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Concurrent with the rapid development of the market for catastrophe (cat) bonds, a steady decline in their risk premia has been observed. Whether the latter trend is consistent with the evolution of natural disasters risk is an open question. Indeed, a large share of outstanding risk capital in the cat bonds market appears to be exposed to some climate change-related risk as, for instance, hurricane risk, which global warming is expected to enhance. This paper addresses the above issue by assessing the global warming evidence, its implications for the natural environment and the drivers of cat bonds risk premia. We find that radiative forcing, i.e. the net insolation absorbed by the Earth, drives the warming trend in temperature anomalies and the trend evolution of natural phenomena, such as ENSO and Atlantic hurricanes, enhancing their disruptive effects. Hence, in the light of the ongoing contributions of human activity to radiative forcing, i.e., greenhouse gases emissions, natural disasters risk appears to be on a raising trend. Yet, the latter does not appear to have been accurately priced in the cat bonds market so far. In fact, while we find that the falling trend in cat bonds multiples is accounted by the expansionary monetary stance pursued by the Fed, we do also find evidence of significant undervaluation of natural disasters risk.

The paper investigates the linkages between temperature anomalies, radiative forcing and ENSO. By means of a new flexible trend modeling approach, we uncover a nonlinear linkage between radiative forcing and global temperature anomalies. The nonlinear trend closely tracks the low frequency evolution in temperature anomalies, also accounting for the mid-end 1990s level switch, the 1998-2013 "warming hiatus" and the current steepening in trend temperatures. Radiative forcing is also found to account for trend dynamics in the Southern Oscillation Index (SOI), therefore providing support for the view that global warming might affect natural variability oscillations such as ENSO, and therefore enhance their disruptive effects. We also document the feature of time-varying volatility of temperature anomalies and SOI, which is well described by an IGARCH process. By means of a new dynamic conditional correlation model (SP-DCC), we finally document the presence of time-varying conditional correlations relating temperature anomalies across various zones and SOI. The correlation pattern is found to be consistent with the effects of ENSO events in the Tropics and their teleconnections.

The paper investigates the macroeconomic and financial effects of oil prices shocks in the euro area since its creation in 1999, with a special focus on the recent slump. The analysis is carried out episode by episode, within a time-varying parameter framework, consistent with the view that "not all the oil price shocks are alike", yet without imposing any a priori identification assumption. We find evidence of recessionary effects triggered not only by oil price hikes, but also by oil price slumps in some cases, likewise for the most recent episode, which is also rising deflation risk and financial distress. In addition through uncertainty effects, the current slump might then be depressing aggregate demand by increasing the real interest rate, as ECB monetary policy is already conducted at the zero lower bound. The increase in real money balances following the slump points to the accommodation of the shock by the ECB, concurrent with the implementation of the Quantitative Easing policy (Q.E.). Yet, in so far as Q.E failed to generate inflationary expectations within the current and expected environment of soft oil prices, the case for a more expansionary use of fiscal policy than in the past would become compelling, in order to counteract the deflationary and recessionary threats to the euro area.

Questa tesi di dottorato è composta da due capitoli sostanziali in diverse aree della finanza empirica. This PhD dissertation is comprised of two substantive chapters in different areas of empirical finance. In the first chapter which is published in the “International Review of Financial Analysis”, I dissect the investing style of different hedge fund strategies, while in the second chapter, I investigate the stock-oil conditional comoments, by employing a battery of modern econometric methods. The following paragraphs provide a summary for each chapter. The first chapter, “Dissecting Hedge Funds' Strategies” (joint with Asmerilda Hitaj), dissects the dynamics of the hedge fund industry with four financial markets, including the equity market, commodities, currencies, and debt market by employing a large number of assets from these markets. We employ four main representative hedge fund strategy indices, and a cap-weighted global index to estimate an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model using daily data from April 2003 to May 2021. We break down the performance, riskiness, investing style, volatility, dynamic correlations, and shock transmissions of each hedge fund strategy thoroughly. Further, the impact of commodity futures basis on hedge funds’ return is analyzed. Comparing the dynamic correlations during the 2008 global financial crisis (GFC) with COVID-19 pandemic reveals changing patterns in hedge funds’ investing styles. There are strong and pervasive shock spillovers from hedge fund industry to other financial markets, especially to futures commodities. An increase in the futures basis of several commodities drives up hedge funds’ performance. While hedge fund industry underperforms compared to equity market and commodities, the risk-reward measures show that hedge funds are superior to other markets, and safer than the bond market. The second chapter “Stock-Oil Comovements Through Fear, Uncertainty, and Expectations: Evidence from Conditional Comoments", investigates the dependencies and comovements between the S&P 500 and WTI by means of time-varying conditional comoments from April 1983 to December 2021 at the daily level. The conditional comoments mark a new pattern between the two markets' dependencies since the 2008 global financial crisis (GFC). I employ three macroeconomic sentiment measures, including VIX (representing fear), economic policy uncertainty (representing uncertainty), and expected business condition index (representing expectation), to investigate the underlying mechanism for the new emerging stock-oil comovements, using the time-varying parameter vector autoregression (TVP-VAR) to investigate the time-varying impulse responses, and the nonlinear autoregressive distributed lag (NARDL) model to analyze the asymmetries in the short- and long-run effects of the sentiment indices for the pre- and post-GFC periods. The conditional comoments of both markets change direction since the GFC, with crude oil showing a stronger dependence on the S&P 500's return, skewness, and tail events than its counterpart. Overall, the time-varying impulse responses show heightened short-lived responses to the three sentiment indices after the 2008 GFC, with an asymmetric response from the conditional comoments of WTI (negative) and S&P 500 (positive) to the positive shocks in the fear index during the post-GFC period. The NARDL regression results prove that the explanatory power of the three sentiment indices increase largely after the GFC, with strong short-run asymmetries. Further investigations reveal that oil-specific fear index has weaker effect on stock-oil comoments than VIX.

We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given.