Given the growing need for managing financial risk, risk prediction plays an increasing role in banking and finance. In this study we compare out-of-sample performance of existing methods some new models predicting value-at-risk (VaR) a univariate context. Using more than 30 years daily return data on NASDAQ Composite Index, find that most approaches perform inadequately, although several are acceptable under current regulatory assessment rules model adequacy. A hybrid method, combining...

The use of Markov-switching models to capture the volatility dynamics financial time series has grown considerably during past years, in part because they give rise a plausible interpretation nonlinearities. Nevertheless, GARCH-type remain ubiquitous order allow for nonlinearities associated with time-varying volatility. Existing methods combining two approaches are unsatisfactory, as either suffer from severe estimation difficulties or else their dynamic properties not well understood. In...

Abstract In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. The construction "observable" or series from intra-day transaction use standard time-series techniques lead to promising strategies for predicting (daily) volatility. this article, we show that residuals commonly used models logarithmic variance exhibit non-Gaussianity clustering. We propose extensions explicitly account...

In the 1960's Benoit Mandelbrot and Eugene Fama argued strongly in favor of stable Paretian distribution as a model for unconditional asset returns. Although substantial body subsequent empirical studies supported this position, plays minor role current work. While economics finance literature distributions are virtually exclusively associated with distributions, paper we adopt more fundamental view extend concept stability to variety probabilistic schemes. These schemes give rise...

We first demonstrate the simultaneous need for both more general GARCH structures and non-normal innovation distributions modelling returns on certain return series such as highly volatile exchange rates East Asian currencies against US dollar. This is accomplished not only via in-sample goodness-of-fit criteria, but also in terms of precision Value-at-Risk calculations made out-of-sample density predictions. Second, a forecasting strategy using weighted maximum likelihood estimation...

The paper illustrates and evaluates a Kalman filtering method for forecasting German real GDP at monthly intervals. is produced quarterly intervals but analysts decision makers often want forecasts. Quarterly could be regressed on indicators, which would pick up feedbacks from the indicators to GDP, not implicit onto itself or indicators. An efficient model aims incorporate all significant correlations in monthly-quarterly data should include feedbacks. We do this with estimated VAR(2)...

Formulas are derived for computing asymptotic covariance matrices of sets impulse responses, step or variance decompositions estimated dynamic simultane- ous-equations models in vector autoregressive moving-average (VARMA) form. Com- puted covariances would be used to test linear restrictions on decompositions. The results unify and extend previous formulas handle any model VARMA form, provide accurate computations based analytic derivatives, insights into the structures covariances....

Environmental, social and governance (ESG) ratings (scores) provide quantitative measures for socially responsible investment. We consider ESG scores to be a third independent variable—on par with financial risk return—and incorporate such numeric into dynamic asset pricing. Based on this incorporation, we develop the entire investment process market: portfolio optimization efficient frontier, capital market line (the portfolio), risk-assessment hedging instruments (options). There is...