Diversified reward-risk parity

July 10, 2021

Risk parity is a portfolio construction technique that seeks to equalize risk contributions from the different components of the portfolio. Risk parity with respect to uncorrelated risk sources maximizes diversification. Simple risk parity rules are based on the inverses of market beta, price standard deviation, or price variance. These methods can be combined with common reward risk metrics, such as the Sharpe ratio, Calmar ratio, STAR ratio, or Rachev ratio. The resulting diversified reward-risk parity allocations have not only outperformed equally-weighted risk portfolios and standard factor allocations but also provided enhanced risk management.

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Market dynamics: belief, risk, and ambiguity effects

April 3, 2021

To understand financial market dynamics, it is helpful to distinguish beliefs, attitudes towards risk, and attitudes towards ambiguity. Beliefs are subjective evaluations of future cash flows. Risk refers to uncertainty within a model of the asset’s return. And ambiguity means uncertainty about the model and probability distributions. Accordingly, one can separate price dynamics into three effects: changes in beliefs, changes in risk premia and changes in ambiguity premia. Ambiguity premia seem to be dominant, particularly when investors have little information about the nature of a particular risk. Traditional risk premia seem to be much less significant. Belief effects are negligible when ambiguity is high but increase as information accumulates. Often trading opportunities arise from the mean reversion of ambiguity premia and the “under-adjustment” of beliefs.

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The financial stability interest rate

March 6, 2021

The financial stability interest rate is a threshold above which the real interest rate in an economy triggers financial constraints and systemic instability. It is different from the natural rate of interest, which balances growth and inflation. Indeed, the relationship between the financial stability interest rate and the natural interest rate may be one of the most important predictors of medium-term market direction and future crisis risk. A low financial stability rate versus the natural rate will create a tendency for real interest rates to rise to levels that disrupt financial relations. Factors that lower the financial stability rate include leverage and asset quality in the financial system. It is possible to build time series of financial conditions and stability rates.

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Measures of market risk and uncertainty

August 8, 2020

In financial markets, risk refers to the probability distribution of future returns. Uncertainty is a broader concept that encompasses ambiguity about the parameters of this probability distribution. There are various types of measures seeking to estimate risk and uncertainty: [1] realized and derivatives-implied distributions of returns across assets, [2] news-based measures of policy and political uncertainty, [3] survey-based indicators, [4] econometric measures, and [5] ambiguity indices. The benefits for macro trading are threefold. First, uncertainty measures provide a basis for comparing the market’s assessment of risk with private information and research. Second, changes in uncertainty indicators often predict near-term flows in and out of risky asset classes. Third, the level of public and market uncertainty is indicative of risk premia offered across asset classes.

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Risk management shocks and price distortions

January 18, 2020

Risk management relies on statistical metrics that converge on common standards. These metrics can change drastically alongside market conditions. A risk management shock is a large unanticipated market-wide change in statistical risk estimates. These shocks give rise to coerced or even distressed flows, typically subsequent to an initial large move in market prices. Risk management shocks and related flows can team up with other dynamics in the financial system to form feedback loops. Such reinforcing dynamics include dynamic hedging, market price-driven credit downgrades, popular fear of crisis, investment fund redemptions, and forced deleveraging. Feedback loops can trigger large and persistent price distortions and offer special trading opportunities.

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Bayesian Risk Forecasting

May 4, 2019

Portfolio risk forecasting is subject to great parameter uncertainty, particularly for longer forward horizons. This simply reflects that large drawdowns are observed only rarely, making it hard to estimate their ‘structural’ properties. Bayesian forecasting addresses parameter uncertainty directly when estimating risk metrics, such as Value-at-Risk or Expected Shortfall, which depend on highly uncertain tail parameters. Also, the Bayesian risk forecasting method can use ‘importance sampling’ for generating simulations that oversample the high-loss scenarios, increasing computational efficiency. Academic work claims that Bayesian methods also produce more accurate risk forecasts for short- and long-term horizons.

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Signaling systemic risk

March 23, 2019

Systemic financial crises arise when vulnerable financial systems meet adverse shocks. A systemic risk indicator tracks the vulnerability rather than the shocks (which are the subject of ‘stress indicators’). A systemic risk indicator is by nature slow-moving and should signal elevated probability of financial system crises long before they manifest. A recent ECB paper proposed a practical approach to building domestic systemic risk indicators across countries. For each relevant categories of financial vulnerability, one representative measure is chosen on the basis of its early warning qualities. The measures are then normalized and aggregated linearly. In the past, aggregate systemic risk indicators would have shown vulnerability years ahead of crises. They would also have indicated the depth of ensuing economic downturns.

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How to estimate risk in extreme market situations

March 16, 2019

Estimating portfolio risk in extreme situations means answering two questions: First, has the market entered an extreme state? Second, how are returns likely to be distributed in such an extreme state? There are three different types of models to address these questions statistically. Conventional “extreme value theory” really only answers the second question, by fitting an appropriate limiting distribution over observations that exceed a fixed threshold. “Extreme value mixture models” simultaneously estimate the threshold for extreme distributions and the extreme distribution itself. This method seems appropriate if uncertainty over threshold values is high. Finally, “changepoint extreme value mixture models” even go a step further and estimate the timing and nature of changes in extreme distributions. The assumption of changing extreme distributions across episodes seems realistic but should make it harder to apply the method out-of-sample.

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Drawdown control

January 19, 2019

Containment of drawdowns and optimization of performance ratios for multi-asset portfolios is critical for trading strategies. Alas, short data series or structural changes often render estimates of covariance matrices unreliable. A popular solution is risk-parity with volatility targeting. An alternative is ‘MinMax’ drawdown control, which builds on a broad interpretation of drawdowns as maximum actual or opportunity losses from not adjusting a benchmark portfolio to a specific underlying asset. In the case of one risky and one safe asset, this boils down to managing simultaneously the risks of conventional PnL drawdowns and foregone risk returns. Optimal asset allocation depends only on aversion to different types of drawdowns. Averaging over a plausible range of aversion parameters gives a model portfolio. Empirical evidence for the case of cryptocurrencies suggests that in an environment of uncertain returns MinMax delivers better PnL return-to-drawdown ratios than conventional volatility control.

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How systemic financial risk is measured

November 10, 2018

Public institutions have developed a wide range of methods to track systemic financial risk. What most of them have in common is reliance on financial market data. This implies that systemic risk indicators typically only show what the market has already priced, in form of correlation, volatility or value. They cannot anticipate market crises. Their main use is to predict when and how market turmoil begins to sap the functioning of the financial system. Some methods may be useful for macro trading. For example, Conditional Value-at-Risk can identify sources of systemic risk, such as specific institutions or market segments. Principal Components Analysis can indicate changing concentration of risk across securities and markets.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation central counterparty clearing 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 over eighteen years. 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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