The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables\u2019 relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles

Gender wage inequality: new evidence from penalized expectile regression

The Machado-Mata decomposition building on quantile regression has been extensively analyzed in the literature focusing on gender wage inequality. In this study, we generalize the Machado-Mata decomposition to the expectile regression framework, which, to the best of our knowledge, has never been applied in this strand of the literature. In contrast, in recent years, expectiles have gained increasing attention in other contexts as an alternative to traditional quantiles, providing useful statistical and computational properties. We flexi bly deal with high-dimensional problems by employing the Least Absolute Shrinkage and Selection Operator. The empirical analysis focuses on the gender pay gap in Germany and Italy. We find that depending on the estimation approach (i.e. expectile or quantile regres sion) the results substantially differ along some regions of the wage distribution, whereas they are similar for others. From a policy perspective, this finding is important as it affects conclusions about glass ceiling and sticky floors

The US real GNP is trend-stationary after all

This article applies the Fractional Frequency Flexible Fourier Form (FFFFF) Dickey–Fuller (DF)-type unit root test on the natural logarithm of US real GNP over the quarterly period of 1875:1–2015:2, to determine whether the same is trend- or difference-stationary. While standard and Integer Frequency Flexible Fourier Form DF-type test fails to reject the null of unit root, the relatively more powerful FFFFF DF-type test provides strong evidence of the real GNP as being trend-stationary, i.e. US output returns to a deterministic log-nonlinear trend in the long run.http://www.tandfonline.com/loi/rael202018-01-13hj2017Economic

Quantile regression methods in economics and finance

In the recent years, quantile regression methods have attracted relevant interest in the statistical and econometric literature. This phenomenon is due to the advantages arising from the quantile regression approach, mainly the robustness of the results and the possibility to analyse several quantiles of a given random variable. Such as features are particularly appealing in the context of economic and financial data, where extreme events assume critical importance. The present thesis is based on quantile regression, with focus on the economic and financial environment. First of all, we propose new approaches in developing asset allocation strategies on the basis of quantile regression and regularization techniques. It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and performance indicators. In particular, we introduce a risk-adjusted profitability measure, useful in evaluating financial portfolios under a pessimistic perspective, since the reward contribution is net of the most favorable outcomes. Moreover, as we consider large portfolios, we also cope with the dimensionality issue by introducing an l1-norm penalty on the assets weights. Secondly, we focus on the determinants of equity risk and their forecasting implications. Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate, in a forecasting perspective, the impact of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables' relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles. Finally, we study the dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk. We analyse the relevance of recently developed news-based measures of economic policy uncertainty and equity market uncertainty in causing and predicting the conditional quantiles and distribution of the crude oil variations, defined both as returns and squared returns. For this purpose, on the one hand, we study the causality relations in quantiles through a non-parametric testing method; on the other hand, we forecast the conditional distribution on the basis of the quantile regression approach and the predictive accuracy is evaluated by means of several suitable tests. Given the presence of structural breaks over time, we implement a rolling window procedure to capture the dynamic relations among the variables.Negli ultimi anni, la regressione quantile ha suscitato un notevole interesse nella letteratura statistica ed econometrica. Tale fenomeno è dovuto ai vantaggi derivanti dalla regressione quantile, in particolare, la robustezza dei risultati e la possibilità di analizzare differenti quantili di una certa variabile casuale. Tali caratteristiche sono particolarmente rilevanti nel contesto di dati economici e finanziari, data la cruciale rilevanza di eventi estremi. Innanzitutto, la tesi introduce approcci innovativi per la definizione di strategie di "asset allocation" sulla base di modelli di regressione quantile penalizzati. Come noto in letteratura, la regressione quantile minimizza il rischio estremo di portafoglio, nel momento in cui ci si focalizza sulla coda sinistra della distribuzione della variabile di risposta. Nella presente tesi si dimostra che, considerando l'intera distribuzione, è possibile ottimizzare diversi indicatori di performance e di rischiosità. In particolare, si introduce una nuova misura di performance aggiustata per il rischio, utile a valutare i portafogli finanziari in ottica pessimista. Inoltre, si dimostra che l'introduzione di una "l1-norm penalty" sui pesi dei titoli implica vantaggi non indifferenti su portafogli di notevoli dimensioni. In secondo luogo, la tesi analizza i fattori determinanti del rischio sul mercato azionario, con particolare enfasi sulle loro implicazioni previsionali. Dalla combinazione delle stime di volatilità realizzata di tipo "range-based", corrette per "noise" microstrutturali e "jumps", e modelli di regressione quantile, è possibile valutare, in ottica previsionale, l'impatto dei fattori determinanti del rischio in diversi stati del mercato e, senza assunzioni sulle innovazioni dei "realized range", ottenere le previsioni sia puntuali che sull'intera distribuzione. Inoltre, l'implementazione di una procedura a finestre mobili consente di analizzare l'evoluzione nel tempo delle relazioni tra le variabili d'interesse. Infine, l'ultimo aspetto trattato dalla tesi riguarda l'impatto dinamico dell'incertezza nel causare e prevedere la distribuzione dei rendimenti e del rischio del mercato petrolifero. L'attenzione è posta sull'impatto di due indici di tipo "news-based", recentemente elaborati, che misurano l'incertezza, rispettivamente, sulla politica economica e sui mercati azionari nel causare e prevedere le dinamiche del mercato petrolifero. A tale scopo, da un lato, la tesi esplora le relazioni di causalità nei quantili utilizzando un test non parametrico; dall'altro, la distribuzione condizionata è prevista sulla base di modelli di regressione quantile. La capacità previsionale dell'approccio adottato è valutata mediante differenti test. Data la presenza di break strutturali nel tempo, una procedura a finestre mobili è utilizzata al fine di catturare le dinamiche nei modelli proposti

