PREDICTION OF CRASHES IN MOEX RUSSIAN INDEX USING LOG-PERIODICITY

Financial crises in emerging nations pose significant dangers to economic stability yet predicting them remains difficult. This study tests whether speculative bubbles and subsequent crashes can be predicted by using the Log-Periodic Power Law (LPPL) model to the MOEX Russian Index using daily data from 1997– 2024. We estimate that with little lag, LPPL can predict crash dates and detect important moments of instability in MOEX. The results demonstrate that log-transformed data produce smaller error margins, indicating trade-offs between variance capture and forecasting precision, even if raw price data frequently anticipate crashes more accurately in terms of timing. Although LPPL's ability to predict smaller collapses and lower false positives is still limited, these findings imply that it can give regulators and policymakers early warning signs of systemic risk.