Building international financial conditions indices

April 29, 2017

IMF staff has developed global financial conditions indices for 43 global economies. Conceptually, these indices extract the communal component of range of indicators for local financing conditions, independent of economic conditions. The idea looks like a good basic principle for building FCIs for macro trading strategies. The research on these indices suggests that [1] financial conditions are a warning sign for recessions, and [2] global financial shocks have a powerful impact on local conditions, particularly in the short run and in emerging economies.

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Nowcasting GDP growth

April 8, 2017

Financial markets have long struggled with tracking GDP growth trends in a timely and consistent fashion. However, over the past decade statistical methods for “nowcasting” various economies have improved considerably, benefiting macro trading strategies. Dynamic factor models have become the method of choice for this purpose: they extract the communal underlying factor behind timely economic reports and translate the information of many data series into a single underlying trend. The estimation process may look daunting, but its basics are intuitive and calculation is executable in statistical programming language R.

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Cash hoarding and market dynamics

March 11, 2017

Institutional asset managers can aggravate market swings due to the pro-cyclicality of redemptions, internal leverage and cash positions. A new empirical analysis shows that cash hoarding, a rise in funds’ cash positions in times of redemptions, is the norm. Cash hoarding seems to be particularly pronounced in less liquid markets and is a rational response if fire sale haircuts are prone to escalate with growing flows, i.e. if liquidating late is disproportionately costly. Investment opportunities arise initially from timely positioning and subsequently from the detection of flow-driven price distortions.

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Equity alpha through volatility targeting

February 25, 2017

Volatility targeting has historically enhanced the statistical alpha of standard equity strategies. That is because volatility is more predictable in the short-term than returns. Thus, Sharpe ratios tend to decline, when volatility rises. Expected returns increase after turmoil but only overtime, when volatility might already be subsiding. On its own volatility is not a pure measure of risk premia and does not indicate if actual risk is overstated or underappreciated. A flipside of mechanical volatility targeting is that it contributes to herding and escalatory price dynamics.

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Volatility risk premia and FX returns

February 18, 2017

Volatility risk premia – differences between implied and realized volatility – are plausible and empirically validated predictors of directional foreign exchange returns, particularly for EM currencies. The intuition is that excess implied volatility typically results from elevated risk aversion, which should be indicative of undershooting. When calculating the volatility risk premium it is important to compare short-term implied volatility with realized volatility of that same period. One would expect positive returns on currencies whose very recent volatility has been less than feared.

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Lessons from long-term global equity performance

February 11, 2017

A truly global and long-term (116 years) data set for both successful and failed financial markets shows that equity has delivered positive long-term performance in each and every country that did not expropriate capital owners, even those that were ravaged by wars. Also, equity significantly outperformed government bonds in every country, with a world average annual return of 5% versus 1.8%. The long-term Sharpe ratio on world equity has been 0.24 versus 0.09 for bonds. Valuation-based strategies for market timing have historically struggled to improve equity portfolio performance. Active management strategies that rely on both valuation and momentum would have been more useful.

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Statistical remedies against macro information overload

February 4, 2017

“Dimension reduction” condenses the information content of a multitude of data series into small manageable set of factors or functions. This reduction is important for forecasting with macro variables because many data series have only limited and highly correlated information content. There are three types of statistical methods.The first type selects a subset of “best” explanatory variables (view post here). The second type selects a small set of latent background factors of all explanatory variables and then uses these background factors for prediction (Dynamic Factor Models). The third type generates a small set of functions of the original explanatory variables that historically would have retained their explanatory power and then deploys these for forecasting (Sufficient Dimension Reduction).

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Debt-weighted exchange rates

January 28, 2017

Trade-weighted exchange rates help assessing the impact of past currency depreciation on economic growth through the external trade channel. Debt-weighted exchange rates help assessing the impact of past currency depreciation on economic growth through the financial channel. Since these effects are usually opposite looking at both simultaneously is crucial for using exchange rate changes as a predictor of economic and local market performance. For example, as a consequence of the financial channel many EM economies fail to benefit from currency depreciation in the way that small developed economies do.

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Using SVAR for macro trading strategies

January 21, 2017

Structural vector autoregression may be the most practical model class for empirical macroeconomics. Yet, it can also be employed for macro trading strategies, because it helps identifying specific market and macro shocks. For example, SVAR can identify short-term policy, growth or inflation expectation shocks. Once a shock is identified it can be used for trading in two ways. First, one can compare the type of shock implied by markets with the actual news flow and detect fundamental inconsistencies. Second, different types of shocks may entail different types of subsequent asset price dynamics and may, hence, be a basis for systematic strategies.

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Volcker Rule and liquidity risk

January 7, 2017

The Volcker Rule has banned proprietary trading of banks with access to official backstops. Also, market making has become more onerous as restrictions and ambiguities of the rule make it harder for dealers to manage inventory and to absorb large volumes of client orders in times of distress. This increases liquidity risk, particularly in market segments with longer turnover periods, such as corporate bonds. A new empirical paper confirms that the Volcker Rule has indeed reduced corporate bond liquidity and aggravated the price impact of distress events, such as significant rating downgrades.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

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