### Table 3 Implied Volatility as Proxy for the Instantaneous Volatility

"... In PAGE 23: ...2.2 Exchange Rate Table3 veri#0Ces that the one-week implied volatility is a reasonable proxy for the instantaneous volatility of the exchange rate. It tests whether the true volatility of the exchange rate, denoted by h t , is constant #28h t = h 0 , model B#29, proportional to the implied volatility#28h t = h 1 v t , model C#29, or linear in the implied volatility#28h t = h 0 + h 1 v t , model D#29.... In PAGE 24: ... These apparent inconsistencies between the data and the theory could be due to noise in the volatility proxy or estimation error in the bound. Overall though, the results in Table3 and Figure 3 support our use of the one-week implied volatilityasaproxy for the instantaneous volatility of the exchange rate. The drift of the log exchange rate depends on the market price of pure currency risk #20, whichwe estimate to be 0.... ..."

### Table 5 Stochastic Volatility Models

"... In PAGE 26: ...etween 5.7 and 14.4 percent in our sample. Our choice of a linear drift and a square-root di#0Busion function for the dynamics of the exchange rate volatility is admittedly ad-hoc. Therefore, Table5 tests this speci#0Ccation #28Model A#29 against a number of alternatives. Model B has a linear drift and a constant elasticity of variance #28CEV#29 di#0Busion function p #0C 2 v #0C 3 .... ..."

### Table 3. Posterior Moments for the Stochastic Volatility Model

2001

"... In PAGE 9: ... 3.2 Empirical Results for the Stochastic Volatility Model Figure 4 and Table3 summarize the posterior simula- tions for the stochastic volatility model. Notice that the results in the table correspond to a re-parameterization of the model that improve upon the convergence speed of the algorithm.... ..."

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### Table 4. Sampling Experiments for the Stochastic Volatility Model

2001

"... In PAGE 14: ... Due to the computational demands, we limit the posterior draws to 20,000. Each experiment was repeated 200 times, and the resulting parameter estimates are summarized in Table4 . Also, , which posterior distri- bution requires an extensive number of draws to estimate accurately, is xed to 1 2 in the simulation study.... In PAGE 14: ... For the parameter measuring the reversion in the log-volatility process, z, the estimates are also slightly downward biased, while the estimate of the di usion coe cient for the log-volatility process, z, is virtually unbiased using a sample size of 2000. When interpreting the results in Table4 , it should be kept in mind that the reversion coe cients can be inter- preted as approximately quot;autocorrelation - 1 quot;. This, cou- pled with the well-known fact that sample autocorrelation coe cients are downward biased for processes with near unit roots may explain, at least partially, the direction of the bias in the sampling experiments.... In PAGE 14: ... It does, however, in uence the dispersion of the posterior means, as measured by the RMSE. Hence, the results in Table4 are likely to underestimate the e ciency of the MCMC estimator. Overall, the performance of the MCMC method for es- timating the parameters in the stochastic volatility model is di cult to assess.... ..."

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### Table 3. Diagnostic Tests of the Stochastic Volatility Models

1996

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### Table 2. Simulation results for stochastic volatility model.

### Table 2: Unemployment and Wage Volatility Baseline Model Informational Rent Model

2004

"... In PAGE 23: ...roduct J2)y, which is set to 0.03. The no-screening condition then holds provided that J2 # 0.5605. Table2 shows that informational rents can generate realistic variations in the unemployment rate. Even though the informational rent is only 3% of the productivity level, it moves the unemployment rate by about 40%.... In PAGE 24: ... The nondecreasing rate is given by w1 = (r+*)W1 - 81(W2 - W1), as explained in Section 5. Table2 also shows that even though wages are sticky with respect to cyclical changes in the distribution of the idiosyncratic component of productivity, there is nevertheless substantial cyclical wage variation. Thus although ad hoc sticky wage models have been strongly criticized by Pissarides (2007) on the grounds that they generate too little wage volatility, this criticism does not apply to the informational rents model.... ..."

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### Table 6: Relative Pricing Errors (%) of Alternative Models with Implied Volatility Risk Premium for SV Models and Implied Volatility for Constant Volatility Models Moneyness Days-to-Expiration

"... In PAGE 26: ... This finding is also consistent with the conjecture in Lamoureux amp; Lastrapes (1993) and explains why the implied volatility is an inefficient forecast of the underlying volatility.16 Table6 reports the relative pricing errors (%) for alternative models in terms of option prices. In 16 As suggested by the referee, these results could potentially explain the findings in Lamoureux amp; Lastrapes (1993) if we extract the implied volatility from various models and regress daily realized volatility over the life of the option.... ..."

### Table 4: Memory Estimates for the Helms Data Volatility

"... In PAGE 8: ... For the Helms data, the only exceptions are two of the shorter series, soybeans for March 77 (BO M76) and soybean meal for the same date (SM M76), which display con icting evidence. Results for the extended data set, shown in Table4 , consistently indicate signi cant long-memory behavior. INSERT TABLES 3 AND 4 AROUND HERE 5 A LONG MEMORY STOCHASTIC VOLATILITY MODEL FOR FUTURES DATA Breidt, Crato, and de Lima (1998) introduced the long-memory stochastic volatility (LMSV) model to describe the type of persistent dependence structure observed for the futures data.... ..."