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Stochastic Volatility (SV) models are an alternative to GARCH models for estimating volatility and several empirical studies have indicated that volatility exhibits long-memory behavior. The main objective of this work is to propose a new method to estimate a univariate long-memory stochastic volatility (LMSV) model. For this purpose we formulate the LMSV model in a state-space representation with non-Gaussian perturbations in the observation equation, and the estimation of parameters is performed by maximizing the likelihood written in terms derived from a Kalman filter algorithm. We also present a procedure to calculate volatility and Value-at-Risks forecasts. The proposal is evaluated by means of Monte Carlo experiments and applied to real-life time series, where an illustration of market risk calculation is presented.

We study the importance of economic uncertainty so as to predict realized jumps (hereafter jumps) in the pound-dollar exchange rate. The empirical analysis covers the time period from February 1900 to May 2018 on a monthly basis, incorporating several market states, including various booms and crashes. First, we apply a standard linear Granger causality test in order to identify causal effects from economic uncertainty to jumps. We show that the standard linear Granger causality test fails to capture such casual effects. Providing the misspecification of the linear model, we next make use of a nonparametric causality-in-quantiles test. This test allows us to take into account the substantial evidence of nonlinearity along with the structural breaks between economic uncertainty and jumps. In applying this data-driven robust procedure, we find strong evidence of uncertainty causing jumps of the dollar-pound exchange rate. These results are robust over the entire conditional distribution of jumps, exhibiting the strongest impact at the lowest conditional quantiles considered. In addition, our results are generally found to be robust to alternative measures of uncertainty, jumps generated at a daily frequency based on shorter samples of intraday data, and across three other dollar-based exchange rates.

This paper investigates the volatility spillover effect between the national stock and bond markets in the five East Asian emerging countries. We use wavelet signal decomposing technique, GARCH models with different distribution functions and quantile regression. We find that the spillover effect is much higher in more turbulent times, than in calm periods, whereby this effect is stronger from stocks to 10Y bonds, than vice-versa , and it applies for all the countries. Using wavelet signals, we determine that, in most cases, the volatility transmission is higher in short-term horizon, than in midterm and long-term. The effect is stronger in countries with the less developed financial markets (Thailand, Indonesia and Malaysia) than in countries with more developed financial markets (China and Korea), and this is particularly evident in direction from stock to bond markets. Wavelet coherence indicates low volatility correlation in short time-horizons and relatively high correlation in midterm and long-term, which applies for all selected countries. Wavelet cross-correlation indicates that volatility spillover shocks predominantly transmit from bond markets to stock market in more developed China and Korea, whereas volatility shocks from stock market spill over towards bond market in less developed Thailand and Indonesia in very short-time horizon (2–4 days).

Using annual data from two panels, one of 11 Eurozone countries and another of 31 OECD countries, we estimate a two-regime log-linear as well as a nonlinear model for the spread as a function of macroeconomic and quality-of-institutions variables. The two regimes, a high-spread and a low-spread regime, are distinguished by using a threshold, in accordance with the perceived “fair” value of the spread as a reference point. Our results suggest that government-bond spreads are regime-dependent, as most of the regression coefficients of the determinants of the spread are larger (in absolute value) in the high-spread regime than in the low-spread regime. That is, an improvement in the macroeconomic environment (e.g., lower unemployment, lower inflation, lower growth of the debt-to-GDP ratio, less macroeconomic uncertainty, higher growth of real GDP), and/or an improvement in the quality of institutions (e.g., less corruption) reduce the spread facing a country (by enhancing its creditworthiness) to a greater extent in high-spread situations than in low-spread situations. A possible explanation is that the demand for and the supply of loans are inelastic at higher than “fair” interest rates and elastic at lower rates.

The widely used Prais–Winsten technique for estimating parameters of linear regression model with serial correlation is sensitive to outliers. In this paper, an alternative method based on Gini mean difference (GMD) is proposed. A Monte Carlo simulation is used to show that the Gini estimator is more robust than the general least squares one when the data are contaminated by outliers.

This article introduces a model to estimate the risk-neutral density of stock prices derived from option prices. To estimate a complete risk-neutral density, current estimation techniques use a single mathematical model to interpolate option prices on two dimensions: strike price and time-to-maturity. Instead, this model uses B-splines with at-the-money knots for the strike price interpolation and a mixed lognormal function that depends on the option expiration horizon for the time-to-maturity interpolation. The results of this “hybrid” methodology are significantly better than other risk-neutral density extrapolation methods when applied to the recovery theorem.

The random coefficient autoregressive model has been utilized for modeling financial time series because it possesses features that are often observed in financial time series. When the mean of the random coefficient is one, it is called the stochastic unit root model. This paper proposes two Lagrange multiplier tests for the null hypotheses of random coefficient autoregressive and stochastic unit root models against a more general model. We apply our Lagrange multiplier tests to several stock index data, and find that the stochastic unit root model is rejected, whereas the random coefficient autoregressive model is not. This result indicates that it is important to check the validity of the stochastic unit root model prior to applying it to financial time series data, which may be better modeled by the random coefficient autoregressive model with the mean being not equal to one.