social Sciences

Time series data are commonly clustered based on their distributional characteristics. The moments play a central role among such characteristics because of their relevant informative content. This paper aims to develop a novel approach that faces still open issues in moment-based clustering. First of all, we deal with a very general framework of t...

This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock market. To improve the forecasting accuracy, the model integrates temporally lagged volatility information and information from adjacent nodes, which may instantaneously sp...

This paper discusses the use of forecast reconciliation with stock price time series and the corresponding stock index. The individual stock price series may be grouped using known meta-data or other clustering methods. We propose a novel forecasting framework that combines forecast reconciliation and clustering, to lead to better forecasts of both...

In this paper we propose a framework for fuzzy clustering of time series based on directional volatility spillovers. In the case of financial time series, detecting clusters of volatility spillovers provides insights into the market structure, which can be useful to both portfolio managers and policy makers. We measure directional—i.e. “From” and “...

This paper investigates the business cycle synchronization in Africa, which is important for the definition of optimal monetary unions. Previous studies adopted either a cross-sectional or a time series approach, and this may have led to substantial variation in the findings for common business cycles across African countries. Considering fifty-two...

Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent (h t). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Therefore, the Hurst exponent offers a level of comparison between theoretical and empirical market effici...

Thickness of pyroclastic deposits governs various geomorphological and hydrological processes, but studies on the areas characterized by pyroclastic soil coverage are limited in the literature worldwide and the existing models predict thickness mainly based on morphological features of the slope. In this paper, additional variables are also derived...

This paper proposes a probabilistic model for the evaluation of the peak components of the return of a commodity. The ground of the study lies in the evidence that the spikes in the returns are due to the shocks occurring in the external environment. We follow an approach based on a particular class of point processes—the Spatial Mixed Poisson Proc...

This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007–2020. The stud...

In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Followin...

Time series distribution parameters, such as mean and variance, are usually used as features for clustering. In this paper, starting from the hypothesis that the distributional features of the time series are time-varying, a frequency domain clustering approach based on time-varying parameters is applied. Under a specified probability distribution,...

A large amount of assets characterizes high-dimensional portfolio selection problems compared to temporal observation. In such a high-dimensional framework, the asset allocation is unfeasible because the covariance matrix obtained with the usual sample estimators cannot be inverted. This paper proposes a new shrinkage estimator based on reinforceme...

Little attention has been devoted to the long memory among the different data features considered for clustering time series. Following previous literature, we measure the long memory of a time series through the estimated Hurst exponent. However, we exploit the fact that a constant value for the Hurst exponent h is unrealistic in many practical ex...

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time s...

Agent-based models are computational approaches used to reproduce the interactions between economic agents. ese models are widely applied in many contexts to get deeper understanding about agents' behaviors within complex systems. In this paper, we provide a bibliometric analysis about agent-based models in finance and, considering bibliographic co...

Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data. We propose an Integer GARCH-based Fuzzy C-medoids (INGARCH-FCMd) method for clustering count time series based on a Mahalanobis distan...

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate th...

Well-being is a multidimensional concept that cannot be described using a single indicator. By the synthesis of different dimensions it is possible to obtain composite indicators (CIs). Principal Components Analysis (PCA) is one of the most popular multivariate statistical techniques used building CIs. However, the fact that PCA does not take into...

In this paper, we follow an Instrumental Variable (IV) estimation strategy to assess the impact of crowd effects on the outcomes of the Italian Serie A matches. We use weather conditions to instrument for crowd attendance. We verify the validity of our identification strategy by taking advantage of matches played during the COVID-19 outbreak in Ita...

The VIX is a proxy for the implied volatility, computed considering Standard & Poor’s 500 Index data. It widely regarded as a measure of turbulence in U.S. and global financial markets. Hence, forecasting the VIX is essential for both portfolio managers and policy makers. By modeling the S&P 500 Index as a multifractional Brownian motion, we exploi...

The SARS-Cov-2 has spread differently over space and time worldwide. By monitoring the contagion’s time evolution, the November 3 2020 the Italian government introduced differentiated regime of restrictions among its regions. This experiment demonstrated that public health policies can be effectively designed by means of clustering. This paper prop...

