QCAM

QCAM Currency Asset Management – Cooperation Partner

From 2017 to 2020, Joerg Osterrieder collaborated with QCAM Currency Asset Management as a cooperation partner, focusing on the quantitative research, development, and implementation of new currency-overlay asset-management products. The engagement combined systematic strategy design with rigorous statistical validation and risk-management integration, supporting QCAM’s systematic investment process and its operational and regulatory context.

Currency-Overlay Strategy Design

Strategy design at QCAM centred on systematic currency-overlay strategies built on quantitative models that drew on both fundamental and technical indicators. Factor-based frameworks integrated carry, momentum, and value signals to target excess returns in currency markets, while rolling-window regressions and Kalman-filter approaches estimated time-varying hedge ratios as market relationships shifted. The resulting strategies produced rules-based currency exposures that could be rebalanced on a systematic schedule, giving clients transparent access to overlay strategies calibrated to prevailing market regimes.

Signal Construction and Forecasting

Signal construction combined short- and medium-term momentum signals built from time-series techniques — ARIMA models, moving averages, and trend-following indicators — with macroeconomic mean-reversion signals that drew on interest-rate differentials and purchasing-power-parity metrics to identify over- and undervalued currencies. Cointegration techniques then detected long-term equilibrium relationships between currency pairs, providing the statistical foundation for relative-value trades. The integration of technical and macro signals created a forecasting stack that covered multiple horizons and diversified away from the single-signal fragility that simpler models often exhibit.

Risk-Management Integration

Risk-management integration was woven through every stage of strategy design. Dynamic position-sizing models drew on rolling volatility and conditional Value-at-Risk (CVaR) to manage tail risks and control drawdowns, while scenario analysis using historical stress periods — the 2008 financial crisis and the 2011 eurozone crisis in particular — validated model behaviour under conditions far from the post-crisis sample. Liquidity filters adjusted position sizes and execution schedules during low-liquidity periods, reducing the cost and operational risk of trading in thinner conditions. The layered controls converted the research-layer signals into something that could be run as a live overlay without outsized tail exposure.

Backtesting and Performance Evaluation

Backtesting frameworks used historical spot and forward-rate data, with transaction costs and slippage modelled explicitly so that the net-of-cost performance picture reflected real trading rather than an idealised simulation. Walk-forward testing and rolling performance metrics — the Sharpe ratio and maximum drawdown in particular — assessed out-of-sample robustness across different regimes, and strategy performance was evaluated across those regimes to verify consistency and stability. The evaluation discipline mattered as much as the strategy design itself, because small changes in assumed transaction costs or slippage can swing headline Sharpe figures dramatically for high-turnover currency strategies.

Portfolio Construction and Optimisation

Portfolio construction integrated the currency-overlay signals into multi-asset portfolio-optimisation frameworks, with mean-variance and risk-parity approaches used to balance currency risk against the underlying asset exposures of client portfolios. Currency correlation matrices were modelled explicitly, and portfolio sensitivity to exchange-rate shocks was stress-tested to produce a clear view of residual currency exposure after overlay. The portfolio layer converted individual currency signals into strategy-level exposures that fit each client’s broader investment mandate.

Collaboration Highlights and Outcomes

Close work with QCAM’s quantitative research team aligned the models with the firm’s systematic investment process and its operational and regulatory requirements, and contributions to integrating trend-following signals into production systems ensured consistency with operational risk controls and compliance guidelines. The engagement delivered quantitative currency-overlay strategies that enhanced portfolio diversification and reduced currency risk, and dynamic risk-management frameworks that improved drawdown control and performance stability. The QCAM collaboration focused on designing and implementing systematic currency-overlay strategies using traditional quantitative models, contributing to currency asset-management products that offered clients rules-based exposure to currency markets and strengthening the firm’s systematic investment offering during the 2017–2020 period.