What is he best known for?
The fields of study he is best known for:
- Statistics
- Econometrics
- Finance
Francis X. Diebold mainly investigates Econometrics, Volatility, Exchange rate, Realized variance and Financial economics.
His research investigates the link between Econometrics and topics such as Statistics that cross with problems in Variation.
His work deals with themes such as Capital asset pricing model and Actuarial science, which intersect with Volatility.
His Exchange rate research incorporates themes from Price discovery, Heteroscedasticity, Martingale and Autocorrelation.
Francis X. Diebold has researched Realized variance in several fields, including Stock market index and Joint probability distribution.
His study in Financial economics is interdisciplinary in nature, drawing from both Purchasing power parity, Relative purchasing power parity, Interest rate parity and International finance.
His most cited work include:
- Comparing Predictive Accuracy (5344 citations)
- MODELING AND FORECASTING REALIZED VOLATILITY (2478 citations)
- The distribution of realized stock return volatility (1660 citations)
What are the main themes of his work throughout his whole career to date?
Francis X. Diebold mainly focuses on Econometrics, Volatility, Business cycle, Financial economics and Exchange rate.
His biological study spans a wide range of topics, including Yield curve and Univariate, Multivariate statistics.
His Implied volatility study in the realm of Volatility connects with subjects such as Financial econometrics.
His study explores the link between Implied volatility and topics such as Volatility smile that cross with problems in Volatility swap.
His studies deal with areas such as Index and Recession as well as Business cycle.
Francis X. Diebold is interested in Capital asset pricing model, which is a field of Financial economics.
He most often published in these fields:
- Econometrics (75.44%)
- Volatility (39.88%)
- Business cycle (13.75%)
What were the highlights of his more recent work (between 2010-2021)?
- Econometrics (75.44%)
- Social connectedness (5.11%)
- Volatility (39.88%)
In recent papers he was focusing on the following fields of study:
Francis X. Diebold focuses on Econometrics, Social connectedness, Volatility, Model selection and Recession.
His Stochastic volatility study in the realm of Econometrics interacts with subjects such as Perspective.
While the research belongs to areas of Volatility, Francis X. Diebold spends his time largely on the problem of Linear model, intersecting his research to questions surrounding Range.
Francis X. Diebold works mostly in the field of Model selection, limiting it down to topics relating to Regularization and, in certain cases, Survey of Professional Forecasters.
His research on Recession also deals with topics like
- Index which is related to area like Keynesian economics and Monetary economics,
- Business cycle that intertwine with fields like Globalization.
The study incorporates disciplines such as Monetary policy and Debt in addition to Financial economics.
Between 2010 and 2021, his most popular works were:
- Better to give than to receive: Predictive directional measurement of volatility spillovers (1077 citations)
- Better to give than to receive: Predictive directional measurement of volatility spillovers (1077 citations)
- On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms (802 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Finance
- Econometrics
The scientist’s investigation covers issues in Econometrics, Social connectedness, Perspective, Systemic risk and Big data.
His Econometrics research focuses on Volatility in particular.
The study incorporates disciplines such as Market risk and Risk management in addition to Systemic risk.
The Financial risk management research Francis X. Diebold does as part of his general Risk management study is frequently linked to other disciplines of science, such as Financial econometrics, therefore creating a link between diverse domains of science.
His Big data research is multidisciplinary, relying on both Management, Positive economics and Data science.
He has researched Financial crisis in several fields, including Vector autoregression and Equity.
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