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Dynamics of the volume-volatility relationship in the currency markets
Tůma, Adam ; Baruník, Jozef (advisor) ; Komárek, Luboš (referee)
This work investigates the volume-volatility relationship dynamics in the currency markets using data of five currency pairs in the period between 2010 and 2022. By employing multiple specifications of the HAR model with volume- related regressors and also with time-varying parameters (TVP), we examine the relationships' changing dynamics over time with a focus on improving volatility forecasting performance. Our main findings suggest a strong correlation between volume and volatility. The TVP-HARV model shows significantly changing dy- namics of the volume-volatility relationship, especially during periods affected by politics, changing monetary policies or global crises. The proposed models, however, do not improve out-of-sample volatility forecasting performance com- pared to the benchmark HAR model. The causal effect in the volume-volatility relationship in the currency markets is slightly more substantial in the direction of volatility towards volume, where we find slight forecasting improvements. Our findings conclude that volume and volatility in the currency markets are mainly moving simultaneously with a very strong correlation and much weaker and often insignificant causal effects on both sides, which supports the mixture of distributions hypothesis.
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Volume - volatility relation across different volatility estimators
Kvasnička, Tomáš ; Krištoufek, Ladislav (advisor) ; Avdulaj, Krenar (referee)
The main objective of this thesis is to analyze whether traded volume increases predictive power of volatility. We are mostly focused on Garman-Klass volatility estimator, which is more efficient than squared returns. Both univariate (AR, HAR, ARFIMA) and multivariate models (VAR, VAR-HAR) are used to find out if traded volume improves volatility forecasting. Furthermore, GARCH(1,1) both with and without traded volume is carried out and forecasted. All these methods are estimated on a basis of rolling window and during each step 1-day ahead forecast is computed. Final assessment is based on MAPE, RMSE and Mincer-Zarnowitz test of the out-of-sample forecasts, which are compared with the realized volatility. It turns out that traded volume slightly improves predictive power of the scrutinized models in case of FTSE 100 and IPC Mexico, contrary to Nikkei 225 and S&P 500 when a decrease of the predictive power is detected. Moreover, we observe that only HAR and VAR-HAR models are able to produce an unbiased forecast. As the evidence of the improvement is not conclusive and to maintain model parsimony, HAR model fitted by Garman-Klass volatility appears to be the best alternative in case of missing the realized volatility.
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Volume - volatility relation across different volatility estimators
Kvasnička, Tomáš ; Krištoufek, Ladislav (advisor) ; Avdulaj, Krenar (referee)
The main objective of this thesis is to analyze whether traded volume increases predictive power of volatility. We are mostly focused on Garman-Klass volatility estimator, which is more efficient than squared returns. Both univariate (AR, HAR, ARFIMA) and multivariate models (VAR, VAR-HAR) are used to find out if traded volume improves volatility forecasting. Furthermore, GARCH(1,1) both with and without traded volume is carried out and forecasted. All these methods are estimated on a basis of rolling window and during each step 1-day ahead forecast is computed. Final assessment is based on MAPE, RMSE and Mincer-Zarnowitz test of the out-of-sample forecasts, which are compared with the realized volatility. It turns out that traded volume slightly improves predictive power of the scrutinized models in case of FTSE 100 and IPC Mexico, contrary to Nikkei 225 and S&P 500 when a decrease of the predictive power is detected. Moreover, we observe that only HAR and VAR-HAR models are able to produce an unbiased forecast. As the evidence of the improvement is not conclusive and to maintain model parsimony, HAR model fitted by Garman-Klass volatility appears to be the best alternative in case of missing the realized volatility.
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