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Equity Volatility Term Premia
Van Tassel, Peter (2018-09-01)
This paper estimates the term-structure of volatility risk premia for the stock market. Realized variance term premia are increasing in systematic risk and predict variance swap returns. Implied volatility term premia are decreasing in risk initially, but then increase at a lag, predicting VIX futures returns. By modeling the logarithm of realized variance, the paper derives a closed-form relationship between the prices of variance swaps and VIX futures. The model provides accurate pricing and highlights periods of dislocation between the index options and VIX futures markets. Term premia ...
Staff Reports , Paper 867

Working Paper
The Term Structure and Inflation Uncertainty
D'Amico, Stefania; Orphanides, Athanasios; Breach , Tomas (2016-12-12)
This paper develops and estimates a Quadratic-Gaussian model of the U.S. term structure that can accommodate the rich dynamics of inflation risk premia over the 1983-2013 period by allowing for time-varying market prices of inflation risk and incorporating survey information on inflation uncertainty in the estimation. The model captures changes in premia over very diverse periods, from the inflation scare episodes of the 1980s, when perceived inflation uncertainty was high, to the more recent episodes of negative premia, when perceived inflation uncertainty has been considerably smaller. A ...
Working Paper Series , Paper WP-2016-22

Working Paper
Sluggish news reactions: A combinatorial approach for synchronizing stock jumps
Bouamara, Nabil; Boudt, Kris; Laurent, Sebastien; Neely, Christopher J. (2024-03-26)
Stock prices often react sluggishly to news, producing gradual jumps and jump delays. Econometricians typically treat these sluggish reactions as microstructure effects and settle for a coarse sampling grid to guard against them. Synchronizing mistimed stock returns on a fine sampling grid allows us to better approximate the true common jumps in related stock prices.
Working Papers , Paper 2024-006

Working Paper
The role of jumps in volatility spillovers in foreign exchange markets: meteor shower and heat waves revisited
Lahaye, Jerome; Neely, Christopher J. (2014-10-01)
This paper extends the previous literature on geographic (heat waves) and intertemporal (meteor showers) foreign exchange volatility transmission to characterize the role of jumps and cross-rate propagation. We employ heterogeneous autoregressive (HAR) models to capture the quasi-long-memory properties of volatility and the Shapley-Owen R2 measure to quantify the contributions of components. We conclude that meteor showers are more influential than heat waves, that jumps play a modest but significant role in volatility transmission and that significant, bidirectional cross-rate volatility ...
Working Papers , Paper 2014-034

Working Paper
Mildly Explosive Dynamics in U.S. Fixed Income Markets
Contessi, Silvio; De Pace, Pierangelo; Guidolin, Massimo (2017-08-01)
We use a recently developed right-tail variation of the Augmented Dickey-Fuller unit root test to identify and date-stamp periods of mildly explosive behavior in the weekly time series of seven U.S. fixed income yield spreads between September 2002 and January 2015. We find statistically significant evidence of such behavior in six of these spreads. Mild explosivity migrates from short-term funding markets to more volatile medium- and long-term markets during the Great Financial Crisis. For some markets, we statistically validate the conjecture, originally suggested by Gorton (2009a,b), that ...
Globalization Institute Working Papers , Paper 324

Working Paper
The Contribution of Jump Activity and Sign to Forecasting Stock Price Volatility
Murphy, Anthony; Hizmeri, Rodrigo; Bu, Ruijun; Tsionas, Mike G.; Izzeldin, Marwan (2020-11-17)
This paper proposes a novel approach to decompose realized jump measures by type of activity (finite/infinite) and by sign. We also provide noise-robust versions of the ABD jump test (Andersen et al. 2007) and realized semivariance measures for use at high frequency sampling intervals. The volatility forecasting exercise involves the use of different types of jumps, forecast horizons, sampling frequencies, calendar and transaction time-based sampling schemes, as well as standard and noise-robust volatility measures. We find that infinite (finite) jumps improve the forecasts at shorter ...
Working Papers , Paper 1902

Working Paper
Regular Variation of Popular GARCH Processes Allowing for Distributional Asymmetry
Prono, Todd (2017-09-22)
Linear GARCH(1,1) and threshold GARCH(1,1) processes are established as regularly varying, meaning their heavy tails are Pareto like, under conditions that allow the innovations from the, respective, processes to be skewed. Skewness is considered a stylized fact for many financial returns assumed to follow GARCH-type processes. The result in this note aids in establishing the asymptotic properties of certain GARCH estimators proposed in the literature.
Finance and Economics Discussion Series , Paper 2017-095

Working Paper
Better the Devil You Know: Improved Forecasts from Imperfect Models
Oh, Dong Hwan; Patton, Andrew J. (2021-11-05)
Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis- specied model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecication of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we nd signicant fore- cast improvements from ...
Finance and Economics Discussion Series , Paper 2021-071

Working Paper
Dynamic Factor Copula Models with Estimated Cluster Assignments
Oh, Dong Hwan; Patton, Andrew J. (2022-05-06)
This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model ...
Finance and Economics Discussion Series , Paper 2021-029r1

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