Multivariate Volatility Forecast Evaluation

The evaluation of volatility models is gracefully complicated by the fact that, unlike other time series, even the realization is not observable. Two researchers would never disagree about what was yesterday’s stock price, but they can easily disagree about what was yesterday’s stock volatility. Because we don’t observe volatility directly, each of us uses own proxy of choice. There are many ways to skin this cat (more on volatility proxy here).

In a previous post Univariate volatility forecast evaluation we considered common ways in which we can evaluate how good is our volatility model, dealing with one time-series at a time. But how do we evaluate, or compare two models in a multivariate settings, with two covariance matrices?

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Fed Fund Rate futures curve and what they tell us

“The Fed is certainly moving forward with plans to normalize interest rates.” We keep on hearing that, we believed it in the past and we believe it now. We believe that the Fed believes and that, in fact, this means something.

Should we become more suspicious and less trusting given history? Let’s take a look.

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