scholarly journals Testando o poder preditivo do VIX: uma aplicação do modelo de erro multiplicativo

2015 ◽  
Vol 13 (4) ◽  
pp. 571
Author(s):  
Luis Fernando Pereira Azevedo ◽  
Pedro L. Valls Pereira
Keyword(s):  
Implied Volatility ◽  
Predictive Power ◽  
Explanatory Power ◽  
Relevant Information ◽  
Realized Volatility ◽  
Time Series Models ◽  
Economic Stability ◽  
Short Term ◽  
Volatility Index ◽  
Black Scholes

VIX - Volatility Index - emerged as an alternative calculation of implied volatility in order to mitigate some problems encountered in models of the Black-Scholes. This kind of volatility is seen as the best predictor of future volatility, given that option traders' expectations are embedded in their values. In this paper we test whether the VIX has more predictive power for future volatility and contains relevant information not found in time series models time for non-negative variables, treated by multiplicative error model. The results indicate that the VIX has greater predictive power in periods of economic stability, but does not contain relevant information to the realized volatility which here is considered as the "true volatility". In periods of economic crisis the result changes, with the VIX presenting the same explanatory power, but contains relevant information in the short term.

An Empirical Research on the Informational Efficiency of Model Free Implied Volatility

2008 ◽  
Vol 16 (2) ◽  
pp. 67-94
Author(s):  
Byung Kun Rhee ◽  
Sang Won Hwang
Keyword(s):  
Implied Volatility ◽  
Realized Volatility ◽  
Informational Efficiency ◽  
Option Prices ◽  
Index Options ◽  
Model Free ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Korean Market ◽  
Better Than

Black-Scholes Imolied volatility (8SIV) has a few drawbacks. One is that the model Is not much successful in fitting the option prices. and It Is n야 guaranteed the model is correct one. Second. the usual tradition in using the BSIV is that only at-the-money Options are used. It is well-known that IV's of In-the-money or Qut-of-the-money ootions are much different from those estimated from near-the-money options. In this regard, a new model is confronted with Korean market data. Brittenxmes and Neuberger (2000) derive a formula for volatility which is a function of option prices‘ Since the formula is derived without using any option pricing model. volatility estimated from the formula is called model-tree implied volatillty (MFIV). MFIV overcomes the two drawbacks of BSIV. Jiang and Tian (2005) show that. with the S&P index Options (SPX), MFIV is suoerlor to historical volatility (HV) or BSIV in forecasting the future volatllity. In KOSPI 200 index options, when the forecasting performances are compared, MFIV is better than any other estimated volatilities. The hypothesis that MFIV contains all informations for realized volatility and the other volatilities are redundant is oot rejected in any cases.

Who predicts dollar-rupee volatility better? A tale of two options markets

2019 ◽  
Vol 45 (9) ◽  
pp. 1292-1308
Author(s):  
Aparna Prasad Bhat
Keyword(s):  
Implied Volatility ◽  
Realized Volatility ◽  
Informational Efficiency ◽  
Significant Information ◽  
Content Type ◽  
Merton Model ◽  
Model Free ◽  
Ex Post ◽  
Black Scholes ◽  
Practical Implications

Purpose The purpose of this paper is to examine whether volatility implied from dollar-rupee options is an unbiased and efficient predictor of ex post volatility, and to determine which options market is a better predictor of future realized volatility and to ascertain whether the model-free measure of implied volatility outperforms the traditional measure derived from the Black–Scholes–Merton model. Design/methodology/approach The information content of exchange-traded implied volatility and that of quoted implied volatility for OTC options is compared with that of historical volatility and a GARCH(1, 1)-based volatility. Ordinary least squares regression is used to examine the unbiasedness and informational efficiency of implied volatility. Robustness of the results is tested by using two specifications of implied volatility and realized volatility and comparison across two markets. Findings Implied volatility from both OTC and exchange-traded options is found to contain significant information for predicting ex post volatility, but is neither unbiased nor informationally efficient. The implied volatility of at-the-money options derived using the Black–Scholes–Merton model is found to outperform the model-free implied volatility (MFIV) across both markets. MFIV from OTC options is found to be a better predictor of realized volatility than MFIV from exchange-traded options. Practical implications This study throws light on the predictive power of currency options in India and has strong practical implications for market practitioners. Efficient currency option markets can serve as effective vehicles both for hedging and speculation and can convey useful information to the regulators regarding the market participants’ expectations of future volatility. Originality/value This study is a comprehensive study of the informational efficiency of options on an emerging currency such as the Indian rupee. To the author’s knowledge, this is one of the first studies to compare the predictive ability of the exchange-traded and OTC markets and also to compare traditional model-dependent volatility with MFIV.

scholarly journals VOLATILITY MEASURES, LIQUIDITY AND CREDIT LOSS PROVISIONS DURING PERIODS OF FINANCIAL DISTRESS

2018 ◽  
Vol 06 (02) ◽  
pp. 1850006
Author(s):  
GIULIO ANSELMI
Keyword(s):  
Credit Risk ◽  
Financial Distress ◽  
Implied Volatility ◽  
Risk Measures ◽  
Balance Sheet ◽  
Realized Volatility ◽  
Loan Loss ◽  
Short Term ◽  
Futures Contract ◽  
Lending Activity

In this paper, we investigate the role of liquidity in banks lending activity and how liquidity provision is related to bank’s credit risk and others market-based risk measures, such as bank’s implied volatility skew from options traded on the market and realized volatility from futures contract on LIBOR, during periods of global financial distress. Credit risk is given by the ratio between loan loss reserves and total assets and we find that losses from lending activity force banks to build up new liquidity provisions only during the period of financial distress. Liquidity ratio is given by the sum of cash and short-term assets over total assets and we discovered that credit risk reduces liquidity ratio only in bad times, as this demand for liquid asset is suddenly switched on and the more reserves from loan losses the bank has, the more it cleans its balance sheet from long-term commitments in order to replenish its cash and short-term securities. When we control for market-based risk measures, we evidence that both implied volatility skew and LIBOR’s realized volatility are negatively related with the liquidity ratio and are useful in predicting a distress in bank’s liquidity holdings.

scholarly journals Can the Baidu Index predict realized volatility in the Chinese stock market?

