Forecasting

In addition to assessing the relationship between the factor for idiosyncratic volatility and market volatility, we also explore what, if any, impact the factor for volatility has on volatility forecasting. In addition to the three models we presented in Section 1.3.5, we include two additional benchmark models:

1. BMK - The benchmark model where only the factor has time-varying volatility (con- stant idiosyncratic volatility). Jacquier et al. (1994) proposed a Stochastic Volatility version of this model, though they did not estimate it. Diebold and Nerlove (1989) proposed and estimated a similar model, where the factor volatility is an ARCH pro-

cess.

2. AR - In addition to time-varying volatility in the factor, idiosyncratic volatility is also time-varying, but they vary as independent autoregressions. Kim et al. (1998) pro- posed this multivariate stochastic volatility model, though Pitt and Shephard (1999) and Aguilar and West (2000) independently (and with different MCMC techniques) actually produced estimation procedures.

In order to estimate our three Factor for Idiosyncratic Volatility models, we proceed in one of the following ways:

• For model 1 of 3 (FVOL MKT), we regress each of the log- diagonal vector ofΩvt,σeit, againstlog(σf_t)to estimate βi.

• For model 2 of 3 (FVOL2), regresslog(σ_ei

t)againstlog(σFt), and conduct PCA on the panel of residuals.

• For model 3 of 3 (FVOL PCA), we conduct PCA directly on the panel of log-idiosyncratic volatlitities. σ_ei

t. We then regress the residuals against σFt.

For all datasets we focus on the forecast errors of the panel of variances. Correlations are modeled via loadings from the level regression, which are the same for all models. All models and datasets forecast poorly at the beginning of the financial crisis in 2008, so we report both average Mean Squared Error (MSE) and Median Absolute Error (MAE), where the mean/median is taken across time for each asset and then averaged across assets. We also plot the cumulative squared one-step ahead forecast errors, both for the whole sample and pre- and post-2008 (FX is also plotted pre-2008).

1.6.1 Equities

For both equities datasets, we use a 200 day rolling window estimation period. In each period we estimate each of the five competing models and forecast ahead 1-12 days. Due to the fact that there are some large outliers (even outside the financial crisis), we record both

Average MSE and MAE. The DOW 10 forecasting results are presented in Table 5, while results for the S&P 100 dataset are in Table 6. One-step-ahead cumulative squared forecast errors for both datasets are plotted in Figure 11. To ensure the results are not solely driven by dynamics in the crisis, we also present (in the Appendix) tables of forecasting results and figures with squared forecast errors using forecasts only after January 2009. The DOW 10 forecasting results are in Table 12, while the S&P 100 results are in Table 13. Squared forecast errors for both datasets are plotted in Figure 13.

Table 5: Average Mean Square Error and Median Absolute Error of DOW 10 Rvariances All values are relative to BMK forecasts. Bolded value in each row is the minimum, when better than BMK. BMK is benchmark, AR is with univariate autoregressive idiosyncratic volatility, FVOL MKT uses market volatility as a single idiosyncratic vol factor, FVOL PCA uses a single principal component as an idiosyncratic vol factor, FVOL 2 uses both. All models use a 200-day rolling window to estimate parameters, followed by forecasts for 1-12 days ahead.

Average MSE Average MAE

h AR FVOL FVOL FVOL2 AR FVOL FVOL FVOL2

MKT PCA MKT PCA 1 0.82 1.01 1.03 1.30 0.83 0.89 0.82 0.83 2 0.86 0.99 0.98 1.11 0.88 0.90 0.83 0.85 3 0.90 1.00 1.02 1.08 0.90 0.91 0.85 0.87 4 0.92 0.99 0.99 1.05 0.90 0.90 0.85 0.88 5 0.93 1.00 1.05 1.06 0.91 0.91 0.85 0.88 6 0.94 1.01 1.03 1.05 0.92 0.92 0.86 0.90 7 0.94 1.02 1.05 1.03 0.94 0.94 0.87 0.91 8 0.95 1.01 1.02 1.02 0.94 0.94 0.86 0.91 9 0.94 1.02 1.03 1.00 0.94 0.93 0.86 0.91 10 0.94 1.01 1.01 1.00 0.94 0.94 0.87 0.92 11 0.95 1.01 1.02 1.00 0.94 0.94 0.88 0.93 12 0.95 1.01 1.00 1.00 0.95 0.94 0.88 0.94

First focus on the DOW 10 dataset in Table 5. By average MSE, all FVOL models forecast variances about as well, though FVOL 2 does slightly worse than the others at short horizons. In addition, the model of Pitt and Shephard (1999) (AR) does very well, clearly supporting the hypothesis that idiosyncratic variance is at least time-varying. Despite the FVOL models not performing particularly well, their worse performance is mainly centered around the financial crisis, specifically around late 2008. When we look at average MAE instead of MSE, we see that all models provide substantial forecasting improvements as compared to

Table 6: Average Mean Square Error and Median Absolute Error of S&P 100 Rvariances All values are relative to BMK forecasts. Bolded value in each row is the minimum, when better than BMK. BMK is benchmark, AR is with univariate autoregressive idiosyncratic volatility, FVOL MKT uses market volatility as a single idiosyncratic vol factor, FVOL PCA uses a single principal component as an idiosyncratic vol factor, FVOL 2 uses both. All models use a 200-day rolling window to estimate parameters, followed by forecasts for 1-12 days ahead.