Breakup and Default Risks in the Great Lockdown

n this paper, we exploit CDS quotes for contracts denominated in different currencies and with different default clauses to estimate the risk of a breakup of the Eurozone and the propagation of breakup and default risks after the COVID-19 shock. Our main result is that the risk of a Eurozone breakup is significant although, quantitatively, it is not larger than in the period before the COVID-19 shock. In addition, we find that an increase in the redenomination risk in one country is associated with an increase in default premia and bond spreads in other Eurozone countries. Finally, we find that a sizeable fraction of the changes in the cost of insuring against redenomination and default reflects two additional factors: the first captures the insurance cost against a euro depreciation conditional on redenomination, while the second captures liquidity premia

Estimation and model-based combination of causality networks among large US banks and insurance companies

Causality is a widely-used concept in theoretical and empirical economics. The recent financialeconomics literature has used the standard Granger causality to detect for the presenceof contemporaneous links among financial institutions, that, in turn, determine a networkstructure. Subsequent studies have combined the estimated networks with traditional pricingor risk measurement models to improve their fit to empirical data. In this paper, we providetwo contributions. First, we show how to use a linear factor model as a device for estimatinga combination of several networks that monitor the links across variables from differentviewpoints. Second, we highlight the advantages of combining quantile-based methods withthe Granger causality when the focus is on risk propagation. The empirical evidence supportsour contributions.JRC.B.1-Finance and Econom

Estimation and model-based combination of causality networks

Causality is a widely-used concept in theoretical and empirical economics. The recent financial economics literature has used Granger causality to detect the presence of contemporaneous links between financial institutions and, in turn, to obtain a network structure. Subsequent studies combined the estimated networks with traditional pricing or risk measurement models to improve their fit to empirical data. In this paper, we provide two contributions: we show how to use a linear factor model as a device for estimating a combination of several networks that monitor the links across variables from different viewpoints; and we demonstrate that Granger causality should be combined with quantile-based causality when the focus is on risk propagation. The empirical evidence supports the latter claim