Skanalytix
SKANALYTIX INSIGHTS

Insights

Research notes and analysis on realistic market simulation, financial time series and portfolio risk.

What Information Should the State of a Financial Market Model Contain?

Suppose we want to estimate the conditional distribution of the next return, f(r[t+1]), given the current state x[t]. What information should x[t] capture? Different modelling approaches make different choices. The state could include the recent history of returns, interest rates, credit spreads, liquidity, leverage, positioning, or many other variables. Other models introduce latent variables to represent unobserved aspects of the market, while some build more…

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What Can One Financial History Tell Us About the Future?

Financial risk analysis is concerned with outcomes that have not yet occurred. We want to understand how a portfolio might behave under different market conditions, how severe potential losses could be, and how sensitive our conclusions are to the assumptions underlying them. Yet the evidence available to us is fundamentally limited. We observe only one realised history of financial markets: the particular sequence of events,…

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Can Historical Optimisation Deliver Robust Financial Models?

Financial markets generate enormous amounts of data, yet the number of genuinely distinct market environments available for evaluating financial models is surprisingly limited. Major liquidity crises, sustained inflationary tightening cycles and other periods of severe disruption are relatively rare. This creates a tension at the heart of quantitative finance. We routinely optimise strategies, portfolios and models against historical observations, but how much confidence should we…

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When Extremes Cluster

Extreme financial returns are not distributed evenly through time. Large market movements often occur in periods of sustained turbulence, with unusually large gains and losses appearing close together. This temporal organisation matters for risk analysis: two return series can have similar distributions yet expose a portfolio to very different sequences of losses. A useful test of synthetic financial data is therefore whether it can reproduce…

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When Portfolio Dispersion Collapses

Portfolio diversification is often assessed through the correlations between asset returns. When correlations are relatively low, individual assets can contribute different patterns of returns, reducing overall portfolio risk. But these relationships are not constant. During periods of market stress, assets that ordinarily behave quite differently may begin moving together, weakening diversification precisely when it is most needed. An important question for financial modelling is whether…

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