Your responsibilities for this role will be:
Conduct quantitative research with both financial risk modelling and statistical models to measure liquidity constraints and counterparty risk in FX derivatives and structured products.
Assist in developing and back-testing of Monte Carlo stochastic models for FX portfolio liquidity stress tests in Python.
Develop portfolio market risk and credit risk management models to assess portfolio risk tolerance and drive business decisions.
Evaluate and recommend effective financing and hedging strategies to limit portfolio exposure and liquidity risk.
Develop risk and performance metrics at both individual trade and portfolio levels.
Produce comprehensive liquidity reports to facilitate executive-level decision making on company liquidity reserve, capital financing and portfolio hedging strategies.
Build Python models and algorithms to streamline analytic functions such as calculation of mark to market for FX derivatives, portfolio sensitivity analysis, liquidity reports automation, etc.
Build risk-based pricing models (e.g. liquidity risk, credit risk, swap risk, etc. - XVAs) to derive product minimum spreads.
Build statistical models to maximise product spreads by analysing corporate customers' financials, geographic data and historical hedging behaviour.
Quantify trading costs with different banks such as bid-ask spreads, swap costs and margin posting agreement.
Embed model calculations in either excel pricing tools or Google Data Studio dashboards for the front office.
Recommend strategies and pricing for structuring complex FX derivative products (e.g. Cross currency swap, options structures and deal-contingent forwards).
Identify portfolio Macro risk factors and promote beta neutral trading strategies.
Develop frameworks to evaluate the dealers' profitability and performances.
Familiarity with International Financial Reporting Standards (IFRS - valuations of FX derivatives).
Assist the FP&A team with liquidity forecasts and budget preparation.
Requirements:
Economics or quantitative degree ideally with knowledge of financial modelling, accounting and econometrics.
Advanced experience working with Microsoft Office Suite (Excel, PowerPoint) and Google Docs Editors Suite, Python, SQL.
Familiarity with BI tools such as Looker and Google Data Studio.
Strong written and oral skills, ability to explain modelling results to non-technical audiences.
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