Forecasting the S&P 500 index volatility

This study introduces a new pre-differencing transformation for the AR1MA model for forecasting S&P 500 index volatility. The out of sample forecasting performance of the ARIMA model using the new pre-differencing transformation is compared with the out of sample forecasting performance of the mean reversion model and the GARCH model. The ARIMA model using the new pre-differencing transformation introduced in this study is found to be superior to both the mean reversion model and the GARCH model in forecasting monthly S&P 500 index volatility for the forecast comparison periods used in this study.     

Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility

We show that historical volatility from high frequency returns outperforms implied volatility when standardized returns by historical volatility tends to be normally distributed. For the FTSE 100 futures, we find that historical volatility using high frequency returns outperforms implied volatility in forecasting future volatility. However, we find that implied volatility outperforms historical volatility in forecasting future volatility for the S&P 500 futures. The results also indicate that historical volatility using high frequency returns could be an unbiased forecast for the FTSE 100 futures.     

Risk aversion,fanning preference and volatility smirk on S&P 500 index options

This article proposes a novel way of pricing S&P 500 index options in the presence of jump risk. Our analysis is built upon an equilibrium option pricing rule for a representative agent economy. In particular, we use the weighted utility’s certainty equivalent to specify agent’s risk preference, which displays a fanning-out characteristic. We find that the fanning effect captures a remarkably large portion of the total market risk premium implicit in options. As a result, the model with fanning effect generates pronounced volatility smirks.     

S&P 500 Affiliation and Stock Price Informativeness

Abstract

When firms are added to a stock index, more information should be discovered, traded on, and incorporated into their stock prices, making them more informative. We test this hypothesis using a large sample of additions to the S&P 500 index. Using two alternative statistical tests, we find that the stocks added experience more random, less predictable return and, thus, appear to be priced more efficiently information-wise. We further find concurrent increases in institutional ownership and investor awareness, which tend to contribute to the higher pricing efficiency, adding to the literature. These findings should be of interest to academics and practitioners.     

QARMA-Beta-t-EGARCH versus ARMA-GARCH: an application to S&P 500

Statistical performance and out-of-sample forecast precision of ARMA-GARCH and QARMA-Beta-t-EGARCH are compared. We study daily returns on the Standard and Poor’s 500 (S&P 500) index and a random sample of 50 stocks from the S&P 500 for period May 2006 to July 2010. Competing models are estimated for periods before and during the US financial crisis of 2008. Out-of-sample point and density forecasts are performed for periods during and after the US financial crisis. The results provide evidence of the superior in-sample statistical and out-of-sample predictive performance of QARMA-Beta-t-EGARCH.     

Asset pricing with time-varying betas for stocks traded on S&P 500

This study uses a novel approach for capturing time variation in betas whose pattern is treated as a function of market returns. A two-factor model (TFM) is constructed using estimated coefficients of a nonlinear regression. The model is tested against the CAPM and the Fama and French three-factor model in the context of time series regressions. The used stocks are traded on S&P 500. The period spans from 1993 to 2011. The time series regression results depict the superiority of the TFM in explaining portfolio returns including momentum ones. We also provide evidence that the particular portfolios employed at the construction of the new model accommodate different fundamental characteristics and different risk levels.     

The risk-return relation and VIX: evidence from the S&P 500

A significantly positive risk-return relation for the S&P 500 market index is detected if the squared implied volatility index (VIX) is allowed for as an exogenous variable in the conditional variance equation of the parsimonious GARCH(1,1) model. This result holds for both daily and weekly observations, for extended conditional mean and variance specifications, and is robust to sub-samples. We show that the conditional variance obtained from the GARCH model with VIX has better predictive ability for realized volatility than the conditional variance from GARCH without VIX and VIX itself, thereby documenting an important information content of VIX for conditional variance. The results are interpreted as evidence that adding VIX squared in the conditional variance equation yields a better measure of conditional variance which, subsequently, uncovers a strong risk-return relation.     

Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.     

Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.     

