The Price Variability-Volume Relationship on Speculative Markets by George E. Tauchen and Mark Pitts examines the relationship between daily price change variability and trading volume in speculative markets. The authors derive the joint probability distribution of price change and trading volume over any time interval, and determine how this distribution changes as more traders enter or exit the market. Using daily data from the 90-day T-bills futures market, they estimate the model parameters via FIML. The results reconcile a conflict between the price variability-volume relationship in this market and previous findings in other speculative markets. The paper discusses two explanations for the price variability-volume relationship. Clark's explanation emphasizes randomness in the number of within-day transactions, while Epps and Epps' model focuses on the mechanics of within-day trading. The authors argue that both models are complementary and provide insight into speculative market behavior. However, they note that neither model fully explains the observed data, particularly the decline in price variability as trading volume increases. The authors propose a new model that accounts for the growth in the number of traders and the resulting changes in price variability and trading volume. The model is based on an equilibrium theory of within-day price determination and uses a variance-components scheme to model traders' reservation price revisions. The model predicts that the daily price change is normally distributed and that the trading volume is approximately normally distributed for large numbers of traders. The model also predicts that the variance of the price change decreases as more traders enter the market. The authors apply the model to daily data from the 90-day T-bills futures market and find that the model can explain both previous studies and the anomalous data. The model predicts that the mean trading volume increases linearly with the number of traders, while the variance of the price change decreases. The model also predicts that the price variability-volume relationship is positive, which is consistent with the data. The authors conclude that their model provides a more general explanation of the price variability-volume relationship in speculative markets. The model can explain both the results of previous studies and the anomalous data displayed in the introduction. The model also provides insights into the behavior of speculative markets and suggests that further research is needed to fully understand the relationship between price variability and trading volume.The Price Variability-Volume Relationship on Speculative Markets by George E. Tauchen and Mark Pitts examines the relationship between daily price change variability and trading volume in speculative markets. The authors derive the joint probability distribution of price change and trading volume over any time interval, and determine how this distribution changes as more traders enter or exit the market. Using daily data from the 90-day T-bills futures market, they estimate the model parameters via FIML. The results reconcile a conflict between the price variability-volume relationship in this market and previous findings in other speculative markets. The paper discusses two explanations for the price variability-volume relationship. Clark's explanation emphasizes randomness in the number of within-day transactions, while Epps and Epps' model focuses on the mechanics of within-day trading. The authors argue that both models are complementary and provide insight into speculative market behavior. However, they note that neither model fully explains the observed data, particularly the decline in price variability as trading volume increases. The authors propose a new model that accounts for the growth in the number of traders and the resulting changes in price variability and trading volume. The model is based on an equilibrium theory of within-day price determination and uses a variance-components scheme to model traders' reservation price revisions. The model predicts that the daily price change is normally distributed and that the trading volume is approximately normally distributed for large numbers of traders. The model also predicts that the variance of the price change decreases as more traders enter the market. The authors apply the model to daily data from the 90-day T-bills futures market and find that the model can explain both previous studies and the anomalous data. The model predicts that the mean trading volume increases linearly with the number of traders, while the variance of the price change decreases. The model also predicts that the price variability-volume relationship is positive, which is consistent with the data. The authors conclude that their model provides a more general explanation of the price variability-volume relationship in speculative markets. The model can explain both the results of previous studies and the anomalous data displayed in the introduction. The model also provides insights into the behavior of speculative markets and suggests that further research is needed to fully understand the relationship between price variability and trading volume.