Post Model Correction in Risk Analysis and Management

G.-J Siouris,A Karagrigoriou,D Skilogianni

doi:10.33889/ijmems.2019.4.3-044

G.-J Siouris, A Karagrigoriou + Show 1 more

Open Access

https://doi.org/10.33889/ijmems.2019.4.3-044

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Abstract

This work focuses on Value at Risk (VaR) and Expected Shortfall (ES) in conjunction with the so called, low price effect. In order to improve forecasts of risk measures like VaR or ES when low price effect is present, we propose the low price correction which does not involve additional parameters and instead of returns it relies on asset prices. The forecasting ability of the proposed methodology is measured by appropriately adjusted popular evaluation measures, like MSE and MAPE as well as by backtesting methods. For illustrative and comparative purposes a real example from the Athens Stock Exchange as well as a number of penny stocks from Nasdaq, NYSE and NYSE MKT are fully examined. The proposed technique is always applicable, but its superiority and effectiveness is evident in extreme economic scenarios and severe stock collapses. The proposed methodology that pays attention not only to the asset return but also to the asset price, provides sufficient evidence that prices could contain important information which could if taken under consideration, results in improved forecasts of risk estimation.

Highlights

The quantification of risk is an important issue in finance that becomes even more important during periods of financial crises
Popular evaluation measures used in the literature include the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percent Error (MAPE)
Since the main interest lies on returns, which mostly take values in (−0.5,0.5), we prefer MAE and MAPE, because the square will decrease further the errors

Summary

Introduction

The quantification of risk is an important issue in finance that becomes even more important during periods of financial crises. Asset volatility increases with negative financial news and decreases with positive ones, while returns behave in exactly the opposite way Another characteristic that inevitably affects volatility but is frequently overlooked, is the so called low price effect (lpe) (Fritzemeier, 1936) according to which primarily in periods of economic instability, low price stocks fluctuate nervously resulting in increased volatility. In this work we focus on the most popular risk measures and propose a low price correction (lpc) for improving their forecasts, when the low price effect is present. For the resulting low price effect area, the models considered should be appropriately adapted This adaptation is done through the so called low price correction of both the estimation of percentage value at risk denoted by PVaR and the expected percentage shortfall (EPS) to be introduced respectively in the following two subsections

Percentage Value at Risk (PVaR) and Low Price Correction The Percentage Value at

Expected Percentage Shortfall (EPS) and Low Price Correction

Evaluation Measures and Backtesting

Conclusions