Volatility is a driver for financial crises (Minsky low volatility)

Volatility is a key driver and indicator for financial crises. This volatility prediction is not what you may expect. It is known that during a financial crisis volatility will surge higher, but what is critical for determining whether there will be a crisis is the past volatility. 

What has been found is that a period of low volatility or calm markets will lead to future financial disruptions. This can be viewed as a verification of the Minsky instability hypothesis. See "Learning from History: Volatility and Financial Crises".

You could call this the "volatility paradox", low volatility will increase the chance of systemic event.  If there is prolonged low volatility, there will a higher likelihood of a banking crisis. Form a low volatility regime, there will be excessive credit build-ups and higher balance sheet leverage. You feel like there is less risk and you will then take on more leverage. This work finds that "stability is destabilizing". 

Given the long history studied and the long lag periods, it is hard to use low volatility as a trading signal for short-term shocks, but this volatility relationship is important when thinking about long-term crisis risks. Low volatility will cause investors to take bigger risks. The costs of these risks will have to be borne by someone. 

Using the TIMEMIXER approach for volatility forecasting

An application of time mixing for volatility forecasting can be an important advancement for risk management. Research has extended the work on GARCH to an extreme, but there may be other techniques in time series forecasting that can be applied to financial time series that may be very useful. A recent paper focused on TimeMixers which employs different time scales as a method to improve forecasts. See "Volatility Forecasting in Global Financial Markets Using TimeMixer".

The idea behind TimeMixers is straight-forward. There is imbedded in any times series relationship with different timeframes that can exploited. There can be long-term seasonality. There can be cycles or trends that are longer than a few days that will not be captured with daily data. Classic time series in ARMA models can handle seasonality and can identify autocorrelation at different lengths, but a more explicit breakdown of data may improve forecasts. 

I like the technique used and the author applied it to a broad set of markets, but I was disappointed that there was no testing against other types of models for volatility. This process looks interesting but it is not clear it is any better than what we already have. The MAE, MSE, and RMSE all are low especially for short-term forecasts, but the quality of technique must be balanced with the results, the ease of understanding, and the ease of implementation. This paper does not make that strong relative case for TimeMixer ML. 

That sinking feeling in the CMBS market

 

Sometimes you get hit with a wave, but often you drown because the water level just gets too high and you don't have the energy to find the shore, or worse, there is no shore. There is a problem in the CMBS problem with some very good properties underwater with the sinking still occurring. We can start with the issue of running a CMBS on a single property. There is no diversification. Investors may expect that lower rated tranches will have more credit risk, but in these cases, we are seeing risk with the A tranche. Ouch. This is what is supposed to be avoiding with structuring. A combination of poor engineering with a bad environment spells disaster for investors. 

Can there be money to be made? Yes, however, it is not from dropping these bonds in a portfolio and cutting some coupons. This is risky bond trading. 

TimeMixers as a new time series tool

 

Many trend-followers are time mixers. What has been old for some is now something new. A new technique in machine learning time series forecasting that is being given more attention is a concept called time mixing. The idea of time mixing is simple. Decompose any time series into a set of different time scales. For example, there can be short-term behavior, longer-term, seasonal and very long-term behavior in prices. This breakdown is certainly true for almost all commodities. If we can take the multi-scale data, we can break the problem into two parts or blocks:  the past-decomposable-Mixing block PDM where the information at different scales is learned by the model. From the PDM, the model is sent to the Future-Multipredictor-MIxing (FMM) block which will combine the scaled information into a forecast. This type of model may not do well for very short-term predictions, but longer-term forecasts can be improved by this technique. Given this is a ML technique, the value is found in improved forecasting. This is not a structural model of how the market behaves.

What do many trend-followers do? They will often use trends for different scales or look-backs to blend into a single signal. The weighting of these signals can be a very simple, like an equal-weighted scale, or it can be optimized based on preferences or on past forecasting success. This is not the sample as a formal time mixer model, but the idea behind this approach is the same. Use different time scales to capture the different behaviors in asset time series.