Researcher:
Bali, Turan

Profile Picture
ORCID

Job Title

Other

First Name

Turan

Last Name

Bali

Name

Name Variants

Bali, Turan

Email Address

Birth Date

Filters

2007 - 20098

Advanced Search

Filter by

Settings

Researcher Of Publications: search.filters.isAuthorOfPublication.302da782-d52c-497a-8768-2fce8a1c15a4

Search Results

Now showing 1 - 8 of 8
  • Placeholder
    PublicationMetadata only
    The conditional beta and the cross-section of expected returns
    (Wiley, 2009) Cakici, Nusret; Tang, Yi; Department of Economics; Bali, Turan; Other; Department of Economics; College of Administrative Sciences and Economics; N/A
    We examine the cross-sectional relation between conditional betas and expected stock returns for a sample period of July 1963 to December 2004. Our portfolio-level analyses and the firm-level cross-sectional regressions indicate a positive, significant relation between conditional betas and the cross-section of expected returns. The average return difference between high- and low-beta portfolios ranges between 0.89% and 1.01% per month, depending on the time-varying specification of conditional beta. After controlling for size, book-to-market, liquidity, and momentum, the positive relation between market beta and expected returns remains economically and statistically significant.
  • Placeholder
    PublicationMetadata only
    Nonlinear mean reversion in stock prices
    (Elsevier, 2008) Demirtaş, K. Özgür; Levy, Haim; Department of Economics; Bali, Turan; Other; Department of Economics; College of Administrative Sciences and Economics; N/A
    This paper provides new evidence on the time-series predictability of stock market returns by introducing a test of nonlinear mean reversion. The performance of extreme daily returns is evaluated in terms of their power to predict short- and long-horizon returns on various stock market indices and size portfolios. The paper shows that the speed of mean reversion is significantly higher during the large falls of the market. The parameter estimates indicate a negative and significant relation between the monthly portfolio returns and the extreme daily returns observed over the past one to eight months. Specifically, in a quarter in which the minimum daily return is -2% the expected excess return is 37 basis points higher than in a month in which the minimum return is only -1%. This result holds for the value-weighted and equal-weighted stock market indices and for each of the size decile portfolios. The findings are also robust to different sample periods, different indices, and investment horizons.
  • Placeholder
    PublicationMetadata only
    A model-independent measure of aggregate idiosyncratic risk
    (Elsevier, 2008) Cakici, Nusret; Levy, Haim; Department of Economics; Bali, Turan; Other; Department of Economics; College of Administrative Sciences and Economics; N/A
    This paper introduces a model-independent measure of aggregate idiosyncratic risk, which does not require estimation of market betas or correlations and is based on the concept of gain from portfolio diversification. The statistical results and graphical analyses provide strong evidence that there are significant level and trend differences between the average idiosyncratic volatility measures of Campbell et al. [Campbell, J.Y., Lettau, M., Malkiel, B.G., and Xu, Y., 2001, Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk, journal of Finance 56, 1-43.] and the new methodology. Although both approaches indicate a noticeable increase in the firm-level idiosyncratic risk, the volatility measure of CLMX is greater and has a stronger upward trend than the new idiosyncratic volatility measure. For both measures of idiosyncratic risk, the upward trend is found to be stronger for smaller. lower-priced, and younger firms. The analytical and empirical results show that the significant upward trend in the differences of the two idiosyncratic volatility measures is related to the increase in the cross-sectional dispersion of the volatility of individual stocks.
  • Placeholder
    PublicationMetadata only
    Risk measurement performance of alternative distribution functions
    (Wiley, 2008) Theodossiou, Panayiotis; Department of Economics; Bali, Turan; Other; Department of Economics; College of Administrative Sciences and Economics; N/A
    This paper evaluates the performance of three extreme value distributions, i.e., generalized Pareto distribution (GPD), generalized extreme value distribution (GEV), and Box-Cox-GEV, and four skewed fat-tailed distributions, i.e., skewed generalized error distribution (SGED), skewed generalized t (SGT), exponential generalized beta of the second kind (EGB2), and inverse hyperbolic sign (IHS) in estimating conditional and unconditional value at risk (VaR) thresholds. The results provide strong evidence that the SGT, EGB2, and IHS distributions perform as well as the more specialized extreme value distributions in modeling the tail behavior of portfolio returns. All three distributions produce similar VaR thresholds and perform better than the SGED and the normal distribution in approximating the extreme tails of the return distribution. The conditional coverage and the out-of-sample performance tests show that the actual VaR thresholds are time varying to a degree not captured by unconditional VaR measures. In light of the fact that VaR type measures are employed in many different types of financial and insurance applications including the determination of capital requirements, capital reserves, the setting of insurance deductibles, the setting of reinsurance cedance levels, as well as the estimation of expected claims and expected losses, these results are important to financial managers, actuaries, and insurance practitioners.