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wei Zhang ◽  
Kai Yan ◽  
Dehua Shen
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Power ◽  
Realized Volatility ◽  
Trading Strategies ◽  
Time Series Models ◽  
Chinese Stock Market ◽  
Continuous Components ◽  
Long Term Impact

AbstractThis paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index. Furthermore, the predictability of the Baidu Index is found to rise as the forecasting horizon increases. We also find that continuous components enhance predictive power across all horizons, but that increases are only sustained in the short and medium terms, as the long-term impact on volatility is less persistent. Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

scholarly journals The Forecasting Power of the Volatility Index: Evidence from the Indian Stock Market

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Surya Bahadur G. C. ◽  
Ranjana Kothari
Keyword(s):  
Stock Market ◽  
Implied Volatility ◽  
Market Volatility ◽  
Series Data ◽  
Future Price ◽  
Stock Market Volatility ◽  
Short Term ◽  
Volatility Index ◽  
India Vix ◽  
Indian Stock Market

<div><p><em>Stock market volatility is a measure of risk in investment and it plays a key role in securities pricing and risk management. </em><em>The paper empirically analyzes the relationship between India VIX and volatility in Indian stock market. </em><em>India VIX is a measure of implied volatility which reflects markets’ expectation of future short-term stock market volatility.</em><em> It is a volatility index based on the index option prices of Nifty. </em><em>The study is based on time series data comprising of daily closing values of CNX Nifty 50 index comprising of 1656 observations from March 2009 to December 2015. </em><em>The results of the study </em><em>reveal that India VIX has predictive power for future short-term stock market volatility. It has higher forecasting ability for upward stock market movements as compared to downward movements. Therefore, it is more a bullish indicator. Moreover, the accuracy of forecasts provided by India VIX is higher for low magnitude future price changes relative to higher stock price movements. The current value of India VIX is found to be affected by past period volatility up to one month and it has forecasting ability for next one-month’s volatility which means the volatility in the Indian stock markets can be forecasted for up to 60 days period. </em></p></div>

scholarly journals Does currency smirk predict foreign exchange return?

2020 ◽  
Vol 17 (3) ◽  
pp. 219-230
Author(s):  
Ariful Hoque ◽  
Thi Ngoc Quynh Le ◽  
Kamrul Hassan
Keyword(s):  
Foreign Exchange ◽  
Implied Volatility ◽  
Predictive Power ◽  
Canadian Dollar ◽  
Short Term ◽  
Critical Information ◽  
Short Period ◽  
Negative News ◽  
Opening And Closing ◽  
Fx Market

This study examines the predictive power of implied volatility smirk to forecast foreign exchange (FX) return. The volatility smirk contains critical information, especially when the market experiences negative news. The Australian dollar, Canadian dollar, Swiss franc, Euro, and British pound options traded in the opening, midday and closing periods of the trading day are selected to estimate the currency smirk. Research results reveal that the currency smirk outperforms in forecasting FX returns. In addition, the steeper slope in the middle of the trading day suggests that the predictive power of currency smirk in the midday period is higher compared to the opening and closing periods. However, currency smirks’ predictability lasts for a short period, as the FX market is highly adept at incorporating the vital information embedded in the currency smirk. These findings imply that the currency smirk is distinctive for forecasting very short-term FX fluctuations, and the day- or overnight FX traders can use its uniqueness to profit from quick price swings in the 24-hour global FX market.

Examining Contemporaneous Relationship between Return of Nifty Index and India VIX

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
Prasenjit Chakrabarti
Keyword(s):  
Implied Volatility ◽  
Stock Index ◽  
Quadratic Effect ◽  
Asymmetric Building ◽  
Volatility Index ◽  
India Vix ◽  
The Relationship

The study examines the contemporaneous relationship between Nifty returns and India VIX returns. Literature documents that the relationship between them is negative and asymmetric. Building on this, the study considers the linear and quadratic effect of stock index return (CNX Nifty) and examines the changes in implied volatility index (India VIX). The study finds both linear and quadratic CNX Nifty index returns are significant for changes in the level of India VIX. Findings suggest that India VIX provides insurance both for downside market movement and size of the downside movement.

Nowcasting global trade in goods and services

2020 ◽  
pp. 1-19
Author(s):  
Fernando Cantú-Bazaldúa
Keyword(s):  
Real Time ◽  
Relevant Information ◽  
Global Trade ◽  
Dynamic Factor ◽  
Timely Manner ◽  
Short Term ◽  
Dynamic Factor Models ◽  
Significant Information ◽  
Goods And Services ◽  
World Economic

World economic aggregates are compiled infrequently and released after considerable lags. There are, however, many potentially relevant series released in a timely manner and at a higher frequency that could provide significant information about the evolution of global aggregates. The challenge is then to extract the relevant information from this multitude of indicators and combine it to track the real-time evolution of the target variables. We develop a methodology based on dynamic factor models adapted for variables with heterogeneous frequencies, ragged ends and missing data. We apply this methodology to nowcast global trade in goods in goods and services. In addition to monitoring these variables in real time, this method can also be used to obtain short-term forecasts based on the most up-to-date values of the underlying indicators.

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