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- W2807313568 abstract "This thesis deals with three separate problems in �nance related to covariance. First, Iassess the forecasting performance of popular multivariate GARCH, hybrid implied andrealised covariance models in terms of statistical and economic criteria. I perform a rigorousanalysis across major equity indices using di�erent forecasting horizons, market regimes,loss functions and tests. A Vector Heterogeneous Autoregressive speci�cation is the bestamong competing models. Less complex models that rely on high-frequency data yieldsuperior forecasts and reduce the portfolio risk. Hybrid estimators that combine optionimpliedand high-frequency information also have merit when option-implied volatilities arecorrected for the volatility risk-premium. During �nancial turmoil the ranking does notchange signi�cantly but forecast accuracy deteriorates.Second, I investigate comovement in investor attention as a determinant of excess stockmarket comovement proposing a novel proxy, co-attention. Co-attention is estimated asthe correlation in demand for market-wide information across stock markets approximatedby the Google Search Volume Index (SVI). My results reveal signi�cant co-attention drivento some extent by correlated news and fundamentals. Most importantly, I �nd that coattentionis positively related to excess comovement. This e�ect is more pronounced indeveloped economies and during recessions. I fail to document signi�cant e�ects of correlatednews supply on stock markets, lending support to the idea that information demandgoverns investing decisions. Co-attention is not only induced through international investors,but domestic investors as well. My results provide evidence of attention-induced �nancialcontagion in unrelated economies. However, international investors' co-attention appears tofacilitate volatility transmission indirectly across markets.Third, I solve the optimal budget allocation problem across keywords for paid search adivvvertising accounting for the risk induced by maintaining a portfolio of volatile and correlatedkeywords. In a mean-variance context, I maximise the growth rates in keyword popularities.Advertising costs and conversion rates are shown to be irrelevant. I demonstrate practicalimplementation using readily available data from Google Trends database estimating averages,variances and co-variances as growth rates in SVIs. Based on keyword sets for majorsectors, I form e�cient frontiers consisting of optimal combinations of keywords. Optimalkeyword portfolios o�er statistically higher risk-adjusted performance against portfolios constructedusing popular heuristics. A proposed heuristic based on risk-adjusted performancereduces the computational cost and provides competing results." @default.
- W2807313568 created "2018-06-13" @default.
- W2807313568 creator A5049032926 @default.
- W2807313568 date "2017-07-01" @default.
- W2807313568 modified "2023-09-26" @default.
- W2807313568 title "Stock market correlation and investor attention" @default.
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