WHAT WE DO
Unit Directors: Massimo Guidolin & Massimo Marcellino
Recently the unit has been focussing on the following related topics:
1. CRYPTOCURRENCIES AS AN ASSET CLASS: THE ECONOMETRICS AND ASSET PRICING OF CRYPTO RETURNS
The goal of the project is to estimate and compare statistical, predictive models for the cross-section of cryptocurrency returns to understand how whether both their excess returns and their risky co-move over time. The key question is then whether cryptocurrencies ora a subset of them may represent a novel asset class and whether by diversifying within the crypto domain, any diversification benefits may be earned. Finally, a part of the research is devoted to test whether the cryptocurrency space may be evolving out of an initial, sentiment-driven, bubble-prone phase into a more mature stage in which, indeed, cryptocurrencies may come to represent a viable investable asset class.
2. BIG-DATA, SENTIMENT-DRIVEN INDICATORS IN ASSET PRICING AND PORTOFLIO SELECTION
The objective is to survey and employ modern techinques from data analysis (e.g. based on Twitter and Facebook-scraped data) and machine learning to derive and experiment with indicators of news recency and sentiment to forecast asset returns, their co-movements, and especially their risk over different market cycles.
3. SEGMENTATION AND LOCAL PRICING FACTORS IN THE COMMODITIES MARKET
The goal of the project is to estimate and compare stochastic discount factor models -- hence ruling out arbitrage opportunities -- that include vs. do not include "local" (i.e., specific to commodity markets only) factors, such as Average (Level), Carry, and Momentum as featured in recent research.
4. MODELLING AND FORECASTING REGIME SHIFTS IN THE DYNAMIC RELATIONSHIP BETWEEN BOND BASE AND CDS PREMIA
It is well known that a long-run relationships should tie down the risk premium on bonds and the CDS premium for an identical maturity credit default swap that allows an investor to hedge against default risk. Empirically, such a relationship is violated for a number of rational reasons (among them, liquidity and convenience yields of holding bonds vs. just purchasing naked CDS protection) that are found to be time-varying. The goal of the project is to model and forecast deviations from such a no arbitrage relation in a regime switching framework both in the Treasury and the corporate credit market.
5. CROSS-ASSET CONTAGION IN TIMES OF CRISIS
Motivated by the recent U.S. subprime crisis that provides a clean exogenous shock in a specific market (low-grade mortgage-backed asset security market), the objective is to empirically characterize the patterns through which shocks to different assets propagate across markets (e.g., ABS higher grade, Treasury repo, Treasury bond, corporate bond, and stock markets).
6. WHAT IS TRUE RISK-ADJUSTED BENEFIT OF HEDGE FUNDS: A SYSTEMATIC ASSET ALLOCATION PERSPECTIVE
To assess the often claimed out-performance of hedge fund investments, the project explores the benefits from investing in a range of hedge fund strategies for a long-horizon risk-averse investor who is already well diversified across stocks, REITs, and government and corporate debt. We plan a range of recursive out-of-sample experiments similarly to recent papers by Guidolin and Hyde (2012, 2014) and Bianchi and Guidolin (2014).