Nonlinear dynamics in arbitrage of the S&P 500 index and futures: A threshold error-correction model

Using a three-regime threshold error-correction model, we investigate the nonlinear dynamics of the S&P 500 index and futures. First, using the SupLM statistic, we report estimates of two thresholds for the three-regime model to explain the nonlinear dynamics in arbitrage of the S&P 500 index and futures. This provides empirical evidence of the no-arbitrage band predicted by the cost-of-carry model. Second, using quasi-maximum likelihood estimation, we demonstrate that those indexes that are located outside the no-arbitrage band are a nonlinear stationary process of mean-reversion to the no-arbitrage band. However, index and futures that are located within the no-arbitrage band are non-stationary. Third, we confirm an earlier finding that futures price leads the nonlinear mean-reverting behavior of the index but not vice versa. Impulse response function analysis and forecasting performance of three-regime error-correction model reinforce our findings and our estimation results are robust with different specifications of pricing error terms and endogenous variables.     

The impact of large-scale asset purchases on the S&P 500 index,long-term interest rates and unemployment

After the bankruptcy of Lehman Brothers in September 2008 and the financial panic that ensued, the Federal Reserve moved rapidly to reduce the federal funds rate to .25%. It was quickly judged that additional measures were needed to stabilize the US economy. Beginning in December 2008, the Federal Reserve Bank initiated three rounds of unconventional monetary policies known as quantitative easing (QE). These policies were intended to reduce long-term interest rates when the short-term federal funds rates had reached the zero lower bound and could not become negative. It was argued that the lowering of longer-term interest rates would help the stock market and thus the wealth of consumers. This article carefully investigates three hypotheses: QE impacting long-term interest rates, QE impacting the stock market and QE impacting unemployment using a Markov regime switching methodology. We conclude that QE has contributed significantly to increases in the stock market but less significantly to long-term interest rate and unemployment.     

Crossing of Psychological Price Levels: The Price Dynamics and Interaction between S&P500 Index and Index Futures

The authors provide fresh evidence on the nonfundamental-driven price dynamics and interaction between index and index futures by examining the price movements of the S&P500 index and index futures surrounding the crossing of the 00 psychological barriers and 52-week highs and lows. In contrast to the extant evidence that futures leads in fundamental-driven price movements, the authors show the dominance of the crossing in the index in continuing the price trend after the crossing. Even when synchronized crossings occur, the index rises more than the index futures during upward crossings, whereas the index futures falls more than the index during downward crossings. While volatility is significantly reduced before upward crossings, but not for downward crossings, it is significantly higher during the crossing, and significantly lower after the crossings in both markets. These findings have clear practical implications for index arbitrageurs, investors, and regulators.     

THE LEAD–LAG RELATION BETWEEN THE S&P500 SPOT AND FUTURES MARKETS: AN INTRADAY-DATA ANALYSIS USING A THRESHOLD REGRESSION MODEL

In this paper we examine the lead–lag interaction between the futures and spot markets of the S&P500 using the threshold regression model on intraday data. The use of threshold variables to model the changes in the regression structure with respect to different market conditions enables us to investigate the lead–lag interaction in a data-based approach and avoid stratifying the data arbitrarily. Using the basis as the threshold variable, we find that the short-selling restrictions in the spot market reduce the effect of the spot index as the leading variable. To study the effect of market-wide information on the interaction between the spot and futures markets, we use the coefficient of determination in the regression of the S&P500 on the Morgan–Stanley Composite Index-US and the Major Market Index as the threshold variable. We find that the lead effect of the futures market over the spot market is stronger when there is more market-wide information. On the other hand, the lead effect of the cash market over the futures market is weaker when there is more market-wide information. In addition, we also use the lagged 45-min return of the spot market as the threshold variable. We find that the lead effect of the spot market is stronger in periods of directionless trading than in periods of good or bad markets.     

Forecasting the volatility of crude oil futures using high-frequency data: further evidence

We forecast the realized volatility of crude oil futures market using the heterogeneous autoregressive model for realized volatility and its various extensions. Out-of-sample findings indicate that the inclusion of jumps does not improve the forecasting accuracy of the volatility models, whereas the “leverage effect” pertaining to the difference between positive and negative realized semi-variances can significantly improve the forecasting accuracy in predicting the short- and medium-term volatility. However, the signed jump variations and its decomposition couldn’t significantly enhance the models’ forecasting accuracy on the long-term volatility.     

