Portfolio risk estimation in volatile markets requires employing fat-tailed models for financial returns combined with … copula functions to capture asymmetries in dependence and an appropriate downside risk measure. In this survey, we discuss … how these three essential components can be combined together in a Monte Carlo based framework for risk estimation and …

middle-term asset volatility is used for determination of the size of opening position when buy signal is obtained from trend … following model. The strategy is named as timing-and-volatility strategy.Two ways of implementation of the timing-and-volatility … strategy are proposed: without use of leverage and with use of leverage. Testing of the non-leveraged timing-and-volatility …

We introduce the beta stochastic volatility model and discuss empirical features of this model and its calibration … steeper forward skews, compared to traditional stochastic volatility models …

Estimation of volatility is important for many financial applications. The most common methods are based on a time … volatility and is a natural fit for high frequency traders who continuously monitor price series …

Epstein-Zin preferences to study the volatility implications of a monetary policy shock. An unexpected increases in the policy … rate by 150 basis points causes output and inflation volatility to rise around 10% above their steady-state standard … deviations. VAR based empirical results support the model implications that contractionary shocks increase volatility. The …

to underestimate risk measures such as volatility (i.e. standard deviation). In order to encompass for such serial … random walk model with time varying parameters is largely used in the risk industry for Value-at-Risk4 purposes. Its main … and incorporate a predictive model for volatility. However, their mathematical background lies on a diametrically …

Volatility is widely considered to be a category of technical indicators with a simple interpretation - no matter how … it is measured volatility is widely believed to rise in a market downturn. This approach is applied to indicators such as … the Average True Range (ATR), Bollinger Bands® BandWidth or the most widely followed volatility indicator, VIX, which is …

This paper revisits the performance of frequently used risk forecasting methods, such as the Value-at-Risk models. The … distributions in order to create a model with the best forecasting ability on the MSCI index. Substituting the obtained volatility … finds that incorporating volatility estimates as generated by AVGARCH(2,2) with a JSU distribution yields out-of-sample VaR …

in some specific domains.We discuss some of the recent discoveries in the mathematical theory of machine learning that … reduce the gap between theory and practice. We conduct experiments in the financial time series domain using deep neural … financial time series domain. This is consistent with the finance practitioner's theory that backtesting ( training data …