Indirect and direct forecasting of volatility-timing portfolios
研究比较了间接（预测波动率）和直接（预测权重）两种方法在构建最小方差组合时的表现，发现间接方法在美国45只大股票数据上获得更高的夏普比率，且结果稳健。
Recent studies have challenged the usefulness of variance–covariance matrix forecasting for the purpose of minimum-variance portfolio construction, instead advocating for the direct forecasting of realized weights. This study examines the value of this direct approach when dimension reduction is handled in the portfolio construction problem via popular volatility timing strategies. Using empirical data from the 45 largest U.S. stocks, this paper reveals that the traditional indirect approach, which relies on volatility forecasts, consistently delivers higher out-of-sample portfolio Sharpe ratios. This finding is robust to random portfolio selection, forecasting horizons, and transaction costs. The results demonstrate the continued usefulness of volatility forecasting models in portfolio construction. • Indirect and direct forecasting of portfolio weights. • The dimensionality issue is addressed by using a diagonal covariance matrix. • The indirect volatility-timing strategy outperforms its direct counterpart.