The Impact of Jumps in Volatility and Returns


Book Description

This paper examines a class of continuous-time models that incorporate jumps in returns and volatility, in addition to diffusive stochastic volatility. We develop a likelihood-based estimation strategy and provide estimates of model parameters, spot volatility, jump times and jump sizes using both Samp;P 500 and Nasdaq 100 index returns. Estimates of jumps times, jump sizes and volatility are particularly useful for disentangling the dynamic effects of these factors during periods of market stress, such as those in 1987, 1997 and 1998. Using both formal and informal diagnostics, we find strong evidence for jumps in volatility, even after accounting for jumps in returns. We use implied volatility curves computed from option prices to judge the economic differences between the models. Finally, we evaluate the impact of estimation risk on option prices and find that the uncertainty in estimating the parameters and the spot volatility has important, though very different, effects on option prices.







News Arrival, Jump Dynamics and Volatility Components for Individual Stock Returns


Book Description

This paper models different components of the return distribution which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. This mixture captures occasional large changes in price, due to the impact of news innovations such as earnings surprises, as well as smoother changes in prices which can result from liquidity trading or strategic trading as information disseminates. Unlike typical SV-jump models, previous realizations of both jump and normal innovations can feedback asymmetrically into expected volatility. This is a new source of asymmetry (in addition to good versus bad news) that improves forecasts of volatility particularly after large moves such as the '87 crash. A heterogeneous Poisson process governs the likelihood of jumps and is summarized by a time-varying conditional intensity parameter. The model is applied to returns from individual companies and three indices. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, contemporaneous and lagged leverage effects, the time-series dynamics of jump clustering, and the importance of modeling the dynamics of jumps around high volatility episodes.




The Relationship Between the Volatility of Returns and the Number of Jumps in Financial Markets


Book Description

We propose a methodology to employ high frequency financial data to obtain estimates of volatility of log-prices which are not affected by microstructure noise and Lévy jumps. We introduce the 'number of jumps' as a variable to explain and predict volatility and show that the number of jumps in SPY prices is an important variable to explain the daily volatility of the SPY log-returns, has more explanatory power than other variables (e.g. high and low, open and close), and has a similar explanatory power to that of the VIX. Finally, number of jumps is very useful to forecast volatility and contains information that is not impounded in the VIX.













Financial Market Volatility and Jumps


Book Description

JEL classification. C1, C2, C5, C51, C52, F3, F4, G1, G14.







Asset Pricing, Investment, and Trading Strategies


Book Description

Asset pricing, investment, and trading strategies are very important in finance. They are useful in various situations, for example, supporting the decision-making process of choosing investments; determining the asset-specific required rate of return on the investment; pricing derivatives for trading or hedging; getting portfolios from fixed incomes or bonds, stocks, and other assets; evaluating diverse portfolios; determining macroeconomic variables affecting market prices; calculating option prices; and incorporating features such as mean reversion and volatility, etc. They can also be applied in financial forecast for assets, portfolios, business projects.Understanding, modeling, and using various asset pricing models, investment models, and models for different trading strategies is paramount in many different areas of finance and investment, including banking, stocks, bonds, currencies, and related financial derivatives. Different asset pricing models, investment models, and models for different trading strategies also allow us to compare the performances of different variables through the analysis of empirical real-world data.This Special Issue on "Asset Pricing, Investment, and Trading Strategies" will be devoted to advancements in the theoretical development of various asset pricing models, investment models, and models for different trading strategies as well as to their applications.The Special Issue will encompass innovative theoretical developments, challenging and exciting practical applications, and interesting case studies in the development and analysis of various asset pricing models, investment models, and models for different trading strategies in finance and cognate disciplines.