Average MSE Average MAE

h AR FVOL FVOL FVOL2 AR FVOL FVOL FVOL2

MKT PCA MKT PCA 1 1.06 1.04 0.97 18.18 0.70 0.79 0.70 0.70 2 1.17 1.05 0.98 27.19 0.76 0.83 0.74 0.74 3 1.18 1.07 0.99 9.77 0.79 0.84 0.75 0.77 4 1.34 1.09 0.98 15.11 0.81 0.85 0.75 0.77 5 1.16 1.08 0.98 9.49 0.83 0.86 0.76 0.79 6 1.29 1.04 0.99 7.58 0.85 0.87 0.77 0.81 7 1.22 1.07 1.00 10.56 0.86 0.87 0.78 0.82 8 1.18 1.05 0.99 4.61 0.87 0.89 0.79 0.84 9 1.24 1.11 0.99 3.97 0.88 0.89 0.79 0.84 10 1.28 1.12 0.99 3.23 0.89 0.90 0.79 0.84 11 1.26 1.13 0.99 2.49 0.89 0.91 0.79 0.86 12 1.24 1.16 0.99 1.36 0.90 0.91 0.81 0.87

Figure 11: Equities Squared One-Step Prediction Errors

Cumulative squared errors over time, 2007-2015, of DOW 10 and S&P100. Each date adds the average squared distance of true volatility to forecasted volatility over the panel. The models perform similarly outside the financial crisis 2008-2010, but there the discrepancies are large.

(a) DOW10 (b) S&P 100

the benchmark model. The Pitt and Shephard (1999) (AR) model still performs about as well, but introducing some sort of factor on idiosyncratic volatility also performs comparably well with much fewer estimated parameters. Specifically, using a PCA factor to forecast idiosyncratic volatility works best at all horizons.

In the larger, S&P 100, sample, the results are qualitatively similar. Once again, all models perform very similarly when compared via average MSE. This time though, the AR model slightly underperforms the benchmark, the PCA factor slightly outperforms the benchmark, and the FVOL 2 model performs substantially worse. Once again though, the forecasting deficiencies are mainly due to the financial crisis, and by using average MAE, all FVOL models see large improvements over the benchmark model. Once again, the AR model performs very well, but this time both FVOL PCA and FVOL2 do even better. The FVOL MKT once again underperforms the other models, but still beats the benchmark.

Taken together, as the panel of volatilities grows in cross-sectional dimension, the improve- ments of using FVOL models increases. While using both the market volatility (model 1) and the PCA factor are each helpful, the PCA factor is better for forecasting. This reaffirms the traditional “Blessing of Dimensionality" in factor models - that when dimensions grow, there are increasingly large benefits to fitting factor models rather than attempting to model each series individually.

1.6.2 Exchange Rates

We use the same set of competing models to predict FX monthly volatilities, but this time use a rolling window of 50 months. Once again, we report both average MSE and MAE prediction error, as forecast errors are non-gaussian. The table with forecasting performance is in Table 7 while the plot of squared prediction error is in Figure 12. We also include figures of squared prediction error for pre-August 2008 and post January 2009 in the Appendix (Figure 14), and forecasting results only post-2009 (Table 14).

Once again, when compared via MSE, most models do not make much of an improvement over the benchmark, if any at all. The FVOL MKT model performs slightly better at horizon 1, though worse at all other horizons. FVOL PCA performs best at horizon 2 and 3, but overall they both underperform the benchmark.

Table 7: Average Mean Square Error and Median Absolute Error of FX rate Rvariances All values are relative to BMK forecasts. Bolded value in each row is the minimum, when better than BMK. BMK is benchmark, AR is with univariate autoregressive idiosyncratic volatility, FVOL MKT uses market volatility as a single idiosyncratic vol factor, FVOL PCA uses a single principal component as an idiosyncratic vol factor, FVOL 2 uses both. For all models, we use a 50-month rolling window where we estimate the model in every window and then forecast for 1-12 months ahead.

Average MSE Average MAE

h AR FVOL FVOL FVOL2 AR FVOL FVOL FVOL2

MKT PCA MKT PCA 1 0.98 0.97 1.22 2.47 0.83 0.93 0.81 0.83 2 1.13 1.01 0.98 1.18 0.86 0.96 0.85 0.88 3 1.16 1.02 0.98 1.69 0.91 1.00 0.88 0.92 4 1.10 1.03 1.17 2.47 0.94 0.98 0.87 0.94 5 1.08 1.03 37.84 2.40 0.96 1.00 0.92 0.96 6 1.11 1.06 4.44 15.41 0.99 1.02 0.95 1.01 7 1.06 1.11 1.42 25.39 0.97 0.99 0.95 1.00 8 1.03 1.03 4.20 1.53 0.98 1.01 0.94 1.03 9 1.02 1.05 1.60 1.21 0.97 1.00 0.95 0.99 10 1.03 1.04 1.32 1.19 0.97 1.01 0.95 1.01 11 1.03 1.02 1.01 1.06 0.95 0.97 0.88 0.96 12 1.04 1.02 1.00 1.02 0.98 0.99 0.92 0.97

Figure 12: FX squared One-Step prediction errors

Cumulative squared errors over time, 2007-2015, of the panel of exchange rate volatilities. Each date adds the average squared distance of true volatility to forecasted volatility over the panel. The models perform similarly outside the financial crisis 2008-2010, but there the discrepancies are large.

has a large impact on improving forecasts. All FVOL models perform much better (10-20%) than the benchmark, especially at short horizons. Similar to equities, the FVOL PCA model performs best at all horizons.