Volatility Historical Accuracy in Pakistan Forex Trading

In Pakistan's forex and equity markets, historical volatility and GARCH-type models usually describe reality reasonably well as long as the market structure stays stable. During periods without major political shocks, exchange rate regime changes or abrupt policy announcements, volatility tends to cluster in a way that standard formulas capture: large PKR or stock index moves are followed by large moves, and calm periods follow calm periods. Under these conditions, volatility estimates and Value-at-Risk calculations are often close to realized outcomes and can support position sizing, leverage decisions and stop-loss levels.

Accuracy drops sharply once a structural break occurs. Events such as elections, sudden changes in the State Bank of Pakistan's policy framework, IMF program milestones or global crises like COVID-19 introduce new information that is not contained in the historical sample. Models calibrated only on pre-break data typically understate the size and speed of the resulting volatility spike. In such episodes, historical volatility is better viewed as a minimum expected risk level, not as a forecast.

Regime shifts in the exchange rate system create a similar problem. Moving from a quasi-fixed arrangement to a more flexible rate changes the entire volatility process, so parameters estimated in the old regime cease to be informative. Until models are re-estimated or switched to a regime-dependent specification, forecasts can diverge from actual PKR behavior. For traders active in Pakistan, the key is to treat statistical volatility as useful but conditional: reliable in relatively stable times, fragile when political or structural conditions start to move.

Historical volatility is a backward-looking statistic based on past returns. For daily or weekly data, the usual steps are:

• Calculate percentage returns for each period.
• Find the average return over the chosen window.
• Compute deviations of each return from this average.
• Square these deviations and take their average to obtain variance.
• Take the square root to get volatility.
• Annualize the result with the square-root-of-time rule.

In Pakistan, this approach is applied both to currency pairs involving the PKR and to major stock indices. It summarizes how widely prices have moved over the lookback window but does not, by itself, predict what happens next. GARCH and EGARCH models extend this by letting volatility depend on past shocks and past volatility, so that periods of turbulence and calm are modeled explicitly rather than averaged away.

Empirical work on Pakistan Stock Exchange data often finds that GARCH-family models give a good in-sample fit when the surrounding macro and political backdrop does not shift dramatically. Once fitted, residuals become closer to homoscedastic noise, and standard diagnostics indicate that autocorrelation in returns and volatility clustering are largely absorbed by the model structure.

The Pakistani equity market shows volatility persistence: large shocks fade only gradually, so elevated risk can last for extended periods. During the COVID-19 period, EGARCH specifications documented this persistence, with heightened volatility remaining even after the initial pandemic shock. In such phases, when policy settings and the trading environment are not undergoing further abrupt changes, these models can provide risk estimates and VaR numbers fairly close to actual portfolio outcomes.

On the forex side, managed regimes with relatively steady policy frameworks tend to produce more predictable PKR volatility patterns. When the State Bank of Pakistan maintains a consistent approach, historical volatility for PKR-based pairs often gives reasonable guidance for short-term fluctuations. Traders can then use such estimates as inputs for order placement and capital allocation.

An illustration of how realized equity volatility has evolved is given below:

Values move within a band in non-crisis years, and models calibrated on data like this tend to perform adequately as long as similar conditions continue.

Historical volatility becomes misleading when fundamental assumptions about a stable data-generating process break down. Pakistan's markets are exposed to pronounced political risk, and research frequently identifies elections, government changes and security incidents as primary triggers of volatility spikes. When such events occur, realized volatility often overshoots levels implied by models trained on calmer periods.

The outbreak of COVID-19 is one example where volatility jumped beyond pre-crisis projections. Initial lockdowns and global uncertainty generated swings that pre-pandemic GARCH models failed to anticipate. Only after re-estimation on new data did explanatory power improve, illustrating how backward-looking calibration lags real-time regime changes.

Exchange rate dynamics show similar behavior during policy shifts. A move from tighter control toward more market-based PKR pricing changes how shocks propagate through the system. Models assuming constant parameters across these regimes can give a falsely stable picture and seriously understate risks. VaR frameworks relying on normality assumptions or short historical windows are particularly exposed to underestimating tail risk in such conditions.

Research on Pakistan highlights politics as a central determinant of asset price volatility. Investors tend to react strongly around election periods, major policy announcements and episodes of instability. Purely quantitative models that use only past prices and neglect such information can fit historical data but still miss the timing and magnitude of volatility bursts around these events.

Longer-term series for exchange rates and inflation using State Bank of Pakistan data show that volatility characteristics differ across political and monetary regimes. Authoritarian and democratic episodes, along with varying policy priorities, produce distinct volatility patterns. Stationary models with constant parameters cannot reflect these shifts and may therefore give an overly smooth risk profile that conflicts with actual market experience.

For a trader, this means that statistical volatility should be supplemented with a continuous read on political developments, policy direction and external financing arrangements such as IMF programs.

Several techniques can help bring historical volatility measures closer to Pakistan's trading reality:

• Regime-switching volatility models that allow for separate high- and low-volatility states, so parameters adjust when the market environment changes.
• Inclusion of global risk indicators alongside local variables to capture how international shocks propagate into PKR and local equities.
• Frequent recalibration of model parameters so that new data from changed conditions gradually replace outdated information.
• Use of stress tests and scenario analysis to assess losses from extreme but plausible events, such as abrupt devaluations or sharp political crises, that lie outside the historical sample.

These approaches do not remove the backward-looking nature of historical volatility, but they reduce the gap between formula and realized outcomes when Pakistan enters a new regime.

For day-to-day decisions, traders active in Pakistan usually treat volatility estimates conditionally:

• In relatively quiet periods, GARCH-based volatility and historical VaR can be used for position sizing, leverage management and stop-loss levels.
• Around known political events, expected regime shifts or signs of external stress, the same metrics are better read as conservative baselines rather than precise forecasts.

Practical adjustments in high-uncertainty phases can include:

• Reducing leverage on PKR pairs and sensitive equities.
• Widening stop-loss distances while lowering overall exposure.
• Increasing cash or hedged positions when structural breaks seem likely.
• Monitoring communication from the State Bank of Pakistan, IMF program reviews, election calendars and geopolitical news as early signals of changing volatility regimes.

Forex participants who combine statistical measures with systematic attention to political and structural signals are better placed to judge when historical volatility formulas still mirror Pakistan's market reality and when they have started to fall behind it.