Signals in the Storm: Predicting Market Shifts with VIX, CVI, MOVE, and BBDXY Indices
| dc.contributor.author | Eeso, Meron | |
| dc.date.accessioned | 2025-10-20T14:40:17Z | |
| dc.date.available | 2025-10-20T14:40:17Z | |
| dc.date.issued | 2025-04-03 | |
| dc.description | This study examines the ability of volatility indices to predict asset returns across equity, bond, cryptocurrency, and currency markets. Using daily data for the VIX, CVI, MOVE, and BBDXY indices, the analysis finds that each index offers meaningful return predictability both within and across markets. Notably, VIX Change Lagged forecasts equity returns, while CVI and MOVE demonstrate significance in multiple markets, and BBDXY Change Lagged provides strong cross-asset signals. The predictive power increases when the indices are combined, with adjusted R² values of 5.16% for SPY and 2.71% for AGG. These results highlight the value of volatility indices as forward-looking market indicators. | |
| dc.description.abstract | This study examines the ability of volatility indices to predict asset returns across equity, bond, cryptocurrency, and currency markets. Using daily data for the VIX, CVI, MOVE, and BBDXY indices, the analysis finds that each index offers meaningful return predictability both within and across markets. Notably, VIX Change Lagged forecasts equity returns, while CVI and MOVE demonstrate significance in multiple markets, and BBDXY Change Lagged provides strong cross-asset signals. The predictive power increases when the indices are combined, with adjusted R² values of 5.16% for SPY and 2.71% for AGG. These results highlight the value of volatility indices as forward-looking market indicators. | |
| dc.description.sponsorship | The Honors College, Oakland University | |
| dc.identifier.citation | Eeso, M. (2025). Signals in the Storm: Predicting Market Shifts with VIX, CVI, MOVE, and BBDXY Indices (Undergraduate honors thesis, Oakland University). | |
| dc.identifier.uri | https://hdl.handle.net/10323/18846 | |
| dc.language.iso | en_US | |
| dc.publisher | Oakland University | |
| dc.rights | Attribution 3.0 United States | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | |
| dc.subject | Volatility Indices | |
| dc.subject | Market Prediction | |
| dc.subject | Asset Returns | |
| dc.subject | VIX | |
| dc.subject | CVI | |
| dc.subject | MOVE | |
| dc.subject | BBDXY | |
| dc.subject | Equity Markets | |
| dc.subject | Bond Markets | |
| dc.subject | Cryptocurrency | |
| dc.subject | Regression Analysis | |
| dc.subject | Time-Series Analysis | |
| dc.subject | Financial Econometrics | |
| dc.title | Signals in the Storm: Predicting Market Shifts with VIX, CVI, MOVE, and BBDXY Indices | |
| dc.type | Thesis | eng |