Essays in Macroeconomics

The dissertation consists of four papers that aim at providing new contributions in the field of macroeconomics, monetary policy and financial stability. The first paper proposes a new Dynamic Stochastic General Equilibrium (DSGE) model with credit frictions and a banking sector to study the pro-cyclicality of credit and the role of different prudential regulatory frameworks in affecting business cycle fluctuations and in restoring macroeconomic and financial stability. The second paper develops a simple DSGE model capable of evaluating the effects of large purchases of treasuries by central banks. This theoretical framework is employed to evaluate the impact on yields and the macroeconomy of large purchases of medium- and long-term government bonds recently implemented in the US and UK. The third paper studies the effects of ECB communications about unconventional monetary policy operations on the perceived sovereign risk of Italy over the last five years. The empirical results are derived from both an event-study analysis and a GARCH model, which uses Italian long-term bond futures to disentangle expected from unexpected policy actions. The fourth paper proposes a DSGE model with an endogenous term structure of interest rates, which is able to replicate the stylized facts regarding the yield curve and the term premium in the US over the period 1987:3-2011:3, without compromising its ability to match macro dynamics.

Essays in Macroecometrics: methodological aspects and empirical applications

In the first chapter, I develop a panel no-cointegration test which extends Pesaran, Shin and Smith (2001)'s bounds test to the panel framework by considering the individual regressions in a Seemingly Unrelated Regression (SUR) system. This allows to take into account unobserved common factors that contemporaneously affect all the units of the panel and provides, at the same time, unit-specific test statistics. Moreover, the approach is particularly suited when the number of individuals of the panel is small relatively to the number of time series observations. I develop the algorithm to implement the test and I use Monte Carlo simulation to analyze the properties of the test. The small sample properties of the test are remarkable, compared to its single equation counterpart. I illustrate the use of the test through a test of Purchasing Power Parity in a panel of EU15 countries. In the second chapter of my PhD thesis, I verify the Expectation Hypothesis of the Term Structure in the repurchasing agreements (repo) market with a new testing approach. I consider an "inexact" formulation of the EHTS, which models a time-varying component in the risk premia and I treat the interest rates as a non-stationary cointegrated system. The effect of the heteroskedasticity is controlled by means of testing procedures (bootstrap and heteroskedasticity correction) which are robust to variance and covariance shifts over time. I fi#nd that the long-run implications of EHTS are verified. A rolling window analysis clarifies that the EHTS is only rejected in periods of turbulence of #financial markets. The third chapter introduces the Stata command "bootrank" which implements the bootstrap likelihood ratio rank test algorithm developed by Cavaliere et al. (2012). The command is illustrated through an empirical application on the term structure of interest rates in the US.

Topics in climate change risk

In this Thesis, we analyze how climate risk impacts economic players and its consequences on the financial markets. Essentially, literature unravels two main channels through which climate change poses risks to the status quo, namely physical and transitional risk, that we cover in three works. Firstly, the call for a global shift to a net-zero economy implicitly devalues assets that contribute to global warming that regulators are forcing to dismiss. On the other hand, abnormal changes in the temperatures as well as weather-related events challenge the environmental equilibrium and could directly affect operations as well as profitability. We start the analysis with the physical component, by presenting a statistical measure that generally represents shocks to the distribution of temperature anomalies. We oppose this statistic to classical physical measures and assess that it is the driver of the electricity consumption, in the weather derivatives market, and in the cross-section of equity returns. We find two transmission channels, namely investor attention, and firm operations. We then analyze the transition risk component, by associating a regulatory horizon characterization to fixed income valuation. We disentangle a risk driver for corporate bond overperformance that is tight to change in credit riskiness. After controlling a statistical learning algorithm to forecast excess returns, we include carbon emission metrics without clear evidence. Finally, we analyze the effects of change in carbon emission on a regulated market such as the EU ETS by selecting utility sector corporate bond and, after controlling for the possible risk factor, we document how a firm’s carbon profile differently affects the term structure of credit riskiness.

A Unified Model for XVA, including Interest Rates and Rating

We start in Chapter 2 to investigate linear matrix-valued SDEs and the Itô-stochastic Magnus expansion. The Itô-stochastic Magnus expansion provides an efficient numerical scheme to solve matrix-valued SDEs. We show convergence of the expansion up to a stopping time τ and provide an asymptotic estimate of the cumulative distribution function of τ. Moreover, we show how to apply it to solve SPDEs with one and two spatial dimensions by combining it with the method of lines with high accuracy. We will see that the Magnus expansion allows us to use GPU techniques leading to major performance improvements compared to a standard Euler-Maruyama scheme. In Chapter 3, we study a short-rate model in a Cox-Ingersoll-Ross (CIR) framework for negative interest rates. We define the short rate as the difference of two independent CIR processes and add a deterministic shift to guarantee a perfect fit to the market term structure. We show how to use the Gram-Charlier expansion to efficiently calibrate the model to the market swaption surface and price Bermudan swaptions with good accuracy. We are taking two different perspectives for rating transition modelling. In Section 4.4, we study inhomogeneous continuous-time Markov chains (ICTMC) as a candidate for a rating model with deterministic rating transitions. We extend this model by taking a Lie group perspective in Section 4.5, to allow for stochastic rating transitions. In both cases, we will compare the most popular choices for a change of measure technique and show how to efficiently calibrate both models to the available historical rating data and market default probabilities. At the very end, we apply the techniques shown in this thesis to minimize the collateral-inclusive Credit/ Debit Valuation Adjustments under the constraint of small collateral postings by using a collateral account dependent on rating trigger.