{"id":62,"date":"2014-12-03T16:18:53","date_gmt":"2014-12-03T16:18:53","guid":{"rendered":"http:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/?page_id=62"},"modified":"2025-09-01T15:03:16","modified_gmt":"2025-09-01T15:03:16","slug":"research","status":"publish","type":"page","link":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

Working Papers<\/strong><\/p>\n

Using Daily Stock Returns to Estimate the Unconditional and Conditional Variances of Lower-Frequency Stock Returns<\/a><\/p>\n

Using daily returns to construct realized measures of the variances of lower-frequency returns is a natural alternative to using intraday returns for this purpose when transaction-level price data are unavailable. Notably, a suitable application of this approach yields realized measures that are unbiased estimators of the unconditional and conditional variances of holding-period returns for any investment horizon. I use a long sample of daily S&P 500 index returns to investigate the merits of constructing realized measures in this fashion. First, I conduct a Monte Carlo study using a data generating process that reproduces the key dynamic properties of index returns. The results of the study suggest that using realized measures constructed from daily returns to estimate the conditional and unconditional variances of lower-frequency returns should lead to substantial increases in efficiency. Next, I fit a multiplicative error model to the realized measures for weekly and monthly index returns to obtain out-of-sample forecasts of their conditional variances. Using the forecasts produced by a generalized autoregressive conditional heteroscedasticity model as a benchmark, I find that the forecasts produced by the multiplicative error model always generate the smallest losses. Furthermore, the performance advantage of forecasts that are based on realized measures is statistically significant in most cases.<\/p>\n

Regime-Switching PCA and Industry Costs of Equity<\/a><\/p>\n

I use a regime-switching generalization of principal components analysis (PCA) to estimate costs of equity for a set of 48 industry portfolios. In many cases, the cost-of-equity estimates display substantial variation across the unobserved regimes. Furthermore, the estimated pricing errors produced by the PCA factors are smaller in magnitude than those for factor pricing models that feature prominently in the asset-pricing literature. In regression-based tests, for example, the factor-mimicking portfolios for a six-factor PCA specification produce a mean absolute estimated intercept (alpha) that is 45% smaller than the mean absolute estimated intercept for the Fama and French (2015) five-factor model.<\/p>\n

Firm Characteristics and the Cross-Section of Covariance Risk<\/a><\/p>\n

I analyze the cross-section of covariance risk for individual stocks using a new type of multivariate volatility model in which firm characteristics serve as time-varying loadings on fundamental factors. The evidence points to strong linkages between firm characteristics and covariance risk, and also reveals that cross-sectional differences in covariance risk explain much of the cross-sectional variation in expected excess stock returns. I find, for example, that the fundamental factors perform at least as well as the Fama-French factors in regression-based pricing tests. In view of its tractability and performance, the proposed model should find use in a variety of applications.<\/p>\n

Optimizing the performance of sample mean-variance efficient portfolios<\/a>, with B. Ostdiek<\/p>\n

We propose a comprehensive empirical strategy for optimizing the out-of-sample performance of sample mean-variance efficient portfolios. After constructing a sample objective function that accounts for the impact of estimation risk, specification errors, and transaction costs on portfolio performance, we maximize the function with respect to a set of tuning parameters to obtain plug-in estimates of the optimal portfolio weights. The methodology offers considerable flexibility in specifying objectives, constraints, and modeling techniques. Moreover, the resulting portfolios have well-behaved weights, reasonable turnover, and substantially higher Sharpe ratios and certainty-equivalent returns than benchmarks such as the 1\/N portfolio and S&P 500 index<\/p>\n

Recent Publications<\/strong><\/p>\n

Volatility shocks, leverage effects, and time-varying conditional skewness<\/a>, Journal of Financial Econometrics<\/em> 22, 2024<\/p>\n

A closer look at the regime-switching evidence of bull and bear markets<\/a>, Finance Research Letters<\/em> 52, 2023<\/p>\n

Short-term reversals, short-term momentum, and news-driven trading activity<\/a>, with Ethan Chiang and Ziye Nie, Journal of Banking and Finance<\/em> 125, 2021<\/p>\n

Firm Characteristics, Cross-Sectional Regression Estimates, and Asset Pricing Tests<\/a>, Review of Asset Pricing Studies\u00a0<\/em>10, June 2020<\/p>\n

The Value Premium and Expected Business Conditions<\/a>, Finance Research Letters<\/em> 30, 2019<\/p>\n

Estimating the Cost of Equity Capital Using Empirical Asset Pricing Models<\/a>, International Review of Finance<\/em> 19, 2019<\/p>\n

Most Frequently Cited Articles<\/strong><\/p>\n

It’s all in the timing: Simple active portfolio strategies that outperform naive diversification<\/a>, with B. Ostdiek, Journal of Financial and Quantitative Analysis<\/em> 47, April 2012<\/p>\n

The economic value of volatility timing using \u2018realized\u2019 volatility<\/a>, with J. Fleming and B. Ostdiek, Journal of Financial Economics<\/em> 67, March 2003<\/p>\n

The economic value of volatility timing<\/a>, with J. Fleming and B. Ostdiek, Journal of Finance<\/em> 56, February 2001<\/p>\n

Information and volatility linkages in the stock, bond, and money markets<\/a>, with J. Fleming and B. Ostdiek, Journal of Financial Economics<\/em> 49, July 1998<\/p>\n","protected":false},"excerpt":{"rendered":"

Working Papers Using Daily Stock Returns to Estimate the Unconditional and Conditional Variances of Lower-Frequency Stock Returns Using daily returns to construct realized measures of the variances of lower-frequency returns is a natural alternative to using intraday returns for this purpose when transaction-level price data are unavailable. Notably, a suitable application of this approach yields […]<\/p>\n","protected":false},"author":824,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-62","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/wp-json\/wp\/v2\/pages\/62","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/wp-json\/wp\/v2\/users\/824"}],"replies":[{"embeddable":true,"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/wp-json\/wp\/v2\/comments?post=62"}],"version-history":[{"count":34,"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/wp-json\/wp\/v2\/pages\/62\/revisions"}],"predecessor-version":[{"id":163,"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/wp-json\/wp\/v2\/pages\/62\/revisions\/163"}],"wp:attachment":[{"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ckirby10\/wp-json\/wp\/v2\/media?parent=62"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}