Bibliographic record
Abstract
This thesis, entitled Essays on Financial Economics and Macroeconomics, studies the interactions between real macroeconomics and financial variables. There is an emerging literature aims to investigate how can we reduce the impacts from the financial crisis by considering both macroeconomics and finance conditions together. For example, decision-makers should consider the financial market conditions first before policies are made. Meanwhile, the forecasting of short term financial variables' returns should take long term macroeconomic conditions into consideration. This has motivated us to explore further in the relationship between the macroeconomic factors and financial market conditions. In the first chapter, we examine the short-run and long-run dynamics of the correlation between exchange rate and commodity returns, and assess the extent to which the long-run correlation is determined by economic fundamentals. Our empirical analysis is based on the dynamic conditional correlation model with mixed data sampling (DCC-MIDAS) of Colacito, Engle and Ghysels (2011). This model provides a framework that captures the high-frequency relation between exchange rate and commodity returns as well as the low-frequency relation of volatility and correlation to economic fundamentals. Using both economic and statistical criteria, we find that the DCC-MIDAS\\ model augmented with economic fundamentals performs better than competing models in sample and out of sample. In the second chapter, we investigate the direction of Granger causality between business and financial cycles. Our analysis is based on a vector autoregression model applied on mixed frequency data. This allows us to condition on data from higher frequency variables (such as monthly industrial production) and lower frequency variables (such as quarterly aggregate credit) in a way that avoids the effects on data aggregation. Our empirical investigation focuses on five industrialized countries: USA, Canada, UK, Germany and Japan. Firstly, we examine whether the monthly industrial production index causes quarterly aggregate credit or vice versa. Then, we determine the timing of when causality is statistically significant. We find that there is strong bidirectional causality between business and financial cycles. The timing of causality varies across countries, but for all countries, bidirectional causality is significant during the financial crisis. The third and final chapter, which is an extension of the second chapter, investigates the role of the US as a global leader. Specifically, by paring US with other country (i.e, Canada, UK, Germany and Japan), we examine whether the US industrial production or credit causes the industrial production or credit of the other countries. In addition, we investigate whether causality is affected by the nominal interest rate. Our main finding is that the US business cycle strongly causes the business cycles of Canada, the UK and Germany. Finally, there is strong evidence that causality tends to be significant when the US interest rate is higher.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".