A note on the estimated GARCH coefficients from the S&P1500 universe

ABSTRACT

We employ 1440 stocks listed in the S&P Composite 1500 Index of the NYSE. Three benchmark GARCH models are estimated for the returns of each individual stock under three alternative distributions (Normal, t and GED). We provide summary statistics for all the GARCH coefficients derived from 11,520 regressions. The EGARCH model with GED errors emerges as the preferred choice for the individual stocks in the S&P 1500 universe when non-negativity and stationarity constraints in the conditional variance are imposed. 57% of the constraint’s violations are taking place in the S&P small cap stocks.     

Non-linear dependence modelling with bivariate copulas: statistical arbitrage pairs trading on the S&P 100

We develop a copula-based pairs trading framework and apply it to the S&P 100 index constituents from 1990 to 2014. We propose an integrated approach, relying on copulas for pairs selection and trading. Essentially, we fit t-copulas to all possible combinations of pairs in a formation period. Next, we trade these pairs in-sample to assess the profitability of mispricing signals derived from t-copulas. The top pairs are transferred to an out-of-sample trading period, and traded with individualized exit thresholds. In particular, we differentiate between pairs exhibiting mean-reversion and momentum effects and apply idiosyncratic take-profit and stop-loss rules. For the top 5 mean-reversion pairs, we find out-of-sample returns of 7.98% per year; the top 5 momentum pairs yield 7.22% per year. Standard deviations are low, leading to annualized Sharpe ratios of 1.52 (top 5 mean-reversion) and 1.33 (top 5 momentum), respectively.     

Portfolio optimization using dynamic factor and stochastic volatility: evidence on Fat‐tailed errors and leverage

The portfolio optimization problem is investigated using a multivariate stochastic volatility model with factor dynamics, fat‐tailed errors and leverage effects. The efficient Markov chain Monte Carlo method is used to estimate model parameters, and the Rao–Blackwellized auxiliary particle filter is used to compute the likelihood and to predict conditional means and covariances. The proposed models are applied to sector indices of the Tokyo Stock Price Index (TOPIX), which consists of 33 stock market indices classified by industrial sectors. The portfolio is dynamically optimized under several expected utilities and two additional static strategies are considered as benchmarks. An extensive empirical study indicates that our proposed dynamic factor model with leverage or fat‐tailed errors significantly improves the predictions of the conditional mean and covariances, as well as various measures of portfolio performance.     

Technological forecasting at the Korean stock market: A dynamic competition analysis using Lotka–Volterra model

The purpose of this paper is to attempt to analyze the dynamic relationship between the Korean Stock Exchange (KSE) and Korean Securities Dealers Automated Quotation (KOSDAQ), two competing markets at the Korean stock market, in the viewpoint of technological forecasting of competition. The Lotka–Volterra system of equations, one well-known competitive diffusion models, is adopted to represent the competitive situations of the Korean stock market and it is estimated using daily empirical index data of KSE and KOSDAQ during 1997–2001. The results show that there existed a predator–prey relationships between two markets in which KSE acted as a prey for the time being after the emergence of KOSDAQ. This interaction was altered to symbiotic relationship and finally to pure competition relationship. We also perform an equilibrium analysis of the estimated Lotka–Volterra equations. As a result, we find that there is an equilibrium point in a dynamic sense. However, the equilibrium point could be unstable in the latest pure competition relationship.     

Determinants and consequences of soft budget constraintsAn empirical analysis using enterprise‐level data in transition countries1

This paper presents empirical work grounded in the soft budget constraint (SBC) literature. A loan is soft when a bank cannot commit the enterprise to hold to a fixed initial budget and/or the timing of repayment. Using data collected by the European Bank for Reconstruction and Development (EBRD) (Business Environment and Enterprise Performance Survey (BEEPS), 2002) in 26 transition economies, we analyze the determinants of managers’ expectations of having a soft loan. In particular, we find that managers’ expectations are lower when the initial financing requires collateral, and higher for larger firms and when firms had recently experienced financial distress. We also provide evidence that managers’ expectations influence their price responsiveness.