Financial institutions hold risks in their investments that can potentially affect their ability to serve their clients. For banks to weigh their risks, Value-at-Risk (VaR) methodology is used, which involves studying the distribution of losses and formulating a statistic from this distribution....

This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average model that jointly estimates volatility, skewness and kurtosis over time using a modified form of the Gram-Charlier density...

An important concern of macroeconomic analysis is how interest rates affect the cash balance demanded at a certain level of nominal income. In fact, the interest-rate- elasticity of the liquidity demand determines the effectiveness of monetary policy, which is useless under absolute liquidity...

This paper conducts a comparative evaluation of the predictive performance of various Value at Risk (VaR) models such as GARCH-normal, GARCH-t, EGARCH, TGARCH models, variance-covariance method, historical simulation and filtred Historical Simulation, EVT and conditional EVT methods. Special...

A theoretical framework is presented to characterise the money demand in deregulated markets. The main departure from the perfect capital markets setting is that, instead of assuming that investors can lend and borrow any amount of capital at a single (and exogenously determined) interest rate,...

In the paper, I simulate the games with a joint presence of 95% VaR-rule and return-rule groups of agents in the game. Simulations highlighted the level of omniscience, next being the rule, which agents follow at the decision-making, and the third the presence of liquidity agents in the game....

In this paper, we assess the informational content of daily range, realized variance, realized bipower variation, two time scale realized variance, realized range and implied volatility in daily, weekly, biweekly and monthly out-of-sample Value-at-Risk (VaR) predictions. We use the recently...

We discuss linear regression approaches to conditional Value-at-Risk and Average Value-at-Risk (Conditional Value-at-Risk, Expected Shortfall) risk measures. Two estimation procedures are considered for each conditional risk measure, one is direct and the other is based on residual analysis of...

This paper introduces the concept of risk parameter in conditional volatility models of the form $\epsilon_t=\sigma_t(\theta_0)\eta_t$ and develops statistical procedures to estimate this parameter. For a given risk measure $r$, the risk parameter is expressed as a function of the volatility...