The VIX is a proxy for the implied volatility, computed considering Standard & Poor's 500 Index data. It widely regarded as a measure of turbulence in U.S. and global financial markets. Hence, forecasting the VIX is essential for both portfolio managers and policy makers. By modeling the S&P 500 Index as a multifractional Brownian motion, we exploi...

In this paper we test the use of statistical and Machine Learning models to estimate the implied volatility (IV) in out of sample approach. Given an observed market option price $c_{mkt}$ the Black-Scholes implied volatility IV can be determined by solving the following equation: BS(\sigma; S, K, \tau,r)= $c_{mkt}$. The monotonicity of the Black-S...

Market inefficiency is a latent concept, and it is difficult to be measured by means of a single indicator. In this paper, following both the adaptive market hypothesis (AMH) and the fractal market hypothesis (FMH), we develop a new time-varying measure of stock market inefficiency. e proposed measure, called composite efficiency index (CEI), is es...

Several studies deal with the development of advanced statistical methods for predicting football match results. These predictions are then used to construct profitable betting strategies. Even if the most popular bets are based on whether one expects that a team will win, lose, or draw in the next game, nowadays a variety of other outcomes are ava...

Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding both alternative predictors (e.g., interes...

Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding alternative predictors and statistical mod...

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power dis...

This paper develops a new time series clustering procedure allowing for heteroskedasticity, non-normality and model's non-linearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelation-ba...

This paper develops a new time series clustering procedure allowing for heteroskedasticity, non-normality and model's non-linearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelation-ba...

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series have motivated the development of classes of distributions that can accommodate properties such as heavy tails and skewness. Thanks to its flexibility, the Skew Exponential Power D...

We introduced a new method to compute the European Call (and Put) Option price under the assumption of multifractional Brownian motion (mBm). The reason why we need a procedure for estimating the Option price is due to the absence of a closed formula for this process. To compute the Option price, we first simulated the logarithmic price under mBm a...

Composite confidence indicators are widely used to nowcast GDP. In this paper, we aim to construct a new composite confidence indicator which weighting scheme reflects the impact of consumer and business confidence on economic conditions. While GDP is quarterly measured, confidence indicators are monthly recorded. Our approach allows us to deal wit...

Recently, cryptocurrencies have attracted a growing interest from investors, practitioners and researchers. Nevertheless, few studies have focused on the predictability of them. In this paper we propose a new and comprehensive study about cryptocurrency market, evaluating the forecasting performance for three of the most important cryptocurrencies...

The most common purpose of seasonal adjustment is to provide an estimate of the current trend so that judgmental short-term forecasts can be made. Bell (Proceedings of the American Statistical Association, 1995) formally considered how model-based seasonal adjustment could be done in order to facilitate the forecasting, showing that, from a theoret...

Recently, great strides have been made in predicting volatility in the financial market. However, the so widely used GARCH model suffer of several problems, like the normality assumption which can lead to unreliable estimates and forecasts. In order to solve this problem, it is possible to introduce different new assumptions in the classical GARCH...

Several authors have shown better results in forecasting economic variables by considering the sentiment values in their models. Few studies have focused on the identification of the causes which explain opinions and beliefs. In this paper, we propose a methodological framework based on Distributed Lag (DL) models in order to identify dynamic causa...

Much is told in the literature about the determinants that lead graduates to migrate. However, it is crucial to understand how these dynamics have changed after the new worldwide financial crisis: nowadays, inequality has increased, and new generations tend to travel much more than the previous ones, being more prone to look for better opportunitie...

Katsiampa (2017) showed that, among different GARCH models, the optimal conditional heteroskedasticity model regarding the goodness-of-fit to Bitcoin price data is the AR-Component GARCH (AR-CGARCH) model. However, in that paper the author doesn’t take into account for statistical proprieties of Bitcoin’s return distribution and even showing both s...

Sustainable development is a relatively recent approach to economic growth measure, which has the goal of measuring the standards of living in a more comprehensive way. Following this approach, the GDP is not the only right measure of economic growth and, for this reason, several Institutions (as IMF and OECD) have developed the concept of sustaina...

Multicollinearity is one of the most important issues in regression analysis, as it produces unstable coefficients’ estimates and makes the standard errors severely inflated. The regression theory is based on specific assumptions concerning the set of error random variables. In particular, when errors are uncorrelated and have a constant variance,...