Katja Ahoniemi

Joint modeling of call and put implied volatility

This paper exploits the fact that implied volatilities calculated from identical call and put options have often been empirically found to differ, although they should be equal in theory. We propose a new bivariate mixture multiplicative error model and show that it is a good fit to Nikkei 225 index call and put option implied volatility (IV). A good model fit requires two mixture components in the model, allowing for different mean equations and error distributions for calmer and more volatile days. Forecast evaluation indicates that, in addition to jointly modeling the time series of call and put IV, cross effects should be added to the model: put-side implied volatility helps forecast call-side IV, and vice versa. Impulse response functions show that the IV derived from put options recovers faster from shocks, and the effect of shocks lasts for up to six weeks.

Modeling and Forecasting the VIX Index

This paper models the implied volatility of the S&P 500 index, with the aim of producing useful forecasts for option traders. Numerous time-series models of the VIX index are estimated, and daily out-of-sample forecasts are calculated from all relevant models. The directional accuracy of the forecasts is evaluated with market-timing tests. Option trades are simulated based on the forecasts, and their profitability is also used to rank the models. The results indicate that an ARIMA (1,1,1) model enhanced with exogenous regressors has predictive power regarding the directional change in the VIX index. GARCH terms are statistically significant, but do not improve forecasts. The best models predict the direction of change correctly for over 60 percent of the trading days. Out-of-sample option trading over a period of fifteen months yields positive returns when the forecasts from the best models are used as the basis for investment decisions.

Overnight Stock Returns and Realized Volatility

The information flow in modern financial markets is continuous, but major stock exchanges are open for trading for only a limited number of hours. No consensus has emerged on how to deal with overnight returns when calculating and forecasting realized volatility in markets where trading does not take place 24 hours a day. Based on a recently introduced formal testing procedure, we find that for the S&P 500 index, a realized volatility estimator that optimally incorporates overnight information is more accurate in-sample. In contrast, estimators that do not incorporate overnight information are more accurate for individual stocks. We also show that accounting for overnight returns may a ffect the conclusions drawn in an out-of-sample horserace of forecasting models. Finally, there is considerably less variation in the selection of the best out-of-sample forecasting model when only the most accurate in-sample RV estimators are considered.

Realized Volatility and Overnight Returns

No consensus has emerged on how to deal with overnight returns when calculating realized volatility in markets where trading does not take place 24 hours a day. This paper explores several common volatility applications, investigating how the chosen treatment of overnight returns affects the results. For example, the selection of the best volatility forecasting model depends on the way overnight returns are incorporated into realized volatility. The evidence favours weighted estimators over those that have been more commonly used in the existing literature. The definition of overnight returns is particularly challenging for the S&P 500 index, and we propose two alternative measures for its overnight return.?

Flows, Price Pressure, and Hedge Fund Returns

We study how capital flows affect hedge fund returns. The contemporaneous relation is positive: funds with high flows outperform funds with low flows during the month of the flows. This immediate reaction, combined with feedback trading, gives rise to a cycle: flows exert price pressure, this effect on returns induces more flows, and these flows cause further price pressure. The cycle is so strong that it takes almost two years before a full return reversal is witnessed. This flow-return cycle also contributes to the observed persistence in hedge fund performance. The impact of flows on returns also has implications for performance evaluation: roughly one third of the estimated hedge fund alphas are due to flows.

Time-Varying Mixture Multiplicative Error Models for Implied Volatility

In this paper, we incorporate time-varying mixing probabilities into univariate and bivariate mixture multiplicative error models. Switching between the regimes is governed by an observable predetermined variable. The models are applicable to positive-valued time series, and are particularly well-suited for different financial volatility measures. The flexibility afforded by non-constant regime probabilities facilitates capturing the high persistence in financial volatility regimes, as well as time-varying volatility of volatility. We apply the new models to the implied volatilities of call and put options on the USD/EUR exchange rate, using the lagged daily exchange rate return as the regime indicator. In one-step-ahead forecasting, both mean squared errors and directional accuracy improve when allowing for time-varying mixing probabilities. Further improvements are brought about by employing a bivariate instead of a univariate model.