Viktor Todorov
Biographic Data
| ID | 8879751 |
|---|---|
| NAME | Viktor Todorov |
| GIVEN NAMES | Viktor |
| FAMILY NAME | Todorov |
| SIGNATURE | TODOROV V |
| AFFILIATIONS | Northwestern University |
| ORCID | 0000-0002-2360-7812 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2006 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Testing the Dimensionality of Policy Shocks
This paper provides a nonparametric test for deciding the dimensionality of a policy shock as manifest in the abnormal change in asset returns’ stochastic covariance matrix, following the release of a macroeconomic announcement. We use high-frequency data in local windows before and after the event to estimate the covariance jump matrix and then test its rank. We find a one-factor structure in the covariance jump matrix of the yield curve resulti…
Information gains from using short‐dated options for measuring and forecasting volatility
We study the gains from using short‐dated options for volatility measurement and forecasting. Using option portfolios, we estimate nonparametrically spot volatility under weak assumptions for the underlying asset. This volatility estimator complements existing ones constructed from high‐frequency returns. We show empirically, using the market index and Dow 30 stocks, that combining optimally return and option data can lead to nontrivial gains for…
The Pricing of Tail Risk and the Equity Premium
We explore the pricing of tail risk as manifest in index options across international equity markets. The risk premium associated with negative tail events displays persistent shifts, unrelated to volatility. This tail risk premium is a potent predictor of future returns for all the indices, while the option-implied volatility only forecasts the future return variation. Hence, compensation for negative jump risk is the primary driver of the equit…
Rank Tests at Jump Events
We propose a test for the rank of a cross-section of processes at a set of jump events. The jump events are either specific known times or are random and associated with jumps of some process. The test is formed from discretely sampled data on a fixed time interval with asymptotically shrinking mesh. In the first step, we form nonparametric estimates of the jump events via thresholding techniques. We then compute the eigenvalues of the outer prod…
Volatility Jumps
The article undertakes a nonparametric analysis of the high-frequency movements in stock market volatility using very finely sampled data on the VIX volatility index compiled from options data by the CBOE. We derive theoretically the link between pathwise properties of the latent spot volatility and the VIX index, such as presence of continuous martingale and/or jumps, and further show how to make statistical inference about them from the observe…
Simulation Methods for Lévy-Driven Continuous-Time Autoregressive Moving Average (Carma) Stochastic Volatility Models
We develop simulation schemes for the new classes of non-Gaussian pure jump Lévy processes for stochastic volatility. We write the price and volatility processes as integrals against a vector Lévy process, which makes series approximation methods directly applicable. These methods entail simulation of the Lévy increments and formation of weighted sums of the increments; they do not require a closed-form expression for a tail mass function or spec…
No prominent works on this page.
Simulation Methods for Lévy-Driven Continuous-Time Autoregressive Moving Average (Carma) Stochastic Volatility Models
We develop simulation schemes for the new classes of non-Gaussian pure jump Lévy processes for stochastic volatility. We write the price and volatility processes as integrals against a vector Lévy process, which makes series approximation methods directly applicable. These methods entail simulation of the Lévy increments and formation of weighted sums of the increments; they do not require a closed-form expression for a tail mass function or spec…
Volatility Jumps
The article undertakes a nonparametric analysis of the high-frequency movements in stock market volatility using very finely sampled data on the VIX volatility index compiled from options data by the CBOE. We derive theoretically the link between pathwise properties of the latent spot volatility and the VIX index, such as presence of continuous martingale and/or jumps, and further show how to make statistical inference about them from the observe…
Rank Tests at Jump Events
We propose a test for the rank of a cross-section of processes at a set of jump events. The jump events are either specific known times or are random and associated with jumps of some process. The test is formed from discretely sampled data on a fixed time interval with asymptotically shrinking mesh. In the first step, we form nonparametric estimates of the jump events via thresholding techniques. We then compute the eigenvalues of the outer prod…
The Pricing of Tail Risk and the Equity Premium
We explore the pricing of tail risk as manifest in index options across international equity markets. The risk premium associated with negative tail events displays persistent shifts, unrelated to volatility. This tail risk premium is a potent predictor of future returns for all the indices, while the option-implied volatility only forecasts the future return variation. Hence, compensation for negative jump risk is the primary driver of the equit…
Information gains from using short‐dated options for measuring and forecasting volatility
We study the gains from using short‐dated options for volatility measurement and forecasting. Using option portfolios, we estimate nonparametrically spot volatility under weak assumptions for the underlying asset. This volatility estimator complements existing ones constructed from high‐frequency returns. We show empirically, using the market index and Dow 30 stocks, that combining optimally return and option data can lead to nontrivial gains for…
Testing the Dimensionality of Policy Shocks
This paper provides a nonparametric test for deciding the dimensionality of a policy shock as manifest in the abnormal change in asset returns’ stochastic covariance matrix, following the release of a macroeconomic announcement. We use high-frequency data in local windows before and after the event to estimate the covariance jump matrix and then test its rank. We find a one-factor structure in the covariance jump matrix of the yield curve resulti…
Econometrics (6 works) · Mathematics (5 works) · Stochastic processes and financial applications (5 works) · Economics (4 works) · Jump (4 works) · Statistics (4 works) · Financial Risk and Volatility Modeling (3 works) · Nonparametric statistics (3 works) · Stochastic volatility (3 works) · Financial economics (2 works)