What is he best known for?
The fields of study he is best known for:
- Statistics
- Finance
- Econometrics
His primary scientific interests are in Econometrics, Portfolio, Asset, Bond and Predictability.
Massimo Guidolin works on Econometrics which deals in particular with Autoregressive conditional heteroskedasticity.
As a part of the same scientific family, Massimo Guidolin mostly works in the field of Portfolio, focusing on Capital asset pricing model and, on occasion, Kurtosis.
The study incorporates disciplines such as Event study, Stock market index, Sample and Commodity in addition to Asset.
The Predictability study combines topics in areas such as Vector autoregression, Linear model, Stylized fact and Moneyness.
His research integrates issues of Welfare, Investment and Return distribution in his study of Asset allocation.
His most cited work include:
- Asset allocation under multivariate regime switching (254 citations)
- Asset allocation under multivariate regime switching (254 citations)
- International Asset Allocation under Regime Switching, Skew and Kurtosis Preferences ∗ (252 citations)
What are the main themes of his work throughout his whole career to date?
Econometrics, Portfolio, Predictability, Financial economics and Bond are his primary areas of study.
Massimo Guidolin combines subjects such as Equity, Markov chain and Asset allocation with his study of Econometrics.
Massimo Guidolin has researched Portfolio in several fields, including Investment, Asset, Stock market and Risk aversion.
The concepts of his Asset study are interwoven with issues in Market liquidity and Expected utility hypothesis.
In his research on the topic of Predictability, Index and Black–Scholes model is strongly related with Implied volatility.
His Bond study combines topics in areas such as Real estate investment trust, Real estate and Monetary policy, Monetary economics.
He most often published in these fields:
- Econometrics (69.59%)
- Portfolio (37.62%)
- Predictability (26.02%)
What were the highlights of his more recent work (between 2016-2021)?
- Econometrics (69.59%)
- Monetary economics (12.54%)
- Portfolio (37.62%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Econometrics, Monetary economics, Portfolio, Capital asset pricing model and Volatility.
His Econometrics study combines topics in areas such as Sharpe ratio, Asset allocation, Predictability and Markov chain.
His research integrates issues of Asset and Risk aversion in his study of Asset allocation.
He has included themes like Bond and Yield in his Monetary economics study.
In his work, Arbitrage is strongly intertwined with Stock market, which is a subfield of Portfolio.
His study on Capital asset pricing model also encompasses disciplines like
- Financial market and Currency most often made with reference to Equity,
- Risk premium together with Bayes estimator.
Between 2016 and 2021, his most popular works were:
- Modeling systemic risk with Markov switching graphical SUR models (25 citations)
- Linear and nonlinear predictability in investment style factors: multivariate evidence (19 citations)
- Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Finance
- Econometrics
Massimo Guidolin mainly investigates Econometrics, Markov chain, Portfolio, Volatility and Capital asset pricing model.
His work carried out in the field of Econometrics brings together such families of science as Transaction cost and Predictability.
Massimo Guidolin has researched Portfolio in several fields, including Covariance and Wishart distribution.
His biological study spans a wide range of topics, including Index and Post-modern portfolio theory.
His studies in Asset allocation integrate themes in fields like Asset and Stock market.
His work in Bond addresses subjects such as Maturity, which are connected to disciplines such as Financial economics.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
