Does Trading Volume Drive Systemic Banks’ Stock Return Volatility? Lessons from the Greek Banking System

Athanasios Tsagkanos,Christos Floros,Konstantinos Gkillas,Christoforos Konstantatos

doi:10.3390/ijfs9020024

Athanasios Tsagkanos, Christos Floros + Show 2 more

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https://doi.org/10.3390/ijfs9020024

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Abstract

The present research investigates the impact of trading volume on stock return volatility using data from the Greek banking system. For our analysis, the empirical study uses daily measures of volatility constructed from intraday data for the period 5 January 2001–30 December 2020. This period includes several market phases, such as the latest financial crisis, the European sovereign debt crisis and enforcement of restrictions on transactions owing to capital controls on the Athens Stock Exchange in June 2015. Based on the estimated quantile regressions, we find evidence of a direct impact of the trading volume on stock return volatility mainly in all quantiles. The findings extrapolated are of relevance and interest to financial (banking) analysts, policy makers and practitioners concerned with intraday data and volatility modeling.

Highlights

Financial market volatility is a factor of primary importance relevant to several issues in the field of finance, varying from asset management to risk management (Poon and Granger 2003)
At the 0.05 quantile, it is observed that the direct impact of trading volume on volatility estimator σp2 is statistically significant at 1%, which equals
This work investigated the direct impact of the trading volume of Greek banking institutions on stock return volatility considering the four major banking institutions in Greece: (i) Alpha bank, (ii) Eurobank, (iii) National Bank of Greece and (iv) Piraeus Bank

Summary

Introduction

Financial market volatility is a factor of primary importance relevant to several issues in the field of finance, varying from asset management to risk management (Poon and Granger 2003). In this respect, market participants’ main concerns are the nature and level of volatility. Considering the involvement of volatility in investment decision making, derivative pricing and financial market regulation, numerous approaches have been suggested in the relevant literature in terms of its estimation. Stochastic volatility models overcome the limitation observed in Black and Scholes’ model, with the volatility being constant over time and immune to changes in the price level of the underlying asset. Parametric, semi-parametric and nonparametric parameters can estimate volatility, despite the fact that it is latent

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