دانلود و نمایش مقالات مرتبط با Realized volatility::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - Realized volatility

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 Forecasting volatility with time-varying leverage and volatility of volatility effects
پیش بینی نوسانات با تغییر زمان و اهرم و نوسانات اثرات نوسانات-2020
Predicting volatility is of primary importance for business applications in risk management, asset allocation, and the pricing of derivative instruments. This paper proposes a measurement model that considers the possibly time-varying interaction of realized volatility and asset returns according to a bivariate model to capture its major characteristics: (i) the long-term memory of the volatility process, (ii) the heavy tailedness of the distribution of returns, and (iii) the negative dependence of volatility and daily market returns. We assess the relevance of the effects of ‘‘the volatility of volatility’’ and time-varying ‘‘leverage’’ to the out-of-sample forecasting performance of the model, and evaluate the density of forecasts of market volatility. Empirical results show that our specification can outperform the benchmark HAR–GARCH model in terms of both point and density forecasts.© 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords: Realized volatility | Leverage effect | Volatility of volatility | Score driven models | Volatility prediction
مقاله انگلیسی
2 An AI Model for Oil Volatility Forecasting
یک مدل هوش مصنوعی برای پیش بینی نوسانات نفت-2020
Abstract—By introducing a genetic algorithm learning with a classifier system, we construct an AI model for oil volatility forecasting on the basis of Internal Information and External Information. The model provides decision support for mark-to-market portfolio and risk management by forecasting whether 1-day-ahead volatility is above a given threshold. Moreover, we explore the dynamic influencing mechanism of different types of information through information usage frequency in the learning process. In particular, we find that the jump component of oil realized volatility is efficient only in bull market, and currency information contributes most rather than oil information in bear market. Therefore, this article provides an AI method to forecast oil volatility as well as to improve the information structure of forecasting models.
مقاله انگلیسی
3 Evaluating VPIN as a trigger for single-stock circuit breakers
بررسی VIPN به عنوان یک سکوی پرتاب برای مدارشکن های تک - سهامی-2018
We study if VPIN (Easley et al., 2012a) is an efficient advance indicator of toxicity-induced liquidity crises and related sharp price movements. We find that high VPIN readings rarely signal abnormal illiquidity, and very occasionally anticipate large intraday price changes leading to actual trading halts. We find significant differences in illiquidity and price impact between VPIN-identified toxic and non-toxic halts, but they tend to vanish when we control for ex ante realized volatility. We conclude that the capacity of VPIN to anticipate truly toxic events is limited.
keywords: VPIN| BVC| Circuit breakers| Trading halts| Price limits| Order flow toxicity
مقاله انگلیسی
4 Asset pricing with beliefs-dependent risk aversion and learning
قیمت گذاری دارایی با بیزاری و یادگیری خطر مستقل از باروها-2018
This paper studies equilibrium in a pure exchange economy with unobservable Markov switching growth regimes and beliefs-dependent risk aversion (BDRA). Risk aversion is stochastic and depends nonlinearly on consumption and beliefs. Equilibrium is obtained in closed form. The market price of risk, the interest rate, and the stock return volatility acquire new components tied to fluctuations in beliefs. A three-regime specification is estimated using the generalized method of moments (GMM). Model moments match their empirical counterparts for a variety of unconditional moments, including the equity premium, stock returns volatility, and the correlations between stock returns and consumption and dividends. Dynamic features of the data, such as the countercyclical behaviors of the equity premium and volatility, are also captured. Model volatility provides a good fit for realized volatility. A new factor, the information risk premium, is found to be a strong predictor of future excess returns. These results are obtained with an estimated risk aversion fluctuating between 1.44 and 1.93.
keywords: Asset pricing puzzles |Beliefs-dependent risk aversion |Equity premium |Risk-free rate |Volatility
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 8590 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 8590 :::::::: افراد آنلاین: 80