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Background in Quantitative Risk Modeling

A small admission: we arrived at regime switching volatility modeling by way of frustration. Traditional approaches seemed to miss the shifts we observed, and our team became curious about models that adapt to changing risk environments. Through collaborations and countless hours of research, we refined our methods, integrating them into practical tools that inform asset allocation and scenario analysis. Our approach does not promise certainty but provides a framework for understanding market transitions, measuring risk, and supporting data-driven decisions. We benchmark our models against alternatives, finding that regime switching methods capture inflection points others miss. This work remains ongoing, shaped by new questions and market shifts. Our goal is not to deliver absolute answers but to give our clients a more nuanced view of risk — one that helps them think in probabilities and anticipate possible changes. We value dialogue, learning from both clients and peers, and believe each conversation improves our approach. Our commitment is to clarity, rigor, and continuous improvement.
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10+
Field Experience
Years working with volatility models in diverse markets
20+
Published Papers

Research publications on advanced risk modeling techniques

5
Quantitative Experts
Team members with advanced quantitative backgrounds

Our Core Values

Our work is grounded in transparent research, a respect for evolving market dynamics, and a commitment to data-driven decision-making. We do not claim to offer certainty, but rather provide frameworks for understanding change and informing resource allocation.

Transparency

We believe in research that is openly documented and reproducible. Sharing our methods and datasets allows for constructive critique and drives ongoing improvement in our modeling practices.

Adaptability

The market is never static. We value adaptability in both our models and our mindset, revisiting assumptions and updating frameworks as new information emerges from empirical evidence.

Collaboration

Collaboration is core to our process. We seek dialogue with peers and clients, treating every question as an opportunity to clarify, refine, or rethink our approach to volatility modeling.

Integrity

Integrity underpins every analysis we conduct. We aim for accuracy in our reporting and fairness in representing the strengths and limits of the techniques we use.

Meet the Quantitative Modeling Team

Our team brings together expertise in quantitative modeling, finance, and data science. With backgrounds ranging from academic research to applied analytics, we work collaboratively to refine regime switching volatility models for practical use. Our process values peer review, debate, and clear communication.

Rohan Deshmukh Sharma

Rohan Deshmukh Sharma

Quantitative Analyst Lead

Mumbai, India

With a background in stochastic modeling and applied mathematics, this team member specializes in integrating regime switching frameworks into risk assessments and allocation reviews.

Areas of Focus

Stochastic processes Volatility modeling Scenario analysis Mathematical finance
Priya Varadarajan Patel

Priya Varadarajan Patel

Senior Data Scientist

Bengaluru, India

Focused on empirical validation and backtesting, this team member bridges model development with performance measurement, ensuring new techniques are robust and reliable under varying conditions.

Core Skills

Backtesting methods Empirical research Risk metrics Data integrity
Samarjeet Kaul Mukherjee

Samarjeet Kaul Mukherjee

Risk Modeling Consultant

Delhi, India

Experienced in model interpretation and communication, this team member helps clarify technical findings for client and peer audiences, translating quantitative results into practical takeaways.

Specialization Areas

Statistical reporting Client communication Quantitative literacy Workflow integration

A Research-Driven Philosophy for Understanding Dynamic Risk

Our methodology is shaped by the conviction that financial risk is not static. Regime switching volatility models offer a lens to identify transitions and adapt forecasts, providing a more flexible approach than fixed-parameter models. We believe each assumption should be tested and each process revisited.

Adapting to Shifts Using Regime Switching Volatility Models for Deeper Insight

We favor models that recognize multiple regimes in volatility, reflecting real-world market shifts. This approach stands in contrast to static frameworks, which often overlook structural changes in risk and return.

Applying Research Insights While Stress-Testing Real-World Outcomes
Research guides our work, but we balance theoretical rigor with practical application. We review empirical evidence, test assumptions, and apply models that hold up across different market environments.
Clarity Over Complexity in Explaining Model Assumptions and Results
Transparency matters more to us than complexity. We explain our assumptions, highlight model limitations, and encourage questions so users understand both the strengths and constraints of our methods.
Continuous Model Evaluation Against Alternatives and New Market Data

Learning is ongoing. We compare regime switching methods with traditional risk models, track performance over time, and update our processes as new data emerges or as the market evolves.

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