  • Placeholder
    PublicationMetadata only
    Aggregate earnings, firm-level earnings, and expected stock returns
    (Cambridge Univ Press, 2008) Tehranian, Hassan; Demirtaş Özgür; Department of Economics; Bali, Turan; Other; Department of Economics; College of Administrative Sciences and Economics; N/A
    This paper provides an analysis of the predictability of stock returns using market-, industry-, and firm-level earnings. Contrary to Lamont (1998), we find that neither dividend payout ratio nor the level of aggregate earnings can forecast the excess market return. We show that these variables do not have robust predictive power across different stock portfolios and sample periods. In contrast to the aggregate-level findings, earnings yield has significant explanatory power for the time-series and cross-sectional variation in firm-level stock returns and the 48 industry portfolio returns. The mean reversion of stock prices as well as the earnings' correlation with expected stock returns are responsible for the forecasting power of earnings yield. These results are robust after controlling for book-to-market, size, price momentum, and post-earnings announcement drift. At the aggregate level, the information content of firm-level earnings about future cash flows is diversified away and higher aggregate earnings do not forecast higher returns.
  • Placeholder
    PublicationMetadata only
    Idiosyncratic volatility and the cross section of expected returns
    (Cambridge University Press (CUP), 2008) Cakici, Nusret; Department of Economics; Bali, Turan; Other; Department of Economics; College of Administrative Sciences and Economics; N/A
    This paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that i) the data frequency used to estimate idiosyncratic volatility, ii) the weighting scheme used to compute average portfolio returns, iii) the breakpoints utilized to sort stocks into quintile portfolios, and iv) using a screen for size, price, and liquidity play critical roles in determining the existence and significance of a relation between idiosyncratic risk and the cross section of expected returns. Portfolio-level analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse volatility-weighted), three breakpoints (CRSP, NYSE, equal market share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that no robustly significant relation exists between idiosyncratic volatility and expected returns.
  • Placeholder
    PublicationMetadata only
    The role of autoregressive conditional skewness and kurtosis in the estimation of conditional var
    (Elsevier, 2008) Mo, Hengyong; Tang, Yi; Department of Economics; Bali, Turan; Other; Department of Economics; College of Administrative Sciences and Economics; N/A
    This paper investigates the role of high-order moments in the estimation of conditional value at risk (VaR). We use the skewed generalized t distribution (SGT) with time-varying parameters to provide an accurate characterization of the tails of the standardized return distribution. We allow the high-order moments of the SGT density to depend on the past information set, and hence relax the conventional assumption in conditional VaR calculation that the distribution of standardized returns is iid. The maximum likelihood estimates show that the time-varying conditional volatility, skewness, tail-thickness, and peakedness parameters of the SGT density are statistically significant. The in-sample and out-of-sample performance results indicate that the conditional SGT-GARCH approach with autoregressive conditional skewness and kurtosis provides very accurate and robust estimates of the actual VaR thresholds.
  • Placeholder
    PublicationMetadata only
    An extreme value approach to estimating interest-rate volatility: pricing implications for interest-rate options
    (Informs, 2007) Department of Economics; Bali, Turan; Other; Department of Economics; College of Administrative Sciences and Economics; N/A
    This paper proposes an extreme value approach to estimating interest-rate volatility and shows that during the extreme movements of the U.S. Treasury market the volatility of interest-rate changes is underestimated by the standard approach that uses the thin-tailed normal distribution. The empirical results indicate that (1) the volatility of maximal and minimal changes in interest rates declines as time-to-maturity rises, yielding a downward-sloping volatility curve for the extremes; (2) the minimal changes are more volatile than the maximal changes for all data sets and for all asymptotic distributions used; (3) the minimal changes in Treasury yields have fatter tails than the maximal changes; and (4) for both the maxima and minima, the extreme changes in short-term rates have thicker tails than the extreme changes in long-term rates. This paper extends the standard option-pricing models with lognormal forward rates to accomrnodate significant kurtosis observed in the interest-rate data. This paper introduces a closed-form option-pricing model based on the generalized extreme value distribution that successfully removes the well-known pricing bias of the lognormal distribution.