Table 37 Parameters of the best models for \(R_t^{s}\) computed on real and simulated data for Netflix
From: Drawdown-based risk indicators for high-frequency financial volumes
Summary of the best statistical model selection | ||||
|---|---|---|---|---|
for \(R_t^{s}\)-NETFLIX fixing \(M=80\%\) | ||||
\(s=0\) | \(M'\) | Best model | Real data (\(\mu\), \(\sigma\)) | Simulated data (\(\mu\), \(\sigma\)) |
30% | Lognormal | 2.7483–2.5359 | 2.8195–1.8600 | |
40% | Lognormal | 2.6975–2.4817 | 2.4759–1.7315 | |
50% | Lognormal | 2.4494–1.7925 | 2.1006–1.5940 | |
\(s=5\) | \(M'\) | Best model | Real data (\(\mu\), \(\sigma\)) | Simulated data (\(\mu\), \(\sigma\)) |
|---|---|---|---|---|
30% | Lognormal | 2.0871–1.6907 | 2.8411–1.7920 | |
40% | Lognormal | 1.3389–1.2555 | 2.4902–1.7140 | |
50% | Lognormal | 1.6473–1.5233 | 2.2511–1.6284 |
\(s=50\) | \(M'\) | Best model | Real data (\(\mu\), \(\sigma\)) | Simulated data (\(\mu\), \(\sigma\)) |
|---|---|---|---|---|
30% | Lognormal | 2.4181–1.6505 | 2.3354–1.5037 | |
40% | Lognormal | 2.1419–1.5548 | 2.1854–1.4858 | |
50% | Lognormal | 1.8792–1.5830 | 2.0071–1.4330 |
\(s=100\) | \(M'\) | Best model | Real data (\(\mu\), \(\sigma\)) | Simulated data (\(\mu\), \(\sigma\)) |
|---|---|---|---|---|
30% | Lognormal | 2.1027–1.6756 | 1.8558–1.4143 | |
40% | Lognormal | 1.9135–1.6314 | 1.7284–1.3628 | |
50% | Lognormal | 1.6221–1.6373 | 1.5494–1.3021 |