Trend-following in high and low rate regimes

We know that trend-following can add to a portfolio mix of equity and bonds even with bonds being a diversifier; however, there is a question on whether trend-following will add value when rates are high. We know that bond volatility will be higher when rates are higher, but if you can get a higher rate, it provides a hurdle that the trend manager must overcome. For the trend-following to work it must be better than the bond rate and bond returns. The folks at Quantica Capital have another take on this issue in their piece "The Additional Benefits of Trend-following when rates are high". They find that at high rates, the correlation between stocks and bonds increase. If that is the case, there will be a good reason to give a higher allocation to the trend-following diversifier. 

The study focuses on three charts. The first chart sets the stage through showing the return, risk and correlation for stocks and bonds and shows why you should diversify over these two assets. The next figure shows trend-following return and risk in low and high-rate environments as well as the correlation between stocks, bonds and trend-following. Note that trend-following is more volatile in high-rate environments. It is also has slightly lower returns, but the correlation is very different. Trend-following is less correlated to stocks and bonds in high-rate environments, so there is greater benefit for trend-following in a portfolio. 

If rates are above average, then a simple optimizer will show that you should have on average an increase to trend-following of almost 3x versus a low-rate environment. This is easy to do and should be considered by many allocators.

Using our experiences is not always good

 

Follow our experience because as we gain more experience, we become better at making decision. Our wisdom comes from our experience. The Myth of Experience: Why we learn the wrong lessons and ways to correct them by Emre Soyer and Robin Hogarth is another take on behavioral mistakes and the problems of psychology on our decision-making. Their conclusion is that we often take-away the wrong conclusions from our experiences. Experiences that are no assess and filtered will give you the wrong answers. More experiences with the wrong assessment will make you a worse decision-maker. we use experience through linking our actions with results, but if there is not close link between the two, we will find a connection that is often wrong. The authors start with a great example. Learned people used bloodletting for centuries because they thought it worked. You bleed as a cure for a sickness and survive. It must have been the bloodletting that worked. 

We often forget or don't think about what is missing from our experiences. We do not account for the irrelevant. 

Robin Hogarth recently died, and this was one of his last books. He was one of the great researchers on decision-making and human behavior. Tversky and Kahneman have received most of the attention in this area, but Hogarth was a critical researcher in this area one the last 50 